Alternative data is financial information not typically collected by a credit reporting agency. Unlike conventional data such as credit scores, quarterly financial statements or company filings, alternative data can drive new insights and can be leveraged for business expansion. Further, it can be used to provide credit opportunities for thin-file and no-file customers, democratizing the lending space and allowing for greater inclusion.
There are many types of alternative data, which can be accessed either in aggregate or through APIs.
Alternative data such as utility payment history, payroll data and POS transactions have been used by Fintechs to assess loan candidates with such success that traditional banks have begun taking notice. Just this year, Bank of America analysts suggested embracing alternative data in an effort to regain a competitive edge.
Paul von Martels, VP of Equitable Bank, told the Canadian Lenders Association that alternative data can help “scale new product lines” being brought to market, such as prime mortgage, life insurance policy, and reverse mortgage lending. Similarly, Kurt Nelson, the Director of Fraud and Identity Management at Uplift, a CLA member, said that these types of data sets can be helpful for industry specific decisions. For example, lenders in the travel industry can decide whether a person’s flying patterns make sense, and make risk-based assessments from there.
Alternative Data and the Five ‘C’s of Credit
These methods can be used by lenders to more effectively determine risk potential and creditworthiness. Using alternative data can help give lenders a clearer picture of the five ‘C’s of credit:
- Character: leveraging consumer behavior metrics such as purchasing and repayment history gives a clearer picture of a lending candidate beyond a mere credit score.
- Capacity: alternative data gives lenders information about the current debt and future financial obligations of a client. This helps lenders better assess the capacity for loan repayment.
- Collateral: credit scores calculate collateral with information that may not be relevant to a client’s changing circumstances. Alternative data can provide a more up-to-date assessment of risks and can be used to determine the appropriate collateral needed on a loan.
- Conditions: more information on a client allows lenders to better tailor loan structuring and offer more competitive loans.
- Capital: understanding all relevant revenue streams gives lenders a better idea of what factors might impact loan repayment.
Uses in Fraud Prevention
Kurt Nelson from Uplift said that alternative data is “at the heart” of fraud and identity management systems. To better assess fraud risk, lenders can work with datatech companies that specialize in device fingerprinting in order to analyze consumer behavior across the web.
The capability to see on a real-time basis the frequency of transactions and apply fraud indicators based on those data sets is an important feature to be leveraged by alternative data. While data from social media remains controversial in the lending space and can distract from the key indicators gathered from statement data, social media data can be used effectively to determine fraud. Some Fintech lenders have a team that manually investigates such cases and uses social media as a source to identify linkages between transactions. It remains to be seen if this can be accomplished through automation in the future.
Certainly, privacy concerns are part of the broader conversation about the use of alternative data. Firms should be aware of the regulatory environment they operate in, and should continually pursue best practices when it comes to transparency. Although alternative data can be more anonymised than its traditional counterpart, when using personally identifiable information such as public records, web traffic, email metadata or job listings, companies should do everything they can to manage privacy concerns.
Alternative data can be used to extend credit to a credit-invisible population of immigrants, students or thin/no-file candidates with little credit history. From a consumer standpoint, underwriting these loans helps extend affordable credit and discourage borrowing from predatory sources.
Source: Oliver Wyman
A credit score alone can be an insufficient metric to determine credit worthiness. Moreover, a low credit score does not always indicate future inability to repay loans. In an estimate found by the Financial Industry Regulatory Authority, the under-banked population comprises a high proportion of minorities; only 51% of African-Americans and 58% of Latinos have a credit score, while 75% of American adults have a credit score.
For lenders, alternative data can increase how many profitable loans they extend and be leveraged to underwrite larger value transactions in the BNPL space. Ultimately, a better perspective on borrowers gives lenders the ability to offer more competitive interest rates and manage risk.
The Future of Alternative Data
There’s still room to grow in this sector, as a number of promising alternative data acquisition methods have yet to reach widespread adoption. In the realm of identity verification, the demand for innovation in customer verification has led some Fintechs to turn to blockchain in order to facilitate faster customer onboarding and create a more frictionless customer experience.
Further questions remain about agency and how individuals can monetise their own data. While Adam Gibson of Flinks told the CLA that Fintechs should take a “firm stance” that consumer data is private, he stressed that more data exchange across banking organizations could help consumers make payments between accounts and have more direct access to their funds. These concerns touch upon the principles of open banking and whether consumer monetisation of their own data can be beneficial for businesses.
Overall, greater usage of alternative data has the possibility to provide increased democratization within lending and help stimulate innovation in the Fintech sector.