increasing or decreasing – and capture that relationship with risk outcomes. Alternative data sources complement static and trended credit history by introducing consumers’ checking history, property ownership, and alternative finance activity into credit scoring models. Consumers with comparable credit files can demonstrate vastly different repayment performance; incremental information bearing on creditworthiness equips lenders to optimize risk differentiation when the credit file alone doesn’t capture the full story. It is no small feat to create a new model for calculating and predicting high-performing loans. For example, BHG Financial, a leader in unsecured business and personal loans and creator of one of the country’s largest community bank loan networks, once relied on the traditional credit scoring model to help their decision-making. That is until they evolved their credit model to identify a miscategorized set of high-quality borrowers out there most lenders were passing by. Partnering with TransUnion, BHG Financial’s data scientists analyzed over two million consumer loans. Each loan was over $20,000, had 36+ month terms, and originated between 2015 and 2017. Billions of data points were analyzed and assessed to create their proprietary credit model, the Score. As a result, they gained faster approvals, identified sub-prime borrowers that perform well and Prime borrowers with high default rates, thereby increasing their originations significantly. This success trickles down to their Bank Network , which is There is data that, once analyzed, can give critical insights to borrower characteristics unable to be categorized by a single number. That is because people are more dynamic than their credit scores. To learn more about BHG contact: Tom Badolato SVP, Institutional Relationships at (315) 372-4510 or tbadolato@bhgbanks.com. Or visit our website at bhgloanhub.com/Tom. comprised of over 1400 community banks that purchase their loans. The result – almost $1 billion in interest earned since 2001. By working together with the already established success of the FICO score, the chance is lower that good-paying borrowers are labeled as high risk, enabling some lenders to approve pockets of creditworthy consumers others would decline. At the same time, the chance of labeling risky borrowers as low risk is also diminished, enabling lenders to protect the credit quality of their portfolios. Where does that leave lenders unable to dedicate time and money to develop their own evolved credit scoring model? The simple answer is to work with companies like BHG Financial and skip the extensive research and the costly origination process. This gives them immediate access to purchasing top-quality loans with low risk, which can quickly strengthen their loan portfolio to meet their bank’s criteria. This solution is possibly the best answer to finding a more futureforward way of predicting lending outcomes. 11 ISSUE 3 | 2022
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