Alternative Home Financing Solutions for an Australian Company
About the Client
The Business Challenge
The client wanted to predict credit risk complying with the International Financial Reporting Standard. They also wanted the solution to be implemented on a platform where all processes, such as Probability of Default (PD), Loss at Given Default (LGD), and Exposure at Default (EAD) to calculate the Expected Credit Loss (ECL) can be automated. The client also wanted the solution to take in financial and economic variables.
What Aptus Data Labs Did
We built an advanced AI/ML-based engine to predict the credit risk complying with IFRS 9. We used RapidMiner and Python to create an automated process that took either the member id or the entire record as input to process through multiple sub-processes to create the desired output.
The Impact Aptus Data Labs Made
The new AI/ML-based predictive engine enabled the client to assess the possibility of the borrower’s repayment failure and the loss caused to the financer for non-payment. The client was also able to predict or measure the risk factor of any transaction that helped the client to plan with strategies to tackle a negative outcome. The client could also set up credit models to determine the level of risk while lending.
The Business and Technology Approach
We used the following process to meet and resolve the client’s business challenge. We:
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