Finance
Early Detection
Reduced detection time of non-payment risk
The Challenge
Our client requires an advanced model that maximizes the accuracy of default prediction, using historical activity as training data.
| The Outcome
Increased business through our predictive model detecting the risk of non-payment of existing customers and optimizing the qualification of new applicants for the means of payment.
How we did it
- We provided an AI system for automatic recalculation of scoring weights and, subsequently, implemented a Machine Learning model to predict defaults.
- We evaluated the accuracy of the ML model using a sample of test data to estimate the future default prediction hit ratio.
- Optionally, a different date range (per user) could be selected from the default date range proposed by the system.
- The hit ratios for customers accepted or rejected due to risk of non-payment were improved with respect to the existing weighting systems.
- The model is frequently updated and retrained effortlessly, evaluating its performance/estimated accuracy.
- The possibility of including new variables and customer data not initially taken into account is designed to improve the accuracy of the model.
Shall We Talk?
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