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welcome. welcome to the consumer financial protectionbureau's field hearing in charleston, west virginia, at the university of charleston. at today's field hearing, you will hear fromdirector richard cordray and a panel of distinguished experts, who will discuss issues related todevelopments, risks, and benefits of using

Suntrust Online Business Banking Login, alternative sources of financial informationdeveloped through new technologies to weigh a consumer's creditworthiness. today, the bureau issued a request for informationabout this issue. with this rfi, the bureau is seeking commentsfrom the public on whether unconventional

sources of information, new ways to analyzethe data, and the use of new technologies could open up more access to credit for manyamericans who are currently outside the mainstream credit system. the consumer financial protection bureau,or the cfpb, is an independent federal agency whose mission is to help consumer financemarkets work by making rules more effective, by consistently and fairly enforcing thoserules, and by empowering consumers to take more control over their economic lives. as part of the bureau's mission to protectconsumers, to date, we have handled over 1 million complaints and actions resulting innearly $12 billion in relief to over 27 million

consumers. my name is zixta martinez. i am the associate director for the externalaffairs division at the cfpb. our audience today includes consumer advocates,industry representatives, state and local officials, and, of course, consumers. we're delighted that you're here. we're also grateful that the honorable patrickmorrisey, attorney general for west virginia, is here with us today and will provide remarks. we're also grateful that several members ofthe west virginia state legislature have joined

us at today's field hearing. let me spend just a few minutes telling youabout what you can expect. first, you will hear from the attorney general,patrick morrisey; then from cfpb's director, richard cordray, who will provide remarksabout alternative data and the bureau's rfi. following the director's remarks, david silberman,acting deputy director and the associate director for the bureau's research, markets, and regulationsdivision, will frame a discussion with a panel of experts. after the discussion, there will be an opportunityto hear from members of the public. today's field hearing is being livestreamedat consumerfinance.gov, and you can follow

cfpb on facebook and twitter. so let's get started. patrick morrisey was elected as the attorneygeneral for the state of west virginia on november 6, 2012, and reelected to a secondterm november 8, 2016. among the attorney general's many impressiveaccomplishments over two terms, he secured a $160 million internet settlement in decemberof 2015, which marked the largest independently negotiated consumer protection settlementin west virginia's history. he also strengthened the office's consumerprotection division, enabling it to vigorously enforce the state's laws and proactively educatecitizens about scams and ways to protect their

identities. attorney general morrisey, you have the floor. well, thank you very much. and i'm grateful that everyone has come heretoday. and i would like to do a special shout-outto director cordray for coming in from washington, d.c. i think the topic that you're going to hearabout today is incredibly important because all of us in the state of west virginia carevery passionately about enhancing consumers' credit opportunities, and so i look forwardto learning a little bit more about some of

the alternative data and the innovative newways that we can expand opportunities for west virginians. so a hearty welcome to you, director, forcoming. we appreciate it. now, as previously mentioned, my name is patrickmorrisey. i'm the attorney general of the great stateof west virginia. and it's pleasure to welcome a number of youto our great state. i trust director cordray because he's a neighborfrom ohio, is familiar with our state's natural beauty.

we have an incredible place, one of the mostbeautiful states in the nation. i ask everyone who is coming in to come back,ski our mountainsides, come to the summer, go to the new river gorge, go down to greenbriercounty. you're not going to find a better place tocome back and visit. i'm also grateful for director cordray's commitmentto public service, and a lot of the work he did in ohio, in particular, some of the $2billion he secured for ohio retirees. so i know he's been very aggressive protectingconsumers, and i have a lot of respect for that. i also know that there are times that we mighthave different views on the roles of government

and the role of the cfpb. it's probably no secret that west virginiawas part of a collection of states that filed suit over the cfpb because we had a numberof significant legal questions, questions pertaining to how the agency was funded andthe constitutionality of it, but despite those differences, i can say that we share a commoninterest in protecting consumers, and that has to be all of our priority. i can say that our offices have worked togetheron a number of fronts. together we have monitored the conduct ofbanks in the wake of the national mortgage settlement from 2012.

that settlement sought to remedy misconduct,predatory lending, servicing issues, and compliance failures, and we believe that that partnershiphas resulted in west virginia consumers receiving at least $3 million in payment and mortgagemodifications since 2014. beyond that, our offices have actually workedtogether to successfully sue sun trust bank, hsbc, and morgan drexen, a company that providedoutsourced administrative support services to attorneys and debt settlement practices. the sun trust settlement, well, that providedmore than $260,000 to west virginia borrowers whose loans were serviced by the bank. people lost their homes to the terrible foreclosuresthat occurred in 2008 through 2012.

the hsbc settlement allowed more than 2,000west virginia consumers to receive payments or rate reductions, modifications, and somedecrease on their loan. that was a good result for the consumers ofour state. and like the national mortgage settlement,the sun trust and hsbc lawsuits really sought to remedy that misconduct, that predatoryloan behavior, and the servicing and compliance failures that we saw in place for a long periodof time. so the object isn't just to secure money forthe consumers, the object is to fix the problems so they don't occur again in the future. we look at the morgan drexen case.

that focused on misrepresentations as to theprovisions of legal services and debt settlement. i can tell you, one of the issues we spendthe most amount of time on from a consumer protection perspective is ensuring that ourdebt collection laws are upheld vigorously, whether we're dealing with the issues pertainingto how the debt is collected so that there are reasonable tactics for collection, orensuring that the entities are licensed in the state of west virginia before they collectthe resources. we take our responsibility over that very,very seriously. we've also been pleased to work with cfpbon other issues of importance for the state, including the closure of itt technical institute,which had provided courses to hundreds of

and i think there are many other examplesof how both agencies have worked to try to enforce the laws that we're responsible for. in my office, we try to attack every violationof the law aggressively. we don't care about political affiliationor economic status as we're enforcing the law. our job is to make sure that consumers areprotected. since 2013, my office's consumer protectiondivision has brought in over $84 million through lawsuits, assurances of discontinuances, andit's also secured almost $30 million in debt cancellation for consumers.

it was also mentioned earlier we were ableto secure a separate $160 million settlement from frontier communications, over $10 millionof which went to help consumers to address the issues that came from promises that weren'tmet. $150 million went back to invest to ensurethat the internet speeds were going to be increased because, once again, it's not enoughto just collect a settlement, we actually have to change behavior and make sure thatthe underlying problems that gave rise to the consumer protection violations in thefirst place are addressed. that's part of our underlying philosophy withinthe west virginia attorney general's office. beyond the actual settlements, the lawsuits,and the investigations, we also spend a lot

of time educating west virginians on scams. every day someone is being ripped off acrossthe country, and within the state of west virginia. so we have consumer advocates that are spreadout throughout the state meeting with people, collecting consumer complaints, and, mostimportantly, educating people about what's going on. i can tell you that consumer scams are a realproblem because fraud is a very real problem. and i'll give you a personal experience thatoccurred just the other day. i was down in greenbrier county, and i wastalking to an 86-year-old elderly woman.

she was recently ripped off by the grandparentscam, and she talked through how the scammers called and they were able to reach out toher, and how they set up this manipulative system in order to rip her off. scams affect the elderly, but they also affectpeople regardless of demographics, regardless of age. all of us have a responsibility to protectthe most vulnerable and to ensure that people know more about these types of scams so theydon't fall prey to it. it's easier than people think to fall victimto a scam, and i can personally attest to i can tell you about many people who havedealt with technology scams, especially in

a state like west virginia, when you're dealingwith slower internet, when people say, "does your computer have a virus? does it have a problem? would you mind if we access that? because we can fix your problem." those are issues that come up all the time,but then you're surrendering your precious personal identification away. we have to educate people, and that has tobe done every single day by everyone here in this room.

this is critical. so we're busy working on a lot of consumerprotection issues, but i also want to mention another matter that i think is critical asconsumer protection. while we have a great topic today in termsof alternative data, i want to emphasize that consumer protection matters also include fightson substance abuse, and we've worked very hard to educate people in west virginia aboutthe nature of this devastating problem in the state. west virginia has the highest drug overdosedeath rate in the nation, at 41-1/2 people per 100,000.

no office has done more to be more aggressive. we've been very holistic, going after it froma supply, a demand, and an educational perspective. and just recently my office announced thelargest pharmaceutical settlement ever in the history of the state, $36 million. it's my hope that a lot of that money cango to treatment and to address this terrible problem. we have to hold everyone accountable withinthe pharmaceutical supply chain and change that problem. it's a real public health crisis, and i'mcommitted to continue working on that.

in closing, every part of consumer protectiondemands our daily attention. and i look forward to working with all ofthe relevant agencies, people within the state, and in washington to make sure that we achieveour common goals of protecting consumers, going after consumer fraud, and respectingour boundaries, making sure that federal and state agencies comply with the constitutionand our laws. but, director cordray, i'm particularly gratefulthat you're here today to grace your presence in our incredible state. thank you and to your team for coming heretoday. and we'll look forward to hearing about thisexciting new topic.

thank you. thank you, attorney general morrisey, forthe remarks and for the warm and gracious welcome. it is reciprocated, and we look forward toworking with you in the future. i am now pleased to introduce richard cordray. prior to his current role as the cfpb's firstdirector, he led the cfpb's enforcement office. before that, he served on the front linesof consumer protection as ohio's attorney general. in this role, he recovered more than $2 billionfrom ohio's retirees—for ohio's retirees—

—for ohio's retirees, investors, and businessowners, and took major steps to help protect its consumers from fraudulent foreclosuresand financial predators. before serving as attorney general, he alsoserved as an ohio state representative, ohio treasurer, and franklin county treasurer. director cordray? actually, zixta, your unexpected and unusualstumble there reminded me of when my children came home from school, they were in elementaryschool at the time, and they had learned that day that commas save lives, we could say prepositionssave lives. they were talking about the difference betweena t-shirt that said, "let's eat, grandma,"

as opposed to "let's eat grandma." i also want to take a moment to really thankgeneral morrisey. he and i had a chance to talk a little bitahead of time, and we share a common view that consumer protection is a form of lawand order, and people who break promises to the citizens of this state or those of theunited states or know that there are laws in place and violate them in order to getan advantage or get money in their own pocket, those are people who need to be enforced againstvigorously. we try to do that at the consumer bureau,and the west virginia attorney general's office definitely tries to do that.

and, general morrisey, i also appreciatedyour comments about opioids, although there are many issues that go well beyond what wedo at the consumer financial protection bureau, it's a reminder that the state attorneys generaldeal with the entire menu of public policy problems in the states across the country. and we're happy to have the chance to workshould-to-shoulder on them in our particular area of consumer finance. so thank you all for joining us. and we're glad to be in charleston as we exploresome new frontiers for consumer access to credit.

as many of you know, the consumer financialprotection bureau is the single federal agency with the sole mission of protecting consumersin the financial marketplace. we're working to ensure that consumers cangain access to financial products and services that are fair, transparent, and competitive. in this spirit, we continue to encourage consumer-friendlyinnovation, such as through our project catalyst. so today we're announcing a request for informationabout unconventional sources of information, new ways to analyze this data, and how newtechnologies can help in assessing people's creditworthiness. we want to learn more about whether this kindof alternative data could open up greater

access to credit for many americans who arecurrently stranded outside the mainstream we also want to understand how market participantsare or could be mitigating certain risks to consumers that may arise from these innovations. let us begin by reviewing how our mainstreamcredit system generally works. until the rise of the modern credit reportingindustry, many loans were made based on personal relationships of longstanding that developedbetween creditors and their customers. someone who knows all about your personalfinancial story, including your way of making a living, your accumulated wealth, your spendinghabits, and your family background has an excellent vantage point for deciding whetherit's a good risk to extend credit to you.

based on everything they know about you, theycan size up your creditworthiness, including any collateral you may be able to post tosecurity. thus, they can make a pretty careful determinationas to whether you're likely to recover what they decide to lend to you. although this framework still describes somefairly vigorous modes of local lending in this country, particularly at community banksor credit unions, where it's quite successful, we've also developed another credit frameworkin our society. it uses automated underwriting systems, andit's built on extensive data about people's credit histories and algorithms that are usedto analyze that data.

this newer approach reflects changes in oursociety, such as increased mobility and the growth of national banks and online financialfirms. these companies are not in the same positionto know all the detailed history of local communities and individual customers at apersonal level. this approach also reflects new technologicalcapabilities that can mine huge mountains of data and determine mathematically whichelements are most closely correlated with future performance. to get a loan under this more automated framework,a consumer typically needs to have a credit score.

an individual credit score is fashioned fromthe information contained in individual files that are managed by nationwide credit reportingcompanies. they typically have files on 200 million americansor more. this is the product of the modern era, nowgreatly bolstered by computerized databases. each file, known as a credit report, tellsthe story of a consumer's credit history and current credit usage, at least what can beknown from the information that's actually in the file. it records the size and type of loans madeto the consumer, what is owed, how much credit is available, and whether prior debts werepaid on time.

it may list personal loans and car loans,credit card balances, student loans, and mortgages. it may also note unpaid bills and debt collectionand list court judgments, liens, or bankruptcies. this credit history is then used to determinehow likely consumers are to repay existing debts and to gauge the prospects for repaymentof any new debts they may take on. some of the limitations of this system derivefrom historical and contingent circumstances. for example, consumers often try just as hardto make their monthly rent payments—i know i did when i was a renter—as they do theirmonthly mortgage payments, but rent is often omitted from credit files, unlike mortgagepayments. this may be because rent is not typicallyviewed as credit or it may be because mortgage

loans are made by banks and financial companiesthat have mechanisms for keeping careful records of them, which result in more regular categoriesof reportable data. by contrast, rents are collected by millionsof individual landlords scattered all over the country, and data on those payments isnot collected in any systematic way. to take another example, debt collectors oftenreport data on the debts they are collecting, including debts arising from unpaid medicalbills, for example, but the billers themselves, such as medical providers, do not report suchinformation. credit files thus may include informationabout bills you failed to pay but not necessarily about all the bills you did pay.

in automated underwriting systems, and evenin many manual underwriting systems, decisions to grant credit and set interest rates onloans are based on credit scores to a large degree. these familiar three-digit scores are drawnfrom the information contained in individual credit files. as such, credit scores play a central rolein the financial lives of american consumers. they can determine whether people will begranted credit at all, the terms or conditions for doing so, and including the interest rate. the availability of credit scores and theaccuracy and completeness of the underlying

data have thus become increasingly importantto almost all americans. unfortunately, one of the reasons we're heretoday, for many consumers with a limited or nonexistent credit history, a credit scoreis out of reach. the consumer bureau has run the numbers andestimates that 26 million americans—26 million—are credit invisible, meaning they have no credithistory at all in these files. under the most widely used scoring models,another 19 million people have credit histories that are too limited or have been inactivefor too long to generate any credit score. here in west virginia, nearly 180,000 residentsare credit invisible, and nearly 130,000 more residents have too little credit history orhistories that are too inactive to have a

credit score. added up, about one in five adults here inthe mountain state are hampered in their financial lives by a lack of a credit score. the same story could be told virtually anywherein the country, since 45 million adults, americans, fall into these categories nationwide. people with little or no credit history orwho lack a credit score have fewer opportunities to borrow money in order to build a future,and any credit that is available usually costs more. that only deepens their economic vulnerability.

among them are those living in lower incomeneighborhoods, young people just starting out in life, and many who are recently widowedor divorced and may not have yet built sufficient credit history on their own. many people without credit records or creditscores work hard and strive to pay their bills on time. they may live paycheck to paycheck strainingto make ends meet. they often are caught in a catch-22, unableto get credit because they have not had credit before. they cannot seize meaningful opportunities,such as borrowing to start a business or buy

a house. for these consumers, the use of unconventionalsources of information, known as alternative data, may allow them to build a credit historyand gain access to credit. alternative data may draw from sources suchas rent or utility payments. these obligations may not qualify under moretraditional definitions of credit, and, as a result, would not be factored into the creditdecisioning process. alternative data may also draw from electronictransactions, such as deposits, withdrawals, or transfers from a checking account, andit can encompass the kinds of information that relationship lenders typically know asa matter of course, such as the consumer's

occupation, educational attainment, and variousother personal accomplishments. new forms of alternative data may come fromsources that never existed before, such as the way we use our mobile phones or the internet. by filling in more details of a consumer'sfinancial life, this information may paint a broader and more accurate picture of theircreditworthiness. adding this kind of alternative data intothe mix thus holds out the promise of opening up credit for millions of additional consumers. alternative data holds out further promiseas well. credit scores, by their very nature, are backward-lookingindicators.

consumers who experience a financial hardship,such as the loss of a job or a large medical expense—and many people experience suchhardships—may fall behind in making credit payments. this may tag them with a low credit scorelong after their financial situation has turned around. alternative data may help lenders identifymore precisely from those who currently carry so-called subprime credit scores a substantialsubset of consumers who are, in fact, good credit risks. these people should not be held back simplyby their retrospective credit score.

the request for information we're issuingtoday looks into the pros and cons of these uses of unconventional sources of information. we're examining what data are already availablefor use today and we're looking into what the future may hold as technologies continueto evolve. we're seeking to study how these data arebeing gathered and analyzed in underwriting models now used by banks and other financialcompanies, including the so-called fintech companies, and we're seeking to better understandhow these models and modeling techniques are evolving. this request for information focuses on fourmain issues.

first, it looks at the potential risks andbenefits for consumers of using this additional information to better assess their likelihoodof repaying a loan. second, it looks at how introducing new alternativedata sources into the credit decisioning process might add to its complexity. among other things, we want to find out ifthis will make credit decisions more difficult for people to understand, and thus make itharder for them to control their financial lives. third, the request for information looks athow the use and interpretation of these data may affect privacy and transparency.

and, finally, it looks at whether relianceon some types of alternative data could result in discrimination, whether inadvertent orotherwise, against certain consumers. let me start with the first point, accessto credit. as i mentioned, a key question for the consumerbureau is how people without a credit score could begin building a credit history. we want to learn more about how we could promotethe responsible use of alternative data even as we continue to protect consumers' interests. for instance, someone with no credit historymight nonetheless be quite reliable in paying their cell phone bill or their rent on time,or they may have a history of checking account

deposits and have made good use of a debitcard. this might make them a very viable creditrisk. we know that some lenders will not loan moneyto consumers with a credit score that is less than, say, 620, according to traditional measures,but they might do so if alternative data suggests that a particular consumer with such a scorewould be less likely to default on the loan as based on this other type of information. this leads us to the second issue. even as alternative data may shed more lighton a consumer's creditworthiness, the sheer volume of new data that may be streaming intothe system could have other effects.

on the one hand, new analytical methods basedon unconventional information could produce a faster, less complicated application processwith lower operating costs for lenders, and, thus, lower loan costs for borrowers. on the other hand, the accumulation of moreand more alternative data could create a tangle of information that is harder for people tounderstand and unravel. the credit process can already be somewhatmurky, so we want to learn whether folding in alternative data could complicate the decisionsfacing consumers. the harder is it for consumers to understandtheir credit record or whether they're likely to qualify for certain loans, the harder itwill be for them to master their finances.

the same complexity could also burden lenders,who must explain adverse credit decisions to consumers. and it may bog down financial educators andcounselors who are trying to help people understand their credit standing and take more controlof their financial lives. the third issue we're raising today concernshow alternative data is shared, by, and to whom, and whether these interactions are safeand secure. we want to know whether this information isreliable and whether its use is transparent some consumers may not even know that theinformation was collected and shared, let alone how it may be used in the credit process.

we're also exploring whether some informationis more prone to errors because it was collected under weaker standards in place at the time. another question is whether consumers cancorrect any mistakes that turn up. as part of our inquiry, we're looking intohow the credit reporting laws may apply to these and other issues. and, finally, we're looking into how thisinformation, even if entirely accurate, may be applied or interpreted. if the use and analysis of alternative dataleads to certain consumers being needlessly penalized, we want to know that.

for example, some newer underwriting algorithmsuse measures of residential stability. these measures may help predict creditworthiness,they may well do so, and may identify consumers who make their rent payments on time. yet members of the military, to take one example,are required to move frequently as their duty stations change. as a result, this particular measure couldhinder access to credit for service members, even if they are in fact a good credit risk. other data may be strongly correlated withcharacteristics such as race or gender, which could enable lenders to do indirectly whatthey're forbidden from doing directly, drawing

conclusions about whether to make a loan basedon a person's race, gender, or other prohibited categories. similarly, data tied to a consumer's placein the economic ladder may hinder those trying to climb it. this may be especially true for those whoare already struggling financially and facing a system that's full of obstacles. so we're looking into how fair lending lawsmight apply to these and other issues. as we consider how the risks of alternativedata may give rise to the potential for discrimination, i want to pause for a moment and make clearour intentions with the request for information.

the fair lending laws are designed to promoteequal access to credit for all americans without regard to race, sex, ethnic background, ora variety of other personal characteristics. the reason for these laws is to eliminatesuch credit discrimination in the financial marketplace, but if fair lending concernscast a large enough shadow, they may prevent people from considering and using alternativedata that might open up more credit for minority and underserved consumers. this could interfere with progress for thevery people these laws are intended to protect. equal access to credit means even more ifoverall access to credit is expanded and not constrained by lingering uncertainty abouthow regulators intend to apply fair lending

laws. so we've crafted this request for informationto help us better understand whether and how such uncertainty may be hindering credit accessfor disadvantaged populations. we also want to learn more about how the consumerbureau might reduce that uncertainty while holding fast to the anti-discrimination principlesthat are the cornerstones of federal law. that would help market participants go abouttheir business with more confidence that they can better assess the creditworthiness ofparticular consumers without running afoul of legal requirements. in short, we see alternative data as holdingout the promise to the very populations that

may be most disadvantaged by excessive relianceon traditional credit reports and credit scores. and we're committed to having a full and frankdiscussion about how we can minimize the risks and maximize the potential benefits. with the request for information that we'reissuing today, the consumer bureau invites all who are interested in these developmentsto share their views on this rapidly evolving aspect of financial services. we strongly encourage affordable, responsiblelending to more people who may be already deserving of the opportunities that creditcan bring to their lives. at the same time, we want to make sure thatall lenders are playing by the same rules.

this evenhanded oversight both protects consumersand ensures a level playing field for the financial industry, and it applies to bothbig banks and small startups. we want to learn more about how the use ofthis data affects consumers and how it's being analyzed and interpreted, and we want to knowwhether it can help more of our neighbors gain control of their financial decisions,enjoy more options, and achieve their own vision of the american dream. thank you, director cordray. at this time, i would like to invite the paneliststo take the stage. while they are doing so, i will briefly introducecfpb and guest panelists.

david silberman serves as the bureau's actingdeputy director and associate director for the bureau's research, markets, and regulationsdivision. gail hillebrand serves as the associate directorfor the bureau's consumer education and engagement division. keo chea serves as the assistant directorfor the bureau's office of community affairs. our guest panelists are chi chi wu, attorneywith the national consumer law center; aaron rieke, principal of upturn; amanda jackson,organizing and outreach manager, americans for financial reform; michael gardner, seniorvice president of specialized services and initiatives, equifax; nipun goel, senior marketand portfolio strategist with kabbage; francis

creighton, executive vice president of governmentaffairs, financial services roundtable. david, you have the floor. thank you, zixta. good morning, everyone. as zixta said, i am david silberman. i am the acting deputy director of the cfpband the associate director of the bureau's division of research, markets, and regulations. it's my pleasure to be with you to moderatethis panel discussion portion of our hearing about the use of alternative data and modelingtechniques in the credit process.

as zixta indicated, we're going to hear todayfrom a number of respected panelists, including consumer advocates and industry participants. each panel member will give us some backgroundand provide their perspective. we'll then pose questions to our panelistsand engage in a discussion. the panel discussion will then be followedby the public comments component of the hearing, where we will hear from members of the publicwho have signed up to share their observations. as director cordray noted in his remarks,the bureau estimates that approximately 26 million americans have no traditional credithistory, and are considered credit invisible, and another 19 million americans do not havesufficient or recent credit history to generate

a credit score under commonly used scoringmodels. for these 45 million americans, obtainingcredit can be difficult, if not impossible. as director cordray has explained, the useof alternative data could expand access to credit for these consumers, but certain typesof alternative data presents significant risks for consumers. so as you've heard, today the bureau has publisheda request for information, or rfi, to seek information about the use of alternative dataand modeling techniques in the credit process. the purpose of this rfi is to assist the bureauand market participants in better understanding what's happening in the market and what issuesare raised.

we want to be able to assess whether thereare steps the bureau can and should take to facilitate practices that enable responsibleinnovations, allowing consumers to realize the benefits of alternative data while providingnecessary consumer protections and safeguards to mitigate any consumer risks. in the past 10 months, bureau staff have beenmeeting with market participants, including incumbent financial institutions and fintechfirms, with consumer advocates, with academics, and with our sister agencies to understandthe benefits and risks of various types of alternative data and modeling techniques. while we've learned much, there is still agreat deal to be learned.

today's rfi and this field hearing are thenext steps in the process as we move forward to seek to ensure that consumer benefits fromthe use of alternative data are realized and that risks are addressed. so with that in mind as a framing, i wouldlike to invite our panelists to present their opening remarks. each panelist will have 3 minutes to makea brief statement. following this, gail, keo, and i will moderatea discussion. so we'll start, we'll go down the line, andwe'll start with chi chi wu, from the national consumer law center.

thank you, david, for both the introductionand those remarks. so alternative data, there is a lot of buzzabout alternative data. that's why we're here. right? it's seen by many as a potential solutionfor this issue of credit invisibility. and for some, it's seen as a panacea. for us, though, on the consumer advocacy side,it's definitely not a panacea. it could be a solution, but more likely it'sa tool, and like any tool, it could be good or it could be bad.

it depends, from our perspective, on whatkind of data is being used and how that data is being used. for us, the devil is always in the details. and so i'm going to talk a little bit aboutthe kinds of alternative data that have been bandied about, and more right now the moreconventional alternative data. so, for example, director cordray mentionedrental data. it seems from some of the pilot studies, rentaldata could be very promising, especially given that the rental data that's being added ispredominantly positive data. the negative data is already in there in termsof there being collection items.

and new information, negative information,in terms of late payments isn't being added. so that's one that seems to have promise. on the other hand, a type of data that we'vebeen strongly opposed to using, because we think it's potentially harmful, is gas andelectric utility data. this is an industry that is very heavily regulatedbecause it's a natural monopoly. there are a lot of strong consumer protections,and we think that monthly reporting of gas and electric utility data will undermine thoseprotections. plus, it's a payment obligation that reallyfluctuates greatly. in the north, during the winter, you get highbills; in the south, high bills during the

summer; but then as the seasons roll along,the obligations drop. and so people struggle to pay their billsduring high usage areas, and it depends on the weather, which is one thing that consumersdefinitely can't control. so that's one we have concerns about. telecom, cable, and cell phone data, on theother hand, doesn't raise those same concerns, because it's not as heavily regulated. the issue with that is we want to make surethat when such data is used, consumers' rights to dispute errors or problems is properlyprotected, because, as folks know, people do have problems with their cell phone andcable companies every once in a while.

but there is potential there. and then how it's used is also important. we are more concerned about when a bunch ofnew data is dumped into the files of the big three credit reporting agencies because whilethere are maybe 50 million people who are credit visible, there are 200 million whohave existing files. and will that data help or hurt? for the credit invisibles, will it createa bad score? credit invisibility may be bad for creditpurposes, but those big three files are often used for other purposes, such as employmentand insurance, where invisibility isn't such

a bad thing, where a no hit is better thana bad score. so the way the data are used is very important. and then just a couple seconds on less conventionaltypes of alternative data: social media, internet searches, things like that. those, they have potential, but they alsohave risk; most importantly, accuracy and predictiveness. how do dispute whether your internet searchresult accuracy is being properly recorded? whether it's my searches on my laptop or my14-year-old son is engaged in some interesting searches.

so we'll leave it at that. aaron rieke, from upturn. hi, there. first of all, thank you very much to the bureaufor holding this hearing and for looking into this issue and its past research on this issue. and i want to start, i'm i think here partiallyto be the public interest computer nerd on the panel, but i want to start with what makesthis interesting beyond the technology and beyond the data, and that's what directorcordray mentioned in his remarks, that there are about 45 million people in this countrythat are currently underserved by this traditional

system of credit reporting, and many of thosepeople have various other vulnerabilities that contribute to their absence in the system. and so i just want to keep that as the startingtouchstone and the touchstone we return to as we continue this discussion. without access to credit, what's there ispretty ugly. i did a report last fall where we looked atthe sorts of search advertisements that came up when you searched for "i need money fast,"and it's really not pretty. so i think part of this is actually defendingconsumers against the really atrocious products that await them when they don't have otheroptions.

i agree with everything that chi chi justsaid in terms of data potentially being a tool that could be part of the answer in improvingthe situation. i want to start with a broad theme, and i'llgo into more details as the questions emerge throughout the hearing, which is that thegoal here is not just to improve lenders' and banks' ability to predict creditworthiness. that's certainly an important piece of thesolution, and having some sort of good prediction is oftentimes better than having no prediction,but i think it's really important that we not lose sight of the fact that predictionis half of what we're looking for here, and the other half of what we're looking for hereis fairness.

we're not just looking at new big datasetsto see what kind of combinations of things correlate with likelihood to repay versuslikelihood not to, that's a starting place, but that we need to think carefully aboutwhether or not the predictiveness and the correlations that we're seeing in the dataare actually serving the people, the 45 million people, that we're setting out to help here. we shouldn't allow protected class to sneakin somehow and be something that's driving predictions. where you live shouldn't sneak in somehowand be driving predictions. who your friends are shouldn't sneak in somehowand be driving predictions.

so we need to be really, really careful, especiallyas we grow the datasets, especially if we ever get to the point where we're thinkingabout something like social media data. we always need to ask, why is this drivingpredictions? and is this the kind of score and the kindof data we want driving these decisions in our society? we don't want to get past the point wherethese decisions aren't explainable anymore. i think "murky" is a great word for our currentcredit system. it's going to get harder the more data weadd. and so i think we need to keep explainabilityboth for consumers and for regulators in the

puzzle here. and, finally, i think we need to look at thebigger questions of, are we driving virtuous cycles rather than spirals down? are the sorts of data we're adding in likelyto elevate people, or are they going to suffer from the same issues of punishing people thatfall on hard times? so i think that, again, the theme i just wantto start with here, prediction is half of the equation, but we can't lose sight of asking,why is it predictive? is it predictive in a way that we want ourcredit scores to be predictive? and is this actually going to lift up thepeople that we care about, that kind of core

measure of success? amanda jackson, from americans for financialreform. and hi, everyone. thank you for the opportunity for americansfor financial reform to join this important panel on the use of alternative data to helpmake credit scoring decisions. as you may know, americans for financial reformis a project of the leadership conference on civil and human rights, and continues tosupport and seek insight into data conversations. alternative data is just part of the largeruniverse of big data. of course, the credit bureaus which collect,aggregate, and then sell credit reports based

on billions of bits of information concerningwhether and how millions of consumers pay their bills and whether they pay them on timehave been using big data for years. credit scores, whether sold by the bureausor others, are based on credit reports. but with the growing power of network computersand the ubiquitous data collection system that has grown up in the digital economy,more and more data are being collected, and more and more algorithms are being proposedto aid in corporate decision-making. recognizing the importance of looking at bigdata through a civil rights and consumer protection lens, several years ago, the leadership conferenceon civil and human rights, leading members of americans for financial reform, aided bythe technologies here at team upturn, worked

with a broad group of organizations to formcivil rights principles in the area that is big data. we aim to stop high-tech profiling, ensurefairness in automated decisions, preserve constitutionality, enhance individual controlof personal information, and protect people from inaccurate data. these principles represent the first timethe national civil and human rights organizations have spoken publicly about the importanceof privacy and big data for communities of color, women, and other historically disadvantagedgroups. through these principles, we and the othersignatory organizations highlight the growing

need to protect and strengthen key civil rightsprotections in the face of technological change. today, discrimination is not just a productof biased human decision-making; rather, as the obama white house noted at the conclusionof its review on big data, discrimination can result from the way big data technologiesare structured and used. the data we have reflects our history, whichis in part a history of systemic unfairness towards some consumers in the consumer creditmarketplace and systemic economic exclusion in the broader economy overall. whether big or small, more data and more kindsof data will play an even bigger role in the future of lending.

some of this data may help more americansjoin the financial mainstream, helping to identify where individuals in protected statusgroups aren't enjoying the same access to credit as similarly qualified non-minoritybars. so while i'm hopeful that more uses of bigdata will unlock new benefits, i am also concerned about its risk. the same is true with smaller alternativedatasets. the benefits of adding rental and utilitydata to credit reports on credit scores for some consumers must be weighed against itsrisk, as my colleague at the national consumer law center explained today.

but one thing is very clear: as we move forwardto understand the implications of automated decision-making on financial opportunity,we are grateful to cfpb, an effective independent agency that has had the best interests ofconsumers at its core mission, is on the job. the area of big data is one in which we particularlyneed rigorous oversight and standard setting in the public interest. otherwise, the corporate users of data willhave all the information, and members of the public will have no way to see the big picture. this is just one more reason we think it'simportant that the cfpb keep on the path of its independence and rigor in tact.

thank you, amanda. so now moving to my left, michael gardner,from equifax. david, thank you, and thank you for havingus as a part of this panel. equifax is obviously inextricably linked tothe credit report, as we have traditionally defined it, and we take that responsibilityvery seriously. but we have also been in the game of whatwe call additional data. here we call it alternative data—right?—whichis, what additional data can be found in the marketplace that can be used to predict aconsumer's creditworthiness? and that interest in additional data or alternativedata goes back really almost 15 years for

equifax. and we believe that that alternative databenefits both consumers and the financial system, and we approach how we use that datalooking through both of those lenses. one of the other things that is perhaps abit beyond the scope of this particular hearing, but i would like to get it out there, is wealso look at how alternative or additional data can be used in use cases prior to gettingto the financial risk decision that a lender might make, and facilitating those decisionsthat get to that credit risk decision are equally as important to consumers as the finalcredit risk decision. if i can't be identified appropriately, etcetera, then i'm not even going to get to

the point of having a risk decision made. we at equifax are also known for housing thenational consumer telephone and utility exchange database. that is a voluntary database that includesutility, telecommunications, pay tv, and additional data for consumers. that database now approaches 215 million consumers. at any given time, that database has somewherebetween 25 and 30 million consumers in it that we do not find present on our creditfiles, so correlating very closely, director cordray, to the numbers you mentioned in yourcomments.

so we leverage this data and other additionaldata along with the traditional credit data in models that we use, or that we sell, obviously,and that our customers, financial institutions, and others use in the marketplace to assesscreditworthiness. those models and new modeling techniques area big part of how you leverage this data. other panelists have mentioned that some alternativedata does not have the same coverage, and, therefore, you have to identify modeling techniquesthat overcome that coverage issue. the most notable one of those solutions thatis in the market today is our fico xd solution where we partnered with fico as the modelingarm. we leveraged the nctue data and traditionalcredit data, and we also leveraged public

record data sourced from lexisnexis risk solutions. so a lot of alternative data going into asingle credit score, with the purpose of that score being specifically targeted to allownon-scorable consumers on a traditional credit risk score to be scored by the credit cardindustry. and we do all of this with a very, very strongfocus on maintaining the same types of consumer protections for any data that is used in arisk decision that we apply to the core credit data. nipun goel? hey, good morning, everyone, and thank youso much for the opportunity to talk about

a subject that is truly near and dear to ourhearts over at kabbage. just to give you a quick overview of whatwe do, we are a tech and data platform, or, if you will, a fintech company, that's focusedon delivering credit to small businesses all over the u.s. and so while we're not squarely in the consumerspace, i think where we are squarely focused is leveraging alternative data to broadenaccess and break down barriers to traditionally underserved markets. right before we got on the stage, i took aquick look, and in the state of west virginia, to date, we've delivered $7 million in capitalto over 200 small businesses.

and so our company was founded sort of onthis question and notion of, how do we use e‑commerce seller data to provide loansto businesses selling on e-commerce platforms? and that sort of notion has been what's trulybeen the foundation of our growth and trajectory over the last few years. and so for the purpose of this discussion,i would like to offer some of the guideposts that we've used as we've sort of looked foralternative data sources and have served both us as well as our customers pretty well. the first being we look to leverage data sourcesthat our small business—again, caveating that we operate in the small business space—wetry to look for data sources that our customers

are already familiar with. some examples here include connect your quickbooksaccount if that's what you use to manage your accounting. if you use stripe to process payments, welook to use that sort of data. and so what that does for us is it gives usa very in-depth and real-time understanding of the financial and operating health of acompany. what it does for the consumer themselves,it actually engages them in a more in-depth and sort of engaged experience with the lendingprocess and really gives them a stronger voice in the lending process itself.

the second guidepost that i would sort ofthrow out there is one around transparency and reinforcing the use of how we're usingthat data. and so that's not to the level of sort ofbreaking down the machine learning algorithms and narrowing out every single feature, butreally trying to understand for our customers, for example, using financial accounts to understandcash flows, or if they are a seller on an e-commerce platform, using that to understandsales activity and getting, again, a more in-depth picture of what we can use to deemcreditworthiness. so if borrowers the alternative data sourceand how we're using it, and we, as financial service providers, are both transparent inour use and extremely rigorous in our diligence,

as i think a few folks here have mentioned,what we enable is just a more informed, straightforward lending process. in summary, whether we're using alternativedata as a single model or embedding it on top of existing credit features, what i wantto highlight is not striving just for transparency, but effective engagement of the customer inthe process as well. finally, francis creighton, from the financialservices roundtable. thank you very much for having me here thismorning, all of you from the cfpb, especially director cordray. i work at the financial services roundtable,a trade association of about 100 of the 150

largest financial services companies in thecountry, including banks, insurance companies, payments companies, investment firms, andothers. this is a really exciting issue for us, andit's exciting because there is so much innovation and good thinking going on here. and that innovation should help more peopleget access to the kinds of financial services products that they need from the regulatedlending community. first let me note that financial institutionsare both furnishers of information and consumers of that information, and i'm here today totalk mainly about how financial institutions use the information that they obtain fromothers to make lending and other financial

decisions. now, financial institutions use data and informationto assess how people have handled their bills in the past because that can be predictiveof how they might handle their bills in the future. we use this information to judge whether apotential customer will be able to meet their future obligations. so from that perspective, the more information,the better. having more information gives us a betterability to determine whether we should make a loan or not.

we welcome more data and are working withour partners in the consumer data industry to get more because it helps us better serveour customers, both current and prospective. however, we need to make sure that the datawe're using for lending decisions are fair, accurate, and verifiable, and that consumershave the ability to dispute information that they believe is inaccurate. the fair credit reporting act is a stronglegal protector of fairness, security, accuracy, and data integrity. but given that more data is more helpful,we should also remember that we get data through a system where furnishers voluntarily providethe information and consumers of information

voluntarily use it. individual credit bureaus work hard to improvetheir data offerings, and that innovation and competition among them makes the processbetter. therefore, we would be very concerned aboutany efforts to mandate what data we could and could not use. further, what makes our system strong is thatwhen we use data, we have access to both positive information and negative information abouta consumer's payment history. both types of data play a role in assessinga consumer's situation, and limiting the type of data we can use we think would be counterproductive.

we need both sides of the coin. we also want to note that as new data becomeavailable, we have to example the implications of that data, not only on its predictiveness,but also for implications on fair lending and other regulatory grounds. for example, if some new data were more availablefor urban rather than rural families, that could result in thinner files for rural dwellerswith the attendant impacts that could have on underwriting decisions impacting them. as the bureau examines the implications ofusing new types of data, we just ask that these potential unintended consequences bekept in mind.

finally, i would like to just note that dataare used for reasons beyond whether we should grant credit. for example, our members may use data notcovered by fcra, the fair credit reporting act, including some forms of alternative data,these new kinds of data that some of you have referenced, for purposes like fraud protection,identity resolution and verification. using data for lending and other fcra-coveredpurposes should not be conflated with data that may be used for these non-fcra purposes. again, thank you all very much for havingme at this hearing. i look forward to working with the cfpb, ourcolleagues in industry and in the advocacy

community, to get this right, because if wedo, we'll be able to help more of those people that the director referenced in his earlierstatement. i want to thank all the panelists for yourthoughtful remarks. we now have gail, keo, and i now have an opportunityto engage in some q&a and try to engender some interesting dialogue. i'll ask the first question, and i'll askit of you, francis, if i may. so what effect do you think alternative datawill have on the 26 million credit-invisible americans and the 19 million who don't haveenough data in their credit files to have a credit score under the commonly used scoringmodels?

what effect will alternative data have onthose? and also what effect will it have on thosewith existing credit profiles? so i know we're far from washington, but i'llgive you a typically washington phrase first: it depends. and that's a really important point becausethe strength of our system is that we look at people's individual situations. so those 45 million people that you're referringto, the 300,000 people here, what does their individual circumstance show? we would probably be able to serve more peopleas a result of that, but what we don't know

until we really do some research and havesome historical data and everything else, is what are the implications of who we serve,and, therefore, who we don't serve, on fair lending and other questions? and my members would be very unlikely to useinformation until they really can understand that because while they think that they'redoing everything correctly and according to the law when they make the decision, we knowfrom past experience with the bureau and other agencies that making the decision right onan individual basis in the past won't necessarily be viewed when you look at the book of businessin aggregate at some point in the future. so we want to be very careful of that, andwe really owe that to our fair lending and

other regulatory obligations that we do so. keo? [speaking off mic] so i think that's an enormously importantquestion. by no fault of the bureau, alternative datais an enormously broad term, and i kind of want to make a binary distinction about whatwe're talking about. on one hand, we've heard things like utility,rental, telecom bill, payment-related behavior. we heard kabbage talk about payment-relatedbehavior for businesses on e-commerce platforms. that's something that i've just started callingmainstream alternative data.

and i say mainstream because this is the kindsof data that the system and regulators are used to seeing. how have you paid your bills? what does your cash flow look like? and i want to hold that in one hand as somethingthat i have some cautious optimism about with the giant asterisk of all the things thatchi chi said in her opening statement about making sure that consumer protection lawsrelated to utility companies are respected at the state level, making sure that peoplearen't otherwise coerced with even these basic data sources.

but i just to want to like call that mainstreamalternative data, how you pay your bills, and i think we have a better sense of howto deal with that, some of the big credit scoring models are already ready to considerthat. we know how that works. on the other end of the spectrum is probablythe stuff you read about in the media a lot more, which is the social media data, theweb data, the quote/unquote big data, and that's an entirely different ballgame, anentirely different ballgame. no one knows—i don't think i can find adata scientist in the world that is ready to come and sit here and say if you throwfacebook data into a consumer credit profile

and make a credit score off of that, thatwe have any idea how to make sure that that's in any way, shape, or form fair, that it isn'tproxying for race, that there is actually some explainable relationship between whoyour friend network is and how creditworthy someone thinks you are. we're not even close to being ready in myopinion to throw social network data into the mix. and i don't think that anyone sitting up herewould disagree with that. but i just want to point that out becausesometimes the specter of social media data, of all the big marketing data on the internet,is kind of the tail that wags the dog of this

discussion in a way that i think distractsus from the potentially more kind of silver and concrete near-term steps. and i just want to point out that i thinkit was early last year, facebook—many of you are probably on facebook—put a policyinto place that prohibits any outside party using facebook data to make eligibility decisions,and that includes credit and all the other fcra-covered purposes. so you have, at least in facebook's judgment,for whatever purpose, for whatever their internal reasons are, which i don't know, but no oneshould be using facebook data to make these and so if you see startup companies claimingto do so, think twice before investing.

and the very last point i just want to makehere is that one concern that many of us have on the advocacy side of the table is evenwhen you're in that comparatively safer territory of bill repayment behavior that we understand,i still pause because even when that goes into the fcra-regulated framework, that stillmeans that whether i pay my cell phone bill or my utility bill is something that couldbe visible and used by my employer, and that's not the topic of today's conversation, butthere is this kind of shadow of non-credit uses that gets cast across all of this thatdeserves some thought. and, david, i believe my microphone wasn'ton when i asked the question, so i'm just going to restate it for the record.

the question was, how do you define alternativedata as it is used in the credit process? and what is the impact that alternative datahas had and could have on consumers? thanks. you're not going to restate your answer, right? i hope not. gail? michael gardner, my question is for you. what data quality standards for issues suchas accuracy, review, correction, should companies supplying alternative data and companies usingalternative data be utilizing?

great. thank you for that question, gail. at equifax, we take the approach that anydata that will be used in any fcra-regulated decision should meet very, very similar datastandards. i intentionally did not say "exact" becausethe source of the data and the data elements in there are not going to be consistent. so that starts with working with our furnishercommunity, those that voluntarily provide the data, making sure they understand thedata that we would want them to be providing on a consistent basis, and giving them thefeedback when that data is not provided in

an appropriate format. then we look internally at equifax itself,and then, how do we manage our data? how do we make sure that the data is appropriatelyrefreshed? how do we make sure the data that is staleis removed? how do we make sure all of the legal requirementsfor fcra are met on each of our fcra-regulated databases? then internally, you also have to look atuse case assessment for how that data will be used. that's both in building our own products,and i think aaron just kind of alluded to

potentially how that data might be used asraw data out in the marketplace. and we manage that in multiple ways. most notably, we do not commingle alternativedata—so the nctue database, as an example—with the core credit file. so those are distinct databases, and anytimethat data will be commingled in a solution, that goes through extensive internal review. and then, lastly, we make sure that any datathat is used in an fcra use case can be properly disclosed to the consumer and can be appropriatelydisputed by the consumer. and so we make sure that those cras are managedunder the fcra.

so thank you. amanda, what consumer protections should beconsidered when alternative data is incorporated into predictive credit scoring modeling techniques? thank you for that question. so the cfpb already has two tools in its authority,the ecoa or—more "d.c.-speak" here, acronyms—but the equal credit opportunity act and the fcra,the fair credit reporting act. and adding a third, is authority to overseeand examine banks, and i think that that's critical for ensuring that the bureau leveragethat authority to ensure that banks aren't—or see what the secret of sauce is, to see whatpeople are developing with the algorithms

to see how various consumers perhaps are targetedor factored into those algorithms. lastly, i think the cfpb should ensure thatall companies are marketing their products as they should be marketed and aren't usingthem to promote financial opportunity through misleading norms. i think the bureau should make sure that allaspects of marketing comply with the fcra via ecoa and also make sure that various dataaggregators aren't tapping into or trying to avoid the fcra by lumping in data basedon myself with a neighbor or others in my community, just making sure that there isaccuracy and privacy with respect to the fcra. keo.

thanks, david. this question is for nipun goel. what role do you think alternative data hasin responsible consumer financial innovation? what are the key factors to ensure that useis responsible? thanks for that question. i think the role of alternative data in responsibleconsumer financial innovation is finding more ways to responsibly say yes. and whether it's to open access to folks whohave credit-thin files or credit-invisible files, or to just make a more informed decisionon a standard credit file.

what i think alternative data does—and ithink, francis, you alluded to this as well—is to understand the nuances that exist in afinancial situation that inherently exists for all consumers, and as we see, for smallbusinesses. and that's what alternative data and someof the other sort of buzzwords you hear around these types of things, like machine learningand random forest algorithms, et cetera, i think what it really does, and sort of oneof the sort of comments we always make is it gives you sort of information about notjust the data points themselves, but what's going on between those data points and understandingthe nuances of those financial situations. as we all know, credit decisions aren't blackand white, and i think what the alternative

data opportunity lies is around sort of helpingreveal some of that gray and being able to penetrate some of those underserved and underrepresentedareas. in terms of from key factors to ensure responsibleuse—so i'll sort of start top down—i think, michael, you mentioned a lot of the stuffaround accuracy and veracity of the data furnishers, i think we and sort of, aaron, as you mentioned,one of the pieces that we're responsible for, as a financial institution or financial serviceprovider, is really being rigorous in our diligence before even using a single datapoint in a credit decision. it's very easy for us to onboard a data sourceand start pulling that information, like you mentioned.

you'll see marketing campaigns all over theplace saying, "we're doing this, we're doing that." that part is the easy part. the developing and researching takes millionsupon millions of data points and thousands of different model builds before you see aneven single feature used in that. so it's really around just driving more diligenceand rigorous analysis in that what is still a murky process. but i think doing those things together justreally—and the last piece being just engaging the customer in the process itself.

one of the big opportunities we see is thatthis does give more control back to the consumer or the end customer as it's involved in thatlending process. so if we can get all of those things together,i think it drives again a more informed and a better lending experience. gail. chi chi wu, my question is for you. how should companies using alternative datatake into account fair lending considerations? thank you, gail, for that question, and it'sa very important one obviously. if alternative data is being used to underwritecredit decisions, it is subject to the ecoa,

and as you know, the ecoa doesn't just prohibitintentional discrimination, it prohibits disparate impact, and it doesn't just apply to banksor financial institutions, it applies to all lenders and even some forms of small businesscredit. and the disparate impact is whether a policyhas or creates disparities for a protected class—race, gender, national origin, etcetera. one of the important things to note is thatcredit scoring itself actually has a disparate impact. there are many, many, many studies that showthat as a group, certain minority groups have lower credit scores, and the reason shouldn'tbe surprising, it's what amanda talked about.

credit scores are a reflection and a measurement,and one of the things they measure is the disparities in economic health of minoritycommunities, which has been impacted by 400 years of slavery and jim crow and redliningand legalized economic discrimination, and that has an impact on these communities. that's why you have the racial wealth gapwhere african americans have 7 cents on the dollar in assets to white families, and thelatinos have 8 cents on the dollar. and that makes a big difference in payingyour bills because if you have a financial crisis, someone with $100,000 in financialassets, which is your average white family, will be able to overcome it and pay theirbills a lot better than someone with $7,000

in assets. so credit scores—and so the thing is creditscores aren't the only thing that is going to reflect those historical inequities, alot of data sources will reflect that. if you have trouble paying your credit card,you're going to have trouble paying your cell phone bill. and there's this concept amanda alluded to,structural racism, where the very institutions and systems in our society carry forward thatracism, not because of any animus, although unfortunately i've seen a lot of that recently,not because of any animus, but because systems replicate themselves, and so it will get reflectedin the data.

so is this hopeless? no. i mean, credit scoring is legally used forcredit, and that's because the ecoa test is a three-part test. and the second part is whether there is alegitimate business justification for a policy that creates a disparate impact. and for credit scoring, the justificationis it's proven to be predictive, it's empirically sound, statistically derived. there's actually a test in regulation b. andso any alternative data source is going to

have to pass that same sort of rigorous levelof being proven to be predictive and empirically derived and statistically sound. and by the way, there is a third part. is there a less discriminatory alternative?which we don't really focus on as much, and there should be more focus. is there a way to get rid of this disparateimpact? and really that's the brass ring here foralternative data and any data. can you find the data source that doesn'thave this structural racism baked in? that's i think what we should be looking for.

i think, director cordray, you had a greatpoint talking about looking at data that's forward looking and not so much a measurementof the past. i think that, because the past is infusedwith this historical discrimination. so let me ask one final question for all thepanelists. this is really a summary kind of wrap-up kindof question, and ask you for any additional thoughts you want to add as to some of thebenefits and the risks that using alternative data has in the credit decision process. and we'll just go in reverse order from whenwe started, so we'll start with you, francis, and go down the line this way.

just to sum up, i think what i would say isthat the possibility of alternative data should be looked into two different areas, the kindof traditional pieces, the way aaron described it, and the more innovative pieces. on the more traditional pieces, we have tohave a better understanding of what the long-term impacts are going to be in fair lending andother areas before we'll use it. we have obligations under the law, and wewant to follow those obligations. that competes with this idea that we wantto serve more people. we want to get the unbanked into the system,and those are at tension, and we have resolve that tension together if we're going to makethis worthwhile.

45 million people is both a problem for oursociety, that we're not serving them, and it's also an opportunity that we can go outand help those people get the financial services products that we need. we have to overcome that struggle, though. yeah, i would sort of echo a lot of the samepoints. and i'll go back to i still think at its simplest,it's being able to provide consumers and other end customers with a voice that's in the lendingprocess that traditionally has not existed there before. from a risk side, again, as i mentioned, justensuring the rigorous analysis and diligence

that's needed when onboarding a new data sourceor continuing to assess and regulate existing data sources that are in the market now. michael. benefits i think have been well stated bymy colleagues here. i won't reiterate those. i think one of the risks that we need to bevigilant about, beyond the ones that have been brought forward already, is this ideaof really understanding the long-term impacts of leveraging new alternative data in anydecision. i mentioned earlier in my comments that wehave the privilege of access to the nctue

database at equifax. we have had that data and have been modelingthat data for over 10 years, and really only in the last couple of years moved that datainto solutions that are in the marketplace, in part, because we want to understand thoselonger term impacts. we want to understand how a consumer who ispresent in the nctue data file becomes present in the credit file, and understanding thatconsumer journey and being able to facilitate so that's one risk i think we need to be vigilantabout, is understanding longer term impacts because, as nipun said, getting the data andthrowing it into an algorithm is easy, we all have great it departments that can crunchthe numbers for us—right?—but it's really

understanding the impacts there. and we, as an ncra, have sort of a dual obligationbecause we need to be able to educate our end customers, financial institutions, andothers on appropriate ways to use the data, and, of course, we have our own fcra requirements. amanda. so the cfpb should be commended for its tworeports in the credit marketplace, and one, which has been referenced here today via thedirector's remarks and by some of us on the panel, on credit invisibles. and if i could, i'll just read a line fromthat report, which also the director cited

as well in his opening remarks. "in 2015, we published a report finding that26 million americans are credit invisible. this figure indicates that 1 in every 10 adultsdoes not have any credit history with one of the three nationwide credit reporting companies. the report also found that black and hispanicconsumers and consumers in low-income neighborhoods are more likely to have no credit historyor not enough current credit history to produce a credit score." clearly, the primary benefit of alternativedata, if works as promised, would be to raise credit scores of credit invisibles and openup a financial opportunity, but if alternative

data is implemented unfairly or inappropriately,it could harm the consumer. aaron? so i just wanted to pick up and highlighta few threads of conversation in closing. the first is what chi chi just said abouteven today's credit scoring system kind of baking in uncomfortable gaps in wealth andprivilege in our society. i think that's a really important place tostart that's tolerable as a matter of policy for a lot of the reasons you mentioned, buti think it's just important to keep in mind that that's the foundation we're buildingfrom. and even if you just take the easier datasetslike bill repayment behavior that we understand

and throw it into the mix, that isn't necessarilyimmediately going to help the 45 million people that don't have good visibility in the system. and so i think we need to be really thoughtfulabout how we do that. again, the touchstone is, what helps the peoplewe're trying to help? and this is a situation in which i think somecareful additions of new data could be really useful, but it's certainly not the case thatmore is better. more data doesn't equal more innovation ina helpful way. like i said, there's a whole spectrum of stuffthat people are talking about that's just all smoke and no fire.

anytime someone talks about social media dataor web browsing behavior or your online shopping behavior going in the credit scores, pleasehold that separately, that's a whole separate world. i think it's an interesting question of, ifwe bring new data into the formal credit reporting infrastructure, whether or not it's possiblefor there to be an industry standard of holding that data separate for credit decisions. i think that there's been a lot of talk abouthow all of this data is studied really, really carefully before it's applied to these decisions. i think that if we're going to bring new datainto the conversation, it makes sense to say

let's start by using this for credit decisions,and maybe we're not ready for employment, rental applications, and insurance yet, andi think that would provide some comfort to at least me personally. and the final point i want to make is therehas been discussion about making algorithms transparent and being able to audit algorithms. the easiest way to get your arms around whatan automated decision-making system is doing is understanding the data that's going intoit and what you're trying to measure. and there does become a point where the complexitygets so high or the data becomes so tangential that it becomes really, really hard to askrational questions of that system, which is

i think another reason to just take this slowone step at a time. okay. chi chi, you get the last word. oh, cool. thank you, david. so i want to actually take a step back fromthe question. you know, the question is risks and benefitsto credit decisions, and i want to talk about the credit itself because i think it's reallyimportant. we have been talking about expanding accessto credit without qualifying what kind of

and it's i think really important to rememberwe want the credit to be affordable. some of the fintech lenders, not anybody atthis table, but some of the ones we've seen have triple digit aprs, and really i'm notsure that's going to benefit anyone. and also even affordable credit, you wantto make sure the consumer can afford that let's go back in the "way back" machine, 10,15 years ago, or early 2000s, mid-2000s, there was lots of access to credit. people could get credit without stating theirincome or their assets. and when we, consumer advocates, said, "hey,this is not a good thing," we were told we were against the democratization of credit,and how dare we be against democracy itself?

well, we know how that story ended, right? so the touchstone for credit should alwaysbe ability to repay and creating alternative data to create a credit score, whether it'sa traditional alternative credit score, that's only one part of the equation. the other is the consumer has to be able toafford that credit. and one of the interesting things about someof the data sources we were talking about is it might be able to incorporate that. director cordray, you mentioned bank accountinformation, and that incorporates both payment data, but also income data, so there mightbe some—and nipun goel mentioned the cash

flow. cash flow obviously is important to repaycredit obligations. now, that's why it's great that the bureauhas taken this interest in letting consumers use third-party aggregators, because i'm notsure it's a great idea always to have lenders looking directly at the transaction leveldata because there could be some privacy concerns over there. so having some sort of intermediary to protectthe privacy of consumers, also to give them control so they can opt in and decide to dothis or not to do this is important. and, finally, i think if we're going to usethings like bank account data, we really do

have to deal with the overdraft issue. but i think that there is a potential there,and certainly i think having 6 months or a year of bank account data is probably moreuseful information than a 3-year-old collection account for a medical debt where you disputedwith the provider over the copay. so this concludes the panel portion of ourprogram. and i'm going to ask you to join me in thankingall of our panelists for a thoughtful discussion. and let me now invite the panelists to retaketheir seats and turn the program back over to zixta martinez, our associate directorfor external affairs, who will moderate the next portion of the field hearing.

and again thank you to the panel of experts. an important part of how the bureau helpsconsumer finance markets work is to hear directly from consumers, from industry, from stateand local partners, and, of course, from community advocates across the u.s. one of the ways that we gather public feedbackis through events like these. we've held field hearings, town halls, andother public events across the u.s. from miami, florida, to itta bena, mississippi, to seattle,washington, and at these events, we not only hear from experts in the field, we also invitethe public to participate. but before i open the floor up for publiccomments, i want to remind folks that there

are several other ways to communicate yourobservations, your concerns, or complaints to the cfpb. you can submit a consumer complaint with thecfpb through our website at consumerfinance.gov. our website will walk you through that process. or you can call 1‑855‑411-2372. we take complaints about mortgages, car loansor leases, payday loans, student loans, or other consumer loans. we also take complaints about credit cards,prepaid cards, credit reporting, debt collections, money transfers, bank accounts and services,and other financial services.

if you don't have a specific complaint butwould like to share your story with us, we have a feature on our website called tellyour story, where you can tell us your story, good or bad, about your experience with consumerfinancial products or services. your story will help inform the work thatwe do to protect consumers and create a fair marketplace. we have another feature called ask cfpb, whereyou can find answers to over 1,000 frequently asked questions about consumer financial issuesas well as additional resources. we also have a spanish language website calledcfpb en espaã±ol, where you can find answers to consumers' frequently asked questions andadditional consumer resources.

so i encourage you to visit consumerfinance.govto learn more about the resources and tools the bureau has developed to help consumersmake the best decisions for themselves and for their families. so now it's time to hear from members of thepublic that are here today. a number of you signed up to share commentsand observations about today's discussion. the public comment portion of the field hearingis also an important opportunity for the bureau to hear about what's happening in consumerfinance markets in your community. what we hear from you is invaluable. we would like everyone who signed up to beable to speak, so we encourage you to take

about 2 minutes to share your thoughts andobservations with us. and i will call our first public commenter,and that is jonathan marshall. peter will bring you a microphone. so my name is jonathan marshall. i'm a consumer rights lawyer here in westvirginia, in charleston. and i found this discussion pretty interesting. kind of my role over the last couple years,at least here, has been down at the legislature. we've faced some pretty unprecedented attackson existing debt collection regulations that have been in place for many, many, many yearshere.

and over the last couple years, we've beenable to work with industry, work with banks, to come to reasonable compromises. but unfortunately again this year folks areback again and asking for more. as i see what the cfpb is trying to do withrespect to alternative credit, i understand the need for the availability of credit, andthat's important, but i think that that needs to be balanced with existing both federaland state debt collection protections. i think the last panelist or the last commenter,commentator, here talked about it's not just about alternative data, and you have to lookat those three c's, right? it's capacity, too, right?

it's character and it's collateral. and i would hope that the cfpb, as it movesforward in this area, does keep in mind those important consumer protections that exist,both at a federal level and at a state level. thank you, mr. marshall. sam vallandingham? all right. patrick walker? just a few comments. i would like to underscore the need for alternativedata and new solutions.

it was alluded to a little bit, but i thinkthat the numbers are stark. from the cfpb's own data point on credit invisibility,while the overall rate of unscorability was 19 percent nationwide, in the lowest incomecensus tracts, it's 45 percent. so it's not kind of a small or minor problemin some parts of the country, it's actually a very large issue. and so i would just like to point that outto note that the status quo has some very important gaps that we definitely need newsolutions. secondly, perc has done some of our own researchwhere we looked at the lowest income census tracts, and what we find is that when youdo add in the alternative data, that the 30,

35, 40 percent rates of unscorability fallsgreatly. depending on what score you use and what solutions,it can fall as low as a few percentage points. and those individuals are not just cominginto the system with subprime scores, they're coming in, in many cases, with prime scores,620s or above. i would also like to comment on one of thenotes that we need to look at the longer term impacts of this data, and, of course, it iscompletely the case that we need to be prudent with new data and new solutions. i would like to point out, though, that therehave been a number of utilities that have been reporting to the cras for decades now,so the data is out there.

we don't need to have an experiment that willgo into the future for many, many years and then look at the outcomes. we actually have individuals that were newto the credit system with alternative data back in the late '90s, in the early 2000s. so you could actually look at those individuals. perc has done a little bit of that work andwe didn't find any decrease in credit scores after individuals access credit from alternativedata; in fact, we found score increases over time similar to a control group. so if that is an area of concern, that neednot hold up the transition to new solutions.

we can look at data that's already been reportedfor years. thank you, mr. walker. chris arthur? first of all, i want to thank everyone fortaking the time to come to the great state of west virginia. my name is chris arthur. i'm general counsel for the west virginiadivision of financial institutions. i see benefits and i also see concerns regardingthe use of alternative data. in particular, i'm worried about discriminatorypractices and how massive this information

may be. i even think something like utility—notutility bills, but cable bills or telephone bills is something very concerning to me becausea lot of people look at that as it's not a must, it's a luxury, and i know people thatlive close to me that they may not pay their phone bill because they would rather use thatmoney to do something for their children, but the rest of their credit, they pay theirrent, they pay their other bills, but that's a decision. they may do the exact same thing with a cablebill, they may have something that they find is more important, so they'll miss their cable.

and i could see that being more of a detrimentand a negative to people who are really trying to build a good credit. the other thing, let's face it, technologyis really moving fast, and the amount of data that you can get, especially like facebookor social media, i can see a lot of discriminatory practices. so i think we have to weigh this and limitits use and make sure that we make good decisions when alternative data is useful to build creditversus alternative data that may be used in a very negative way, including a discriminatorypractice. the state of west virginia is very poor, andthere are a lot of poor people, and a lot

of poor people who have worked very hard,but, like i mentioned, they make decisions based on what's in the best interest of theirchildren or their family, and some of the things that were discussed today may be adetriment to them building credit. but, again, thank you for your time. thank you, mr. arthur. bren pomponio? i wanted to first express our appreciationfor the work that the bureau has done in protecting i work at a nonprofit legal service organization,and many of our clients have seen the benefits of the bureau's actions, whether it be consentdecrees that give them new rights and enforcement

actions that show tangible benefits to themin terms of returning money. and so we're thankful for the work that thebureau does. my comments on the alternative data issuecome from representing low-income west virginia consumers for more than 15 years in a varietyof contexts. and west virginia has the highest incidenceof homeownership in the country, but it's also one of the lowest states in terms ofeconomic and poverty. and so at this intersection of high homeownershipincidence and low socioeconomic incidence is a fertile ground for predatory lendersin the past. and i would like to hope that when consideringthe alternative data, that we make sure that

the safeguards are in place that doesn't allowin alternative facts. i'm thinking about going back to before 2008,the no doc loans where a lot of west virginians' income was falsely inflated because therewasn't sufficient safeguards in the underwriting process to ensure that the information thatwas being used to make the credit decision was accurate. and this hurt people because it put them intoloans that were secured by their home, converted unsecured credit to secured credit, in whichthey couldn't pay because their income source was not sufficient to cover the monthly payments. so i would just ask that the accuracy of thatdata be considered when the underwriting decisions

are made. thank you, mr. pomponio. margot saunders? hi. as you know, i'm margot saunders, with thenational consumer law center. i have seen a number of instances where thepromise of the use of alternative data, particularly rent payments, through one of these new rentreporting agencies for rent has been used as the premise for pulling people into verydangerous predatory credit transactions. and i think we've seen it also with some paydayloans.

so i would just urge you to, while you investigatethe viability of using alternative data to boost credit scores, that you ensure thatyou don't allow this vehicle of using alternative data to improve credit scores to be the meansby which people are pulled into dangerous thank you, ms. saunders. linda frame? good afternoon. my name is linda frame, and i work for thewest virginia center on budget and policy. we research economic policies to determinewhat can be done at both the state and federal level to give regular people in our communitiesa shot at a decent job that earns a decent

wage and allows for a decent quality of life. i would like to thank the cfpb for visitingcharleston today. and we are here to express our appreciationfor its hard work and also the hard work of the attorney general's office here to protectour families, in particular, from payday lenders. today's field hearing is a great explanationand exploration of how to expand credit opportunities for people. protecting those who are credit invisible,however, from payday lending is also a very important component of the cfpb's work. my organization is working with states acrossthe nation where payday lending is illegal,

as it is here in west virginia. last year, director cordray may recall, weall gathered in d.c. to present him with a whole pile of cards, postcards, signatures,petitions, from all of our states where we do not have payday lending. all total, there are 90 million people inthe united states who live in states where we do not have payday lending, and we hopethat this can grow. experience from our states—well, the statesthat do have payday lending—has shown that allowing payday lenders to do whatever theywant does not benefit people. in fact, according to the center for responsiblelending, keeping payday lenders out of west

virginia saves our residents $48 million everyyear in payday lending fees. this has helped our families from fallinginto the debt trap caused by payday loans. so thank you for allowing me to stray a littlebit off topic to thank you all for coming to charleston, and we hope you will continueyour good work and help us preserve west virginia's strong tradition of consumer protection andbanning payday lending here. thank you, ms. frame. and thank you to all that provided thoughtfulpublic testimony today. thank you to the audience, to the panelists,and to all those watching via livestream at consumerfinance.gov.

this concludes the cfpb's field hearing incharleston, west virginia. have a great afternoon.

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