The problem & the opportunity
The World Bank estimates that about 1.7 billion adults remain unbanked globally – without an account at a financial institution or through a mobile money provider. Virtually all these unbanked adults live in the developing world. Indeed, nearly half live in just seven developing economies: Bangladesh, China, India, Indonesia, Mexico, Nigeria, and Pakistan. About 56% of all unbanked adults are women.
The World Bank also found that poorer people account for a disproportionate share of the unbanked – globally, half of the unbanked adults come from the poorest 40% of households within their economy, the other half from the richest 60%. But the pattern varies among economies.
On the bright side, the number of unbanked around the world has been steadily declining. The 2017 Global Findex database shows that 1.2 billion adults have obtained an account since 2011, including 515 million since 2014. Between 2014 and 2017, the share of adults who have an account with a financial institution or through a mobile money service rose globally from 62% to 69%.
One of the problems with extending financial access is the lens through which the formal financial system assesses previously “invisible” groups of the global population. Lenses vary in different countries, but one is constant – existing frameworks have not been able to effectively expand the addressable market. Financial technologies, however, have a role to play in changing the very framework.
The progress in extending access to the formal financial system to previously “invisible” groups of the global population is largely attributed to digital financial technologies. Experts believe that smartphones can dramatically reduce the cost of lending because the apps they run generate huge amounts of data – texts, emails, GPS coordinates, social-media posts, retail receipts, and so on – indicating thousands of subtle patterns of behavior that correlate with repayment or default.
The World Bank emphasized that the global spread of mobile phones has facilitated expanding access to financial services to hard-to-reach populations and small businesses at low cost and risk:
Digital IDs make it easier than ever before to open an account
Digitization of cash-payments is introducing more people to transaction accounts
Mobile-based financial services bring convenient access even to remote areas
Greater availability of customer data allows providers to design digital financial products that better fit the needs of unbanked individuals
“Financial access facilitates day-to-day living and helps families and businesses plan for everything from long-term goals to unexpected emergencies. As account holders, people are more likely to use other financial services, such as credit & insurance, to start & expand businesses, invest in education or health, manage risk, and weather financial shocks, which can improve the overall quality of their lives.”– The World Bank
The last point in the list of benefits – the greater availability of consumer data – is particularly important. The hallmarks of one’s lifestyle imprinted in continuous data flow are increasingly becoming vital in innovative ways to assess how trustworthy one is. To trust someone with money in the form of credit, or other financial service(s), financial institutions are required to perform an assessment of one’s history with money. Unable to do so for a large group of the global population using existing frameworks leads to a perpetuated exclusion of potentially mutually beneficial relationships with previously “invisible” individuals, not speaking about unbreakable barriers for building personal prosperity for those individuals.
“A limited credit history can create real barriers for consumers looking to access the credit that is often so essential to meaningful opportunity – to get an education, start a business, or buy a house. Further, some of the most economically vulnerable consumers are more likely to be credit invisible.” – Richard Cordray, Former Consumer Financial Protection Bureau (CFPB) Director
The limitations in existing underwriting processes have been widely highlighted. DirectID – a solution that combines identity verification, real-time financial data, compliance checks, affordability insights, and ACH payment confirmation into a single platform – emphasizes that in traditional underwriting processes, there is a reliance on credit scores, copies of bank statements, and self-reported income data. But here is why they are falling down:
Credit scores provide limited insight into a consumer’s true financial position, they don’t provide the whole picture. With about a third of US consumers having a FICO score under 670, most traditional lenders would not offer loans to individuals with scores that low. But many of these people are creditworthy borrowers. FICO’s data doesn’t help in the assessment of whether they would repay loans – something that new data sources can help to predict more accurately.
Copies of bank statements are susceptible to fraud. With banks encouraging their customers to move to online bank statements, obtaining paper copies for application processes can introduce delays, and leads to high drop-out rates.
Self-reported income is also susceptible to fraud and does not reflect any change in circumstances which may impact the ability to make repayments in the future.
But when 1.7 billion people don’t have a history with the formal financial system, and existing frameworks are largely exclusive, how can institutions reach a balance of accurate risk assessment and continuous inclusive development?
What is alternative data?
According to Experian, in the consumer financial marketplace, alternative credit data refers to information used to evaluate creditworthiness that is not usually part of a traditional credit report.
“To fall under the Fair Credit Reporting Act (FCRA) – compliant umbrella, alternative credit data must be displayable, disputable and correctable. This data provides more insight into both full-file and thin-file consumers, to drive greater visibility and transparency around inquiry and payment behaviors. Adding the information from alternative credit data sources may allow some consumers to gain more access to credit.” – Experian
Some examples the agency brings up are:
Mobile phone payments
Cable TV payments
Bank account information, such as deposits, withdrawals or transfers
Small dollar loans
Other types of alternative data might relate to things less closely tied to a person’s financial conduct, like that person’s education or occupation.
The use of alternative data for extending financial access: the good (mostly, for businesses)
Leveraging data from previously untapped sources can bring tangible opportunities and benefits for financial institutions, a few of which include:
Opportunity to capture new customer segment
“An ‘unscoreable’ individual is not necessarily a high credit risk – rather they are an unknown credit risk. Many of these individuals pay rent on time and in full each month and could be great candidates for traditional credit. They just don’t have a credit history yet.” – The State of Alternative Credit Data, Experian, 2018
An alternative framework includes the use of alternative sources of data in order to profile a potential customer – web search history, phone usage, social media, and more. Mike Mondelli, SVP, TransUnion, listed property, tax, deed records, checking & debit account management, payday lending information, address stability, and club subscriptions being among the sources for alternative data.
“These alternative data sources have proven to accurately score more than 90% of applicants who otherwise would be returned as no-hit or thin-file by traditional models. Alternative data provides a better lens with which to evaluate all consumers, giving lenders who can score them a competitive advantage.” – Mike Mondelli, SVP, TransUnion
Experian offers a demonstrative model of how approvals change with an addition of alternative credit data into the assessment framework.
Source: The State of Alternative Credit Data, Experian, 2018
The agency shares that by adding in the visibility of alternative credit data to a near-prime population, lenders could see an increase in the approval rate within a population that was historically being left behind.
Enriched underwriting process
Arun Ramamurthy, Co-founder of Credit Sudhaar, believes alternative sources of data including social media to be an important part of creditworthiness assessment.
“(About) 6% of the people who sign up for our advisory services are intentional defaulters and fraudsters.” – Arun Ramamurthy, Co-founder, Credit Sudhaar, said.
Ramamurthy shared with The Hindu that it is only through the use of unconventional sources of information can companies and banks identify the intention of a potential borrower. Social media, in particular, has been recognized by Wharton as an important data source for credit scoring back in 2014, although the practice of judging a stranger based on his/her social environment is not really new. One of the core ideas is that “who you know matters.”
Technology companies focusing on alternative lending and alternative credit scoring can gather more information about a person using social media than by looking at their financial data. Social media also gives lenders an insight into how an applicant spends their time.
“If you look at how many times a person says ‘wasted’ in their profile, it has some value in predicting whether they’re going to repay their debt,” FICO CEO Will Lansing told the Financial Times.
Service enhancement; timeliness, and accuracy
According to Experian, some kinds of alternative data, such as online bank account information, may allow lenders to automate tasks that are done manually during the loan approval process. This automation could speed up application processes or avoid subjective interpretations that could lead to differences in treatment or wrongful discrimination.
Additionally, the agency notes that data traditionally used by lenders often does not reflect a person’s most recent activities. Alternative data could provide more up-to-date, real-time information.
“Alternative data could provide more timely indicators, such as real-time access to a consumer’s outstanding credit card balance. It could also help lenders recognize whether a particular consumer’s finances are trending in a particular direction, such as through a job status change appearing on social media. Such information could help to distinguish those consumers whose low scores are a function of prior financial problems that they have surmounted from those consumers whose financial challenges have just begun and who may pose a greater risk than the score indicates. Alternative modeling techniques might also generate more timely feedback to the extent they dynamically change as new data are ingested, though such dynamism could also carry certain risks.” – CFPB
Paul Christensen, Associate Dean of Executive Education and Clinical Professor of Finance at the Kellogg School of Management, shared insights on alternative scoring, mentioning an important positive implication for companies leveraging alternative data to make a credit decision.
“For companies, alternative credit rating is about reducing transaction costs. It’s about figuring out how to make profitable loans that are also affordable for most people – not just business owners.” – Paul Christensen, Kellogg School of Management
He also added that it is a way of addressing the problem of information asymmetry, which he calls one of ‘the definitional causes of market failure’ and one of the biggest threats to traditional microfinance. Christensen believes that would make a big difference if alternative credit scoring can help drive down costs and lower interest rates from 30% to 10%.
Experian, one of the major consumer credit reporting agencies, also outlines the opportunity to lower the costs with the use of alternative data for creditworthiness assessment. Using alternative data could lower costs for lenders and, in turn, benefit consumers through lower prices, the agency shares.
The use of alternative data for extending financial access: the bad, and the ugly (mostly, for consumers)
As with anything new, there is always the bad, and the ugly to anticipate and address before jumping into implementation.
Back in 2016, Lael Brainard, a member of the US Federal Reserve’s Board of Governors where she serves as Chair of the Committees on Financial Stability, Federal Reserve Bank Affairs, Consumer &Community Affairs, and Payments, Clearing & Settlements, shared that, “Nontraditional data, such as the level of education and social media usage, may not necessarily have a broadly agreed upon or empirically established nexus with creditworthiness and may be correlated with characteristics protected by fair lending laws. To the extent that the use of this type of data could result in unfairly disadvantaging some groups of consumers, it requires careful review to ensure legal compliance.”
While non-traditional data may have the potential to help evaluate consumers who lack credit histories, some data may raise consumer protection concerns.
CFPB also emphasized potential discriminatory implications in the use of alternative data and modeling techniques.
“For example, using alternative data that involves categories protected under Federal, State, or local fair lending laws may be overt discrimination. In addition, certain alternative data variables might serve as proxies for certain groups protected by anti-discrimination laws, such as a variable indicating subscription to a magazine exclusively devoted to coverage of women’s health issues. And the use of other alternative data might cause a disproportionately negative impact on a prohibited basis that does not meet a legitimate business need or that could be reasonably achieved by means that are less disparate in their impact.”
“Machine learning algorithms that sift through vast amounts of data could unearth variables, or clusters of variables, that predict the consumer’s likelihood of default (or other relevant outcomes) but are also highly correlated with race, ethnicity, sex, or some other basis protected by law. Such correlations are not per se discriminatory but may raise fair lending risks. The use of alternative data and modeling techniques could potentially lead to a disparate impact on the part of a well-intentioned lender as well as allow ill-meaning lenders to intentionally discriminate and hide it behind a curtain of programming code.” – CFPB
However imperfect traditional creditworthiness assessment framework may be, it can’t be criticized for the lack of transparency. The FICO score, for one, has been molded into components and every scored individual can understand what criteria affect their score.
“It may not always be readily apparent to consumers, or even to regulators, what specific information is utilized by certain alternative credit scoring systems, how such use impacts a consumer’s ability to secure a loan or its pricing, and what behavioral changes consumers might take to improve their credit access and pricing.” – Lael Brainard
With alternative credit scoring frameworks, the situation is not always clear. Let’s take Web search history, for example. Casey Oppenheim, Co-founder & CEO of Disconnect, which helps keep people anonymous online, fairly points out the possibility of negative outcomes the use of such sort of alternative data can have.
“Nobody understands the long-term impact of this data collection. Imagine that someone has 40 years of your search history. There is no telling what happens to that data.” – Casey Oppenheim, Disconnect
The availability of a lifetime of search history traps one in the outcome of the ‘mistakes of youth’ – unless the judging algorithm does not rule out the data from 20 years ago when person’s search would indicate inclinations towards activities incompatible with the idea of a trustworthy person. And it’s not just the problem of being stuck in an obsolete portrait due to a massively affecting the end result data from 10 years ago, but also the problem of fairness and transparency.
Security, accuracy, fraud
Alternative data is subject to significant alteration of opportunities as it can be affected by fraudulent activities. With phone usage records, for example, the problem of cramming can potentially lead to downgraded scoring if a person is unable to detect malicious charges by various services leading to increased billing.
Utility payments are not a perfect alternative data source either – seasonal spikes in energy consumption in some regions can make a significant difference in a financial standing of low-income groups of population, playing their scores in disadvantageous ways.
According to CFPB, though traditional data can also be inaccurate, certain types of alternative data may be more prone to errors if standards governing those data are different or weaker than those governing traditional data. Consumers might not be able to access or view some types of alternative data. This could prevent consumers from finding and correcting any inaccuracies.
Data privacy, ownership
The ability to build a comprehensive portrait based on alternative sources of data requires access to those sources, which itself raises other class of issues – data privacy and ownership. A wide variety of continuous large-scale fraud cases and cybersecurity breaches have illustrated the significance of possible security risks.
“As the data sets that financial institutions utilize expand beyond traditional consumer credit histories, data privacy will become a growing concern, as will data ownership and whether or not the consumer has any say over how these data are used and shared or whether he or she can review it for accuracy.” – Lael Brainard
CFPB also notes that some alternative data may not be related to a person’s own financial conduct and the use of these data could make it more difficult for people to improve their credit standing. Alternative credit factors may also be harder to explain to people seeking credit.
Some professionals believe that such assessments may lead to system gaming practices in the form of segregation. Once it has been figured out that certain connections on social media may negatively affect creditworthiness, people may start deleting negatively-affecting connections.
“What we are finding is that yes, indeed, individuals [could game the system], if they could know somehow that you are a financially responsible person and I am financially responsible, and we all need to show that we are good individuals to the company on social media so that we can be considered worthy of a loan. We find that individuals will have some incentives to drop their friends or at least make the information, the connection of having a friendship with [certain people] less visible.
“What that could do over time is [cause] some sort of fragmentation in social networks. Good types, people who are more financially responsible, have incentives to drop the bad types. That’s also true for the bad types as well. They have an incentive to be connected to a higher number of [financially responsible people]. And they have an incentive to be connected to a smaller number of bad types. That’s going to result in, over time, a segmentation or fragmentation of the markets.” – The Surprising Ways that Social Media Can Be Used for Credit Scoring, Wharton
Uncharted waters & unintended consequences
With alternative data, financial institutions move into unchartered waters with a lack of experience in understanding the long-term impact of such approach and appropriate history-proven algorithms for assessing that data. The new approach may not be consistent with its overall business strategy and risk tolerance of the formal financial system, including regulators.
“Banks collaborating with FinTech firms must control for the risks associated with the associated new products, services, and third-party relationships. When incorporating innovation that is consistent with a bank’s goals and risk tolerance, bankers will need to consider which model of engagement is most appropriate in light of their business model and risk-management infrastructure, manage any outsourced relationships consistent with supervisory expectations, ensure that regulatory compliance considerations are included in the development of new products and services, and have strong fallback plans in place to limit the risks associated with products and partners that may not survive.” – Lael Brainard
More importantly, we are mostly unaware of unintended consequences coming from highly varying lifestyles and circumstances of millions of lives.
In its request for information regarding the use of alternative data and modeling techniques in the credit process, CFPB emphasizes certain groups or behaviors could be penalized or rewarded in ways that are difficult to predict. For example, members of the military may frequently move and the perceived lack of housing stability or continuity may give a false impression of overall instability. Or negative inferences could potentially be drawn about consumers who are not found in the alternative data source being used by the lender.
“Foreseeable or otherwise, using alternative data and modeling techniques could also cause potentially undesirable results. For example, using some alternative data, especially data about a trait or attribute that is beyond a consumer’s control to change, even if not illegal to use, could harden barriers to economic and social mobility, particularly for those currently out of the financial mainstream.” – CFPB
An exceptionally radical example of China’s social score
The social score that’s in use in China can demonstrate everything that can go wrong with the use of highly granular, traditional, and alternative data for a variety of purposes.
The Wall Street Journal illustrated one of the perks for people with high scores early on, bringing up an example with a credit-scoring service by Alibaba affiliate Ant Financial Services – one of the eight companies that were approved to pilot commercial experiments with social credit scoring – which assigned ratings based on information such as when customers shopped online, what they bought and what phone they used. If users opted in, the score could also consider education levels and legal records. Perks in the past for getting high marks have included express security screening at the Beijing airport, part of an Ant agreement with the airport.
While this score can become a ‘free pass’ to everything for abiding citizens, it is widely criticized for its segregational aspect, which will scrutinize life for blacklisted people. A system like China’s social credit score will prevent people from taking advantage of the holes in national security and repeating misconduct in different places. There will no longer be an opportunity to start life over in a new place for convicted felons because one’s history will follow him/her everywhere and have a direct impact on rights and liberties.
The long-term social and economic consequences of developing and deploying systems that integrate vast granular information about every individual are not clear yet, but they certainly contain ever-important security concerns. Bringing together personal data from numerous sources is an extremely curious exercise, but also a magnet for a more dangerous fraud than ever – in case an individual’s record is compromised, the whole life of that person gets affected, not just a particular part of it. However, with proper security measures, such systems will have a strong transformative power over societies and enforcement of behavioral norms as they would directly connect everyday behavior with a long-term prosperity, financial and otherwise.
What do you see as the good, the bad, and the ugly of what alternative data brings to the efforts to expand financial access to 1.7 billion of unbanked individuals around the world? Send your opinion to email@example.com.