Credit Card Default Prediction Using Machine Learning Techniques

With the aid of SMOTE and downsampling, the model makes accurate predictions of customer defaults, which in turn assists financial institutions in risk reduction.

The prediction models can be explained through the use of SHAP and LIME techniques that ensure stakeholders understand how the model comes up with a decision.

The risk of default also helps businesses in estimating customer credit risk, which in turn informs their lending policies.

By managing retention risks and defaults, financial corporations can put in place measures for the customers at the risk of being high on the spot.

The model allows for loan approvals based on relevant data, which eliminates the need for manual assessments of borrowers and enhances efficiency.

Through such, financial service providers would focus on lending their resources to less risky clients avoiding high-risk clients.

If a business is able to correctly predict who will default on their debts, they can operate at a lower cost with regards to bad debt, chasing payment and writing off of loans.

The sermons based on predictions are more reliable, leading to business changes where they predict the customers are likely to produce positive results.

The model can be applied to a multitude of verticals in the finance sphere enabling banks, insurers, and lenders to predict defaults on scale.

Employing customer default predictive models helps banks in risk mitigation as it meets the demands of the ethical lending principle.