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The client is a mortgage financing company based in Australia. The client deals with home financing and is providing alternative solutions to traditional interest-based lending options in Australia.
The client wanted to predict credit risk complying with the International Financial Reporting Standard. The client 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.
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 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.
We used the following process to meet and resolve the client’s business challenge. We:
Tools: Python, RapidMiner, and MySQL
Techniques: SMOTE, Logistic Regression, and Linear regression
The new AI/ML-based model has been performing well with a consistent average accuracy of 85-90% that has helped the client predict credit risk efficiently. The client has also been able to automate the entire process leading to a reduction in costs and resources. The client is now able to plan and strategize to reduce loss add more value to the business.