Alternative Home Financing Solutions for an Australian Company
- Building Credit Risk Model: Developed an IFRS 9 Compliant Risk Model to predict PD (Probability of Default), LGD (Loss at Given Default), EAD (Exposure at Default), and ECL (Expected Credit Loss) with both account prediction and customer prediction options to identify defaulters.
- Risk Behavior Analysis: Identified all the important drivers required to regularly track customer credit history. Ranked the drivers concerning to contribution to PD, EAD, and LGD (EL = PD * LGD * EAD), and multiple types of MIS for business users’ consumption.
- Professional Services: Time-to-time monitored the model performance by Accuracy, False Positive Rate, and current MIS reports for management. Quarter-on-quarter review and model calibration included when necessary.
- Collected historical data to understand and identify the most impacting features for a solution that includes member ID, loan amount, loan status, funded amount, term, interest rate, installation, homeownership, and DTI (debt to income ratio).
- Did an extensive data preparation to feed the selected features into the model.
- Evaluated the model with several metrics for PD, LGD, and EAD.
- Developed a set of models each for PD (Probability of Default), Loss at Given Default (LGD), and Exposure at Default (EAD) to calculate ECL (Expected Credit Loss).
- Deployed the model on the RapidMiner server and exposed it to an API-based web service for client interaction to be easily integrated with any third-party client.
- Identified the data to use, retrain, and refine the data pipeline to maintain the consistency of the model performance.
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