“This problem disproportionately impacts consumers who are Black or Hispanic, and people who live in low-income neighborhoods. It also impacts some recent immigrants, young people just getting started or people who are recently widowed or divorced who don’t have enough credit history on their own.”
These groups need the same products as credit consumers across the scoring spectrum. How can financial institutions pull back the curtain, learn enough about these people and provide appropriate credit while managing risk?
Alternative data: A deciding factor
Making a credit decision for a population without a traditional credit file or sufficient history is no easy task. Without historical context, creditors risk delinquencies and defaults. How can financial institutions know whether to extend a credit offer to an individual they’ve never met?
Creditors looking to serve these populations can benefit from real-time accessibility to supplemental information—and thus develop credit score models for thin-file consumers. Additionally, new and alternative data sources can help make credit available for “credit invisibles.”
This alternative data can include payment histories on a consumer’s mobile phone, rent and utilities, along with electronic deposit and withdrawal transactions. With instant access to this data, issuers can build decision logic that takes into account non-traditional factors—and provide credit to individuals who need it.
By factoring these new elements into the decision process, financial institutions can assume a leading edge as they discover and capture new-to-credit consumers. Credit cards used within a chain or single store, or with low limits, often represent one of the first credit products for younger people or those with thin credit history.
For those populations, credit access (even at a higher interest rate) may prove appealing as a way to access items they need while they build their credit history.
An overview of the underserved
Besides alternative data, financial institutions can use artificial intelligence to help underbanked/unbanked customers, or those with thin credit files. In cases where traditional bureau data is lacking or absent, AI-enabled business processes can track down the details an organization needs to effectively evaluate and engage with those customers.
Access to alternative data can address one element of thin and no-file customers. Putting that information in context and making a profitable, risk-averse lending decision shows how AI applications can benefit both the consumer and financial institution.
For financial institutions, billions of dollars are at stake. According to a report by the Center for Financial Services Innovation, “underserved consumers spent $140.7 billion on fees and interest” in 2015. This money went to short- and long-term credit products, payment and deposit accounts, single payment credit products, and other products and services that banks and financial services organizations can easily provide to consumers. Institutions willing to invest in alternative data could tap into a significant market, develop new customers and enhance experiences for millions of underbanked Americans.
For many financial institutions and would-be creditors, the time is ripe to reconsider credit decisioning logic. For decades, credit scores have served as a reliable, solid metric. But today’s rich data analytics and technologies empower us to dig deeper call for an alternative approach. Thus comes the potential to put a new twist on an old saying, and give credit where credit is due.
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