The model helps lenders increase revenue by identifying viable loan applications that have been wrongly rejected by the banks’ automated credit risk assessments.
Credit scoring models are used during the loan approval process to predict the likelihood of borrower delinquency. Online lending banks receive thousands of loan applications per day, and use automated assessments to approve or reject them in real time. Because these models are based on limited applicant data and subject to strict discretionary measures to keep risk levels manageable, a significant number of applications are usually rejected.
When integrated into a bank’s automated assessment, ThetaRay’s detection system identifies the potential customers with relatively low credit risk while collecting and analyzing additional data on each rejected loan applicant, including credit score, historical loan performance and personal information from government databases. These insights empower lenders to convert many rejected loan requests into approved loans while maintaining acceptable risk levels.
ThetaRay recently tested the model with a large online lending bank. After examining massive amounts of rejected loan applications over the course of a week and a half, ThetaRay determined that more than 30% had been excluded due to the limitation of the existing tools.
ThetaRay is a provider of big data analytics solutions that detect threats and discover opportunities. The company’s platform analyzes massive amounts of data for advanced cyber security, financial risk detection and operational efficiency, protecting financial services sectors and critical infrastructure against unknown threats.
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