A Framework for Credit Risk Prediction Using the Optimized-FKSVR Machine Learning Classifier
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Abstract
Transparency is influenced by several crucial factors, such as credit risk (CR) predictions, model reliability, efficient loan processing, etc. The emergence of machine learning (ML) techniques provides a promising solution to address these challenges. However, it is the responsibility of banking or nonbanking organizations to control their approach to incorporate this innovative methodology to mitigate human preferences in loan decision-making. The research article presents the Optimized-Feature based Kernel Support Vector Regression (O-FKSVR) model which is an ML-based CR analysis model in the digital banking. This proposal aims to compare several ML methods to identify a precise model for CR assessment using real credit database information. The goal is to introduce a classification model that uses a hybrid of Stochastic Gradient Descent (SGD) and firefly optimization (FFO) methods with Support Vector Regression (SVR) to predict credit risks in the form of probability, loss given, and exposure at defaults. The proposed O-FKSVR model extracts features and predicts outcomes based on data gathered from online credit analysis. The proposed O-FKSVR model has increased the accuracy rate and resolved the existing problems. The experimental study is conducted in Python, and the results demonstrate improvements in accuracy, precision, and reduced error rates compared to previous ML methods. The proposed O-FKSVR model has achieved a maximum accuracy rate value of 0.955%, precision value of 0.96%, and recall value of 0.952%, error rate value of 4.4 when compared with the existing models such as SVR, DT, RF, and AdaBoost.
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