Delinquency Prediction
Predict loan performance and proactively prevent delinquency
Overview
Leverage machine learning to assimilate banking data and apply them to statistical models for finding out scores that determine the probability of delinquency. Predict delinquent behavior of borrowers to assist the financial institutions and credit lenders in reducing the financial risks.
Solution
The importance to predict the future and forecast the success and failure of a loan applicant to fulfill their obligation is immense for both banking and private lending institutions. Leverage machine learning to identify early signs of financial distress and avert potential delinquency. Analysis of past financial records of individuals using statistical ML algorithms enables identification and appropriate action against credit defaulters.
Capabilities
Loan Delinquency
Credit Delinquency
Every banking or financial institution performs due diligence to identify successful loan applications but it does not predict the ability of the borrower to repay the loan. Leveraging the statistical ML models and generating patterns of delinquent borrowers from the past are able to identify a potential defaulter which reduces the chances of jeopardizing the full repayment.
ML algorithms help in tracking the customer trends in payments and expenses by which a potential risk candidate/defaulter can be identified. Alert systems can be developed for the lenders along with suggestions for appropriate solutions/actions.
Business Impact
Avoid defaulters
Avoid financial risks
Resources
Case Studies
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