Credit scoring
Use non-traditional data sources in credit scoring to offer loans for thin-file customers.
One of the most significant risks that banks or lending institutions face is credit risk, which is a result of a borrower’s failure to repay a loan or meet contractual obligations. As a result, a sound risk management system and practice are essential for a healthy lending operation.
Here, Niko AutoML platform can be used to build credit risk models to predict likelihood of a loan default. Once we have an accurate model based on historical loan data, we can make predictions for better risk pricing, credit approval, and portfolio management decisions.
Training:
The target variable for this use case is whether or not the loan was default. Selecting this variable as target makes this a binary classification problem.
Niko Automated Machine Learning automates significant steps of the model building process. Niko builds machine learning models in minutes and automatically selects the best fitted model, eliminating the need for time-consuming, labor-intensive manual building and testing of numerous models to find the most accurate model for your business needs.
Outcome:
Niko produces explainable report and charts to evaluate the model performance. The business owner can decide their trade-off and choose threshold of the model.
Prediction:
In order to predict the future loan application’s default probability, you can input new customer and see the result. Niko provides predictions with explainable chart in order to turn the machine-made decision into human-interpretable rationale. It explains why a particular loan decision was made complying with the regulatory requirements.
Fraud detection:
Build fraud detection model for alerting in-action fraudulent activity preventing the worst.
Debt collection:
Focus on borrowers with higher repayment rate to make an impact on your collection.