How Is Loan Decisioning Software Being Used?
Big Data has helped organizations in a number of industries better understand and predict the behaviors of their customers. By scanning thousands of pieces of data, sophisticated loan decisioning software can help lend valuable insight into behavioral characteristics and patterns. Lenders are increasingly turning to these higher-level solutions to evaluate the creditworthiness of borrowers.
These types of analytical solutions are being embraced by online lenders to higher degrees of use, as they can provide the fast decisions that borrowers have come to expect from this market. The New York Times explored how alternative lender ZestFinance has leveraged such software, and the challenges that have come with it.
Since its start in late 2010, ZestFinance has underwritten more than 100,000 loans, and is currently authorized in seven states. Douglas Merrill, the company’s founder, attributes his success to the data-sifting algorithms that are being constantly updated to perform more accurate underwriting. However, despite a solid track record of offering convenient lower cost loans, ZestFinance has drawn the concern of regulators at the Federal Trade Commission.
“I have no doubt that they have come up with neat correlations that are predictive,” the Federal Trade Commission Aaron Rieke told the source. However Rieke added that “we have no idea how to talk about or assess the fairness of their predictions.”
Loan Decisioning Practices
While ZestFinance expressed confidence that these concerns will fade with time, lenders still need to be remain vigilant that their loan decisioning practices are in compliance with both existing and emerging regulations. The Consumer Financial Protection Bureau wants to encourage innovation, but is monitoring these underwriting solutions carefully, according to Patrice A. Ficklin, head of its fair lending office.
Lenders can best mitigate risk by ensuring that their credit risk software is capable of drawing on alternative data sources that are in compliance with current rules.
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Big Data has helped organizations in a number of industries better understand and predict the behaviors of their customers. By scanning thousands of pieces of data, sophisticated software can help lend valuable insight into behavioral characteristics and patterns. Lenders are increasingly turning to these higher-level solutions to evaluate the creditworthiness of borrowers.
These types of analytical solutions are being embraced by online lenders to higher degrees of use, as they can provide the fast decisions that borrowers have come to expect from this market. The New York Times explored how alternative lender ZestFinance has leveraged such software, and the challenges that have come with it.
Since its start in late 2010, ZestFinance has underwritten more than 100,000 loans, and is currently authorized in seven states. Douglas Merrill, the company’s founder, attributes his success to the data-sifting algorithms that are being constantly updated to perform more accurate underwriting. However, despite a solid track record of offering convenient lower cost loans, ZestFinance has drawn the concern of regulators at the Federal Trade Commission.
“I have no doubt that they have come up with neat correlations that are predictive,” the Federal Trade Commission Aaron Rieke told the source. However Rieke added that “we have no idea how to talk about or assess the fairness of their predictions.”
While ZestFinance expressed confidence that these concerns will fade with time, lenders still need to be remain vigilant that their loan decisioning practices are in compliance with both existing and emerging regulations. The Consumer Financial Protection Bureau wants to encourage innovation, but is monitoring these underwriting solutions carefully, according to Patrice A. Ficklin, head of its fair lending office.
Lenders can best mitigate risk by ensuring that their credit risk software is capable of drawing on alternative data sources that are in compliance with current rules.
