Investigating the Utility of Data Mining for Automated Credit Scoring

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Banks and other financial organisations rely heavily on credit scoring (CS) as a method of risk management since it is both effective and necessary. It reduces financial risks and gives sound advice on loan disbursement. As a result, businesses and financial institutions are exploring innovative automated solutions to the CS dilemma in an effort to safeguard their resources and those of their clients. The use of various machine learning (ML) as well as data mining (DM) approaches has led to significant progress in CS prediction in recent years. The Deep Genetic Hierarchical Network of Learners (DGHNL) is a novel approach developed for this study. Support Vector Machines (SVMs), k-nearest Neighbours (kNNs), Probabilistic Neural Networks (PNNs), and fuzzy structures are just some of the many types of methods that may be used in the suggested method. The Statlog German (1000 occurrences) approval of credit dataset from the UCI machine learning library was used to evaluate our model. We used a DGHNL model with five unique learner types, two feature extraction methods, three kernel functions, and three methods for optimising model parameters. In addition to conventional cross-validation (CV) and train-testing (stratified 10-fold) methods, this model employs a cutting-edge biological layered training (participant selection) approach. Using data on German credit approvals from Statlog, we show that the suggested DGHNL model can obtain a prediction accuracy of 94.60% (54 errors per 1000 classifications) with its 29-layer architecture.