Loan underwriting is time-consuming and errors are expensive. Most loan applications require a skilled underwriter. There are hundreds of “hard” and “soft” rules which take years to learn. There is a backlog of loan applications, which means a loss of revenue.
Based on the existing loan history, the team trained a predictive model that automates loan decisions. A binary classification model was used with loan characteristics (income, credit score, debt, etc.) as input. The output was an approved/declined score.
- Financial Sevices
- Data Science Experience (DSX)
Data Science Techniques
- Binary classification
- Decrease of loan failure rates
- Consistent, repeatable decision-making
- Revenue increase by approving more loans
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