The global Lending industry is undergoing a state of significant digital transformation. Non-bank lenders like Rocket Mortgage have taken more market share, with six of the top 10 global lenders non-banks as of 2016, compared with only two in 2011. Additionally, over the last five years, FinTechs have opened up new forms of non-bank lending options such as peer-to-peer lending, private debt, and other new means of receiving capital in a distressed, post-subprime mortgage fall-out, lending environment. According to a report by Zion Market Research, the global digitization in lending was valued at around $1,790 Billion in the year 2016, and it is expected to reach approximately USD 83,460 Billion by 2025. While companies like Rocket Mortgage are winning borrowers based on their mobile-first customer experience, global banks have an opportunity to use more advanced AI techniques to better manage customer acquisition risk and optimize their lending portfolios.

Synechron has developed the Credit Risk Accelerator to empower banks to proactively manage their credit portfolios, whether they consist of mortgages, auto loans, credit card loans, or business loans. By ingesting historical loan, macroeconomic, and borrower data, the accelerator uses machine learning models including Random Forests, clustering algorithms, and neural networks to predict credit defaults, delinquencies, and prepayments. The accelerator enables users to drill down into the factors driving likely credit events and to proactively manage individual risks ranked by probability of incurring a specific credit event. The Credit Risk accelerator produces accurate predictions of credit events by applying the latest advancements in data science, including deep learning, to traditional and, in the future, alternative data including borrower demographics, loan characteristics, interest rates, inflation, unemployment, and other macro and microeconomic data.

This predictive analysis allows credit providers to proactively identify unanticipated credit risks and proactively offer product and service recommendations to reduce credit losses. For example, a loan officer may offer a borrower likely to default a different interest rate or payment plan to prevent a loan default or to offer a borrower likely to prepay a reduced rate or promotional offer to maintain credit at its current level. In addition, the Credit Risk accelerator enables credit providers to produce more accurate financial forecasts and to potentially improve the timeliness and accuracy of financial reports.