Alternative data and financial inclusion (Summer 2017)

 

Table of Contents

 

Data mining to expand access to credit

By Ruth Susswein

One in ten Americans is “credit invisible,” according to the Consumer Financial Protection Bureau (CFPB), because they have either old, limited or no credit history. For the 26 million U.S. consumers without a credit file or credit score, that translates to little, if any, access to affordable loans and credit cards.

FICO, the credit scoring giant, estimates that when you include those consumers with “inactive” credit—meaning old payment histories and too little new data—the figure jumps to more than 50 million people who are deemed “unscorable.” That’s a lot of people with no access to credit.

Lenders traditionally have relied on credit history and associated credit scores to make lending decisions. A traditional credit score includes credit bureau data about your credit card, mortgage, auto or other personal loan payment history. Now lenders have begun to turn to alternative sources of information to evaluate a consumer’s creditworthiness, with the goal of expanding people’s access to credit.

While not well-defined, alternative data includes payments not found on a traditional credit report, behavioral information, or trends in how you handle your finances. Alternative data is used to verify identity, interpret individuals’ credit risk and judge a person’s character to reduce default and fraud.

These alternative data sources include rent, cable, cellphone and gas or electric payment histories, typically not on a credit report, but which can prove a record of timely, regular payments. Other alternative data, such as occupational licenses, bankruptcies, collections activity, homeownership status and property values, are drawn from public records.

For someone who consistently makes rent and utility payments on time, inclusion of alternative data could improve the chances of receiving a loan. LexisNexis estimates that when alternative data is included in credit evaluations, 15 to 20 million additional consumers could have access to auto loans, credit cards and other bank products.

However, some alternative data sources are far more controversial, such as one’s social media habits, online purchases, payday loan usage, bank account balances, and marketing and collections data from text messages.

Consumer Action surveyed consumers online, asking whether it’s fair to use certain forms of alternative data to gauge creditworthiness. Respondents were nearly unanimous (96%) in opposing the use of social media data to assess credit risk. (Survey: Don’t use ‘behavioral data’ to assess credit risk.)

“Only financial data is acceptable to use, i.e., rent or insurance payments, subscriptions, utilities, etc.—anyplace one would be expected to uphold their end of a contract of sorts. How one uses media, social media and/or one’s political views or activities are NOT applicable to one’s creditworthiness. If it isn’t directly related to how you handle your financial obligations, it has NO place in your credit score,” said one respondent.

Fintech loans

Some financial technology (fintech) firms are profiling potential customers with bits of behavioral data, such as how often a consumer texts or what she purchases online. Financial technology firms offer consumers such products and services as online loans, mobile payments and account tracking services. Some fintechs evaluate loan applicants, in part, with consumer-supplied data, such as the time of day that someone applies for a loan online. The consumer who applies at 3 a.m. is assessed differently than the one who applies midday.

Online lender Lenddo relies on alternative data that reportedly uses 12,000 variables in its algorithm to evaluate a borrower for a loan.

One lender offering small ($500) credit lines has studied the use of payday loan data and concludes that lenders can judge credit risk based on how these high-cost loans were repaid and if they were rolled over into new loans.

Are some alternative data sources more valuable or more predictive of future payment behavior than others? The answer is still unknown, because there is no proof that any behavioral data is relevant for consumers or creditors.

Yet some companies with alternative scoring models contend that they can safely tease out those with no record or limited (“thin”) credit files who are “good” credit risks.

Credit scoring leader FICO’s research shows that when the “right” alternative data is used, such as recent positive and negative telecom, rent and cellphone payment history, millions of “unscorable” consumers can be scored and be eligible for credit lines. This was particularly true of those consumers who were new to credit.

More than one-third of the newly scorable had high enough credit scores to gain access to credit. Also encouraging was that those who were credit eligible had improved their credit scores within the next 24 months, making them reliable in the eyes of creditors.

VantageScore’s new 4.0 model relies on “trended data” that tracks payment behavior over time to expand credit access to new applicants, but the company strongly opposes the use of social media details to assess consumers. Both VantageScore and FICO insist that alternative data used to evaluate creditworthiness should comply with current Fair Credit Reporting Act (FCRA) regulations, which give consumers the right to contest inaccurate information and to receive a written explanation when credit is denied.

While including non-credit-related information may open up borrowing opportunities for millions, it could also lead to discriminatory lending decisions if conclusions are drawn based on limited, subjective information. It is unclear how reliable non-financial data is. If lenders incorrectly interpret alternative data, consumers could be unfairly denied credit and not even know why because fair credit reporting laws may not apply to non-financial information.

Alternative data: Helpful or harmful?

By Monica Steinisch

Whether the use of alternative data in calculating credit scores is likely to help or hinder your access to credit will depend on the information being used, your ability to consent to its use, and the way that creditors interpret and apply the data. This alternative data could include everything from address history to payment activity for rent, utilities, cable TV, insurance and other non-credit expenses.

Balancing act

Proponents of the use of alternative data in credit scoring argue that it offers an opportunity to level the playing field for consumers who do not have a mortgage or other credit and loan accounts traditionally recorded in a credit report. For example, while both homeowners’ and renters’ scores can suffer when they miss payments, only homeowners’ scores have routinely reflected positive payment history. Likewise, the consumer with credit cards and loans has had a credit score advantage over the consumer without them, even though the creditless consumer might pay all monthly bills on time and carry less debt.

When credit scores don’t reflect timely rental and household bill payments or a long tenure in a single job or home, they can present an incomplete and undeservedly negative appraisal of a consumer.

Alternative data advocates argue that this is particularly problematic for low- and moderate-income consumers, who may not have other opportunities to demonstrate a pattern of responsible behavior and build a positive credit history.

The non-profit Credit Builders Alliance (CBA) tested the effectiveness of rent payment reporting to help low-income housing residents improve their credit scores. In its pilot project (PDF), the CBA found that nearly 80 percent of participants experienced an increase in credit scores, by an average of 23 points, due to rent reporting, and 100 percent of those who had no score before the project ended up with a credit score in the upper subprime or prime tiers. Conversely, the National Consumer Law Center (NCLC) has spoken out about the potential pitfalls (PDF) of using some alternative data to assess creditworthiness.

Specifically, NCLC is concerned about how utility payment history could harm, rather than help, struggling households. Breaks designed to help struggling consumers would come at the cost of their credit records if utility payment history were a required factor in credit scoring. NCLC argues that in states that prohibit companies from shutting off the heat in winter months for non-payment, households that delay payment to cover rent and other bills would end up with tarnished credit scores, defeating the benefit of using alternative data. In states where tenants are allowed to withhold rent until required repairs have been made, NCLC worries that renters would be reluctant to exercise their rights for fear of the consequences to their credit reports.

A question of choice

While NCLC opposes mandatory full-file (providing positive and negative data) utility credit reporting, the organization supports voluntary, opt-in reporting of utility data.

In April, Consumer Action conducted a survey about the use of alternative data to widen the availability of credit to those with little or no credit history. Nearly 95 percent of respondents thought that companies should be required to obtain consumers’ permission before accessing such information.

Companies like PRBC occupy the middle ground that some see as ideal. PRBC (Payment Reporting Builds Credit) allows consumers to set up a free online account, link the payment accounts they want to include, and share the resulting score with only those they choose.

Limited data, limited use

FICO, developer of the most widely used credit score, updated its formula last year to factor in utility, telecom and rent payment history, when such data is reported. The latest score—FICO 9—was designed to give lenders more information to better assess those with little or no traditional credit history.

In reality, few landlords report rental data to credit bureaus today (although many do check applicants using specialty credit bureaus where information about problem tenants can be reported by landlords). About 50 percent of all rental units are mom-and-pop-owned, and don’t furnish rental data. FICO estimates that less than 1 percent of credit files at the three largest traditional credit reporting bureaus currently contain rental entries.

What’s more, even if you sign up for a rent reporting service like RentTrack, most creditors are not yet using the FICO 9 score that includes rent payments. (For details on rent tracking services, see NerdWallet’s report.) Rent payment history may hold promise for access to credit—in the future.

Utility payment history is similarly limited. Of the nearly 200 million credit files that exist, only 4 to 6 percent contain utility data, and most of what is reported is likely to be delinquent payments rather than timely ones, for now.

FICO’s XD scoring system helps to score “credit invisibles” by including even more types of alternative data, such as cellphone and cable payments. (For details, see Credit scores for those without traditional credit.)

Risks

Some lenders automate part of the credit approval process to save time and reduce costs. Critics caution that incorporating certain types of alternative data into an automated credit analysis could lead to unintentional discrimination. The concern is that the use of new, non-traditional factors might paint an inaccurate, negative picture of a segment of the population—for example, characterizing military families whose duty requires them to change residences frequently as being unstable—or unintentionally discriminate based on race or ethnicity by using ZIP code or similar types of data.

There’s also a concern about the ability of consumers to identify and correct inaccuracies when alternative data is part of the automated decision-making process. Credit reports already are notorious for containing errors. Alternative data may be more likely to contain errors if the standards for accuracy are more lax than those for traditional data. It may also be more difficult for consumers to identify and correct those errors since they may not know that certain non-financial data is being used to evaluate them.

Innovations from financial technology (fintech) companies could help to expand access to credit, but could also increase risks for consumers, at least in the early stages, since fintechs generally aren’t constrained by the same rules as credit bureaus and “furnishers” (those who report customer payment history to bureaus).

The Consumer Financial Protection Bureau (CFPB) has launched an inquiry into ways to expand access to credit for consumers who lack the credit history needed to obtain a credit score. (See Watchdog weighs risks, benefits of data use.)

Survey: Don’t use ‘behavioral data’ to assess credit risk

96 percent of consumers polled think it’s unfair to evaluate based on social media habits

By Alegra Howard

Consumer Action conducted an online survey, in April, that found that respondents are wary of using data sources unrelated to one’s credit or payment history to gauge creditworthiness—even as a means to widen the availability of credit to those with little or no credit record.

Ninety-six percent of respondents balked when asked if social media usage or video and TV viewing should be used to evaluate credit applicants. One respondent spoke for many in her comment: “I don’t think sources that can infringe on someone’s privacy should be used. It’s no one’s business but my own how much TV I watch, how often I use social media or what I do on my cell phone.”

However, the vast majority (82%) of the 2,410 people who took the poll thought rent payment history and utility payment history (electricity, gas or other fuel) were okay to use to assess credit risk.

“Anything that reflects an actual payment history should be used. Social media? TV viewing habits? Nothing at all to do with bill-paying habits,” said a survey participant.

The eight alternative data choices available in the survey included public records (such as property ownership and lawsuits), cable or satellite TV payment history, utility payment history, rent payment history, cellphone data (browsing history and data streaming), television viewing or video watching history, and social media usage (data about your posts, friends, mentions, etc.).

“Alternative data could give false impressions, not unlike judging a book by its cover,” commented one respondent. “Payment history is relevant to risk, alternative data is not.”

Just over half (55%) of respondents agreed that public records data (property ownership, lawsuits, etc.) could be used to evaluate people for access to credit.

Nearly all respondents (95%) agreed that companies should be required to ask permission before accessing behavioral data, putting a high premium on privacy and the right to control data collected about them.

When asked if they thought it was acceptable for credit applicants to have to pay extra (higher interest rate, deposit or upfront fee) if the use of alternative data deemed them a greater credit risk, nearly 77 percent said no. A similar penalty-pricing model can be found in auto insurance quotes—several respondents freely mentioned that they had been quoted higher insurance rates because of bad credit, and they objected because credit is unrelated to their driving history.

Another concern consumers voiced repeatedly, in the optional comment field, related to inaccuracies found in traditional credit reports. (A 2012 study by the Federal Trade Commission found that one in five consumers had an error in at least one of their three credit reports.) Many worried that the use of alternative data might further compound inaccuracies due to errors and disputes in their utility and cellphone bills, negatively impacting their credit profiles.

The survey (PDF) was conducted online and received 2,410 responses between April 6 and April 21, 2017.

Behavioral data may affect access to credit

By Alegra Howard

Traditional lenders in the U.S. have shied away from using some of the more controversial alternative data sources, like educational attainment and social media profiles, to score consumers. But these data points are being studied to examine their potential in gauging credit risk.

Financial technology (fintech) firms seeking to capture a bigger slice of the traditional U.S. lending market are using unconventional data in their predictive consumer credit models. Lenders in emerging markets in Africa, Asia and Central America use alternative data sources to provide financial services to millions of people who don’t have traditional credit histories.

In some cases, consumers in developing regions around the world are sacrificing their privacy to obtain loans they might not otherwise qualify for. Here are some of the data points being used and studied and what they may help decision makers glean about consumers:

Education level: Your highest level of education and the schools you attended can be used to assess credit risk. Why? Those with advanced degrees, or those who attended Ivy League schools, will likely have higher earning potential than high school grads or those who attended community college.

A Federal Reserve Bank of Boston study showed that consumers without a college degree tend to have lower credit scores. The study found that one additional year of schooling raises an individual’s credit score by eight points and reduces the probability of bankruptcy by 3.3 percent.

Student loan debt also was considered. A 2015 report by private student loan company Earnest showed that graduates with the highest levels of student loan debt, associated with advanced degrees in the medical or legal fields, had substantially lower credit scores than borrowers with medium-sized student loan debts.

Cellphones: Mobile phone usage, the time of day you make calls, how long you talk, your texting frequency, text length and even the model of your phone are data points that could help determine your credit risk.

Fintechs like Branch and Tala factor in the apps you download, Wi-Fi networks you use and your mobile wallet balances to help determine if they’ll give you a loan. Microlender Tala, offering credit in emerging markets in Africa, Asia and Central America, states that consumers who organize more than 40 percent of their cellphone contacts by both first and last name are more likely to be “good borrowers.”

The grammar or punctuation you use in a text message may deem you more creditworthy. Fintech lender Lenddo, operating in 20 developing nations, claims your battery level is useful in determining your likelihood of defaulting on a loan. If your battery’s always empty, they surmise you are less likely to plan ahead—a potential sign that you’ll miss credit payments.

Social media usage: While Facebook limited the amount of personal information it makes available to third parties last year, potential employers and landlords in the U.S. and banks in developing nations may ask your permission to search your social media profiles.

Microlenders operating in emerging markets argue that whom you’re friends with might suggest how trustworthy you are. Peer-to-peer lenders in China are giving bonus points to users with “celebrity friends.”

Conversely, lenders in developing nations considering consumers for a credit card or loan might identify which friends and followers are already bank customers. The bank knows the payment history and credit stability of its customers and might judge you by association. If your social network is filled with friends with bad credit, companies that use such data could label you a credit risk.

Keyword searches from your online activities also could be evaluated.

To date, we’ve seen no evidence that conventional lenders in the U.S. are using these unconventional methods of measuring credit risk.

However, U.S. credit analysts and credit card companies are studying the value of behavioral information in predicting a consumer’s ability to repay debt.

If such information were ever used to vet customers, current financial regulations, such as the Fair Credit Reporting Act, would need an overhaul to ensure that they address accuracy and fairness for all types of loans.

Credit scores for those without traditional credit

By Lauren Hall

Regulators, banks and advocates have long pushed for credit scoring companies like FICO to offer “credit invisible” consumers ways to earn a credit score. FICO responded by offering its new FICO Score XD to evaluate these consumers and generate a credit score. The scoring model gauges a consumer’s creditworthiness based on her ability to pay phone, television and utility bills on time.

FICO Score XD incorporates telecom and utility payment data, supplied by credit reporting agency Equifax, and public record and property data from LexisNexis Risk Solutions. (FICO says these data sets comply with consumer protection regulations such as the Fair Credit Reporting Act.)

The XD program gives traditional credit card companies confidence about lending to consumers based on their timely cellphone and other monthly payments. This, in turn, helps credit invisible consumers establish traditional credit reports. Eventually, they could be eligible for a traditional FICO credit score, the scores most lenders use to decide to approve, for example, auto or home loans and how much interest to charge. FICO Score XD will help consumers with little or no credit demonstrate they are worth the risk to lenders.

According to a 2017 Mercator Advisory Group white paper, “In initial FICO Score XD pilots run with several leading U.S. credit card issuers, the addition of alternative data from several other sources improved the scorable rate for traditionally unscorable segments from 40 to 80 percent, with the biggest gains coming in…new credit candidates and consumers with no traditional credit file.”

More than one-third of newly scored individuals in the FICO Score XD program (accounting for millions of consumers) scored at 620 or above—high enough to gain access to credit. Further research revealed that most “credit invisible” people with an XD score above 620 who then open traditional credit accounts go on to maintain good credit and achieve high standard FICO scores as well.

Credit bureau TransUnion offers its own scoring model similar to FICO’s XD, called CreditVision Link. This model combines traditional credit scoring data with non-credit-related data, like consumers’ banking histories and property, deed and tax records. TransUnion says that CreditVision Link can help consumers “be assessed as lower credit risks, receive more beneficial pricing or possibly switch from a decline to an approval.”

When TransUnion tested its CreditVision Link with an auto lender, the scoring model “identified up to 24 percent more approvals.”

Watchdog weighs risks, benefits of data use

By Lauren Hall

The Consumer Financial Protection Bureau (CFPB), the federal watchdog for consumer safety in financial products, is keenly interested in encouraging lenders to open access to credit for more consumers.

In the months ahead, the CFPB will be measuring the benefits and risks of using unconventional, non-credit-related sources of data to underwrite loans. Currently, lenders use credit scores that draw on an individual’s history of repaying extensions of credit. The CFPB is hoping that new lending practices using alternative information will help broaden eligibility for affordable credit card, auto and personal loans. The Bureau is encouraging these new ideas through its Project Catalyst program, which gives companies extra leeway to create products and services, particularly if they will help underserved individuals gain access to financial services.

In February, the CFPB held a field hearing in West Virginia that focused on alternative data and how it could help expand access to credit. CFPB Director Richard Cordray outlined the limitations of traditional credit scores and expressed support for the responsible use of non-credit-related information to help the millions of consumers who are “credit invisible.”

Of the 45 million consumers with thin or no credit histories who are underserved by the mainstream credit system, the Bureau says communities of color and people with lower incomes are vastly overrepresented. The CFPB has been hearing from consumer advocacy groups and others who have suggested that underserved consumers could benefit from a “wide array of other data sources beyond traditional credit files.”

At the hearing, advocates, including the National Consumer Law Center and Americans for Financial Reform, joined financial industry representatives to discuss the pros and cons of using various types of non-credit-related data, from cellphone payments to social media posts. Advocates expressed concerns about the value and accuracy of some alternative data sources. The panelists also discussed how government should regulate alternative data collection.

While the CFPB acknowledged that the use of alternative data could benefit some consumers, it also said it recognizes that its use “could make borrowing more difficult for consumers who have strong credit scores but are weighed down by other factors, such as missed utility payments or frequent address changes, that aren’t traditionally found in credit reports.”

The Bureau will study how alternative data is being used and evaluated for underwriting by both traditional banks and online lenders. It also would like to hear from those who work with programs using alternative data, those underwriting loans and pre-screening applicants, and the public who have views on the use of non-traditional data in credit decisions.

The CFPB’s “request for information” is specifically exploring:

  • whether the use of alternative data could increase access to credit;
  • whether the use of alternative data could make credit decisions more complex for both consumers and industry;
  • what effect the use of alternative data might have on the costs of lending and borrowing;
  • how alternative data may affect consumer privacy when personal information is collected, shared and used in credit decisions; and
  • if there are risks to fairness for particular groups (servicemembers, minorities, etc.) when alternative data is used to determine creditworthiness.

VantageScore pries open access with ‘trended credit data’

VantageScore’s latest 4.0 credit scoring model uses “trended credit data” from the Big Three credit bureaus (Experian, Equifax and TransUnion) to detect patterns in borrower behavior that can help lenders better predict risk and broaden lending opportunities. So far, trended data focuses exclusively on credit card balances and payments.The new 4.0 score hits the market this fall.

The new model notes changes in outstanding balances: Are balances trending up or down? By examining the “trajectory” of both borrowing habits and payment patterns, the company believes it can create a better borrower profile. Mortgage finance giant Fannie Mae already uses trended data to help consumers with limited credit histories qualify for their first mortgage.

Making mortgage credit more available

By Ruth Susswein

About five million renters could afford to purchase a home but they lack traditional credit scores, according to mortgage finance giant Fannie Mae.

But millions of creditworthy consumers without traditional credit scores do have payment histories. Regular, on-time rent, utility and cellphone payments could help borrowers qualify for a home loan.

While there is no consensus on a definition for “alternative data,” there is agreement on the value of using non-traditional information, like rent and cellphone payments, to assess a consumer’s ability to repay a mortgage.

Measures used to evaluate consumers for mortgage credit are beginning to expand so that eligibility can increase for a broader group of borrowers.

Fannie Mae and Freddie Mac (the quasi-governmental corporations that purchase mortgages from lenders to keep credit flowing) have been using old-fashioned credit scoring models. Critics say the old models do not accurately predict if people will repay a mortgage. However, there’s been some recent progress.

For those with no credit score, Fannie Mae now allows lenders, in certain circumstances, to qualify borrowers for a mortgage using rent payment history plus another form of non-traditional data, such as a 12-month record of utility, telecom, childcare or insurance payments.

For first-time homebuyers, Fannie Mae’s system also calculates “trended data,” which examines the proportion you pay each month on your credit card bills: Just the minimum, or do you pay in full? Those consumers with a trend toward paying down credit card balances quickly or paying in full every month are considered a less risky bet. This may help some people qualify for a mortgage.

Freddie Mac also allows lenders to qualify consumers using four non-traditional accounts, such as a year’s worth of timely rent and utility payments plus two other accounts, such as regular contributions to a savings account and recurring childcare or insurance payments.

About half a million homebuyers without traditional credit scores are obtaining Federal Housing Administration (FHA) loans each year, according to the Office of the Comptroller of the Currency (OCC). However, FHA loans require borrowers to pay for mortgage insurance, which increases overall costs. (Mortgage insurance protects the bank’s investment but doesn’t insure the homeowner.)

Meanwhile, some lawmakers are trying to require that alternative non-credit-related data be included in mortgage and other lending evaluations.

The Credit Access and Inclusion Act of 2017 (H.R. 435) would require that rent, utility and telecom payments be furnished to credit bureaus to help improve access to credit. The Credit Score Competition Act of 2017 (H.R. 898) was introduced to expand the use of various credit scoring models to increase access to home loans.

New reporting rules could boost credit scores

By Monica Steinisch

Changes in the way credit reporting agencies (CRAs) report certain types of debt are expected to boost the credit scores of millions of consumers by at least a little. Here is how certain debts are, or will soon be, treated thanks to an overhaul of credit reporting practices.

Tax liens, civil judgments

Beginning July 1, CRAs will remove all reported tax liens and civil judgments from credit reports that are missing complete consumer details, which include name, address and Social Security number or birthdate. On top of that, the big three CRAs—Equifax, Experian and TransUnion—will have to remove liens and judgments if public court records aren’t checked for updates at least every 90 days.

The changes come following mounting pressure on credit bureaus to improve the accuracy and quality of their data.

FICO estimates that 12 million U.S. consumers will benefit from the reporting changes. FICO projects that the vast majority of those affected will see a credit score increase of less than 20 points. The credit score boost may make credit more available to those who are close to qualifying.

Medical debt

As part of a settlement with the New York Attorney General’s office, Equifax, Experian and TransUnion agreed to delay reporting medical debts on credit reports for 180 days. The six-month delay allows reasonable time for health insurance claims to be processed and errors or confusion about payment responsibility to be ironed out. Prior to this change in 2016, medical debt could be as little as 30 days delinquent to be reported.

Outstanding medical debts are now also dropped from consumers’ files if a creditor, collection agency or debt buyer reports the debt as paid in full, or as soon as it’s paid by an insurance provider, rather than be stuck on a credit file for the next seven years.

FICO and VantageScore had previously revised their credit scoring formulas to de-emphasize medical debt. FICO says this change could potentially increase scores by up to 25 points.

With roughly half of all debt collection items being for medical bills, according to a Consumer Financial Protection Bureau (CFPB) study, these changes affect tens of millions of consumers.

Fines and tickets

The three major credit bureaus also stopped reporting unpaid parking and traffic tickets, including speed camera citations, court fees and fines, vehicle storage fees, toll road fees, library fines and eviction fees, in 2016. With the change, local governments and counties lost the threat of a damaged credit score as leverage to get people to pay. (However, failure to pay vehicle-related citations can hold up your auto registration renewal.)

Thanks to updates in some scoring models, newer FICO scores ignore collection accounts with balances of less than $100, and VantageScore ignores all paid collections, as well as unpaid collections under $250.

These and other significant changes in credit bureau policies and practices—including an improving dispute resolution process—are largely the result of a 2016 settlement with 31 state attorneys general and greater CFPB oversight of credit reporting agencies. The federal watchdog agency’s attention to credit bureau improvements is partially in response to the thousands of complaints consumers file with the consumer agency against credit bureaus each year.

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Alternative data and financial inclusion (Summer 2017)   (CA_News_alt_data_17.pdf)

 

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