Work2021

Credit Approval System Using Machine Learning: Challenges and Future Directions

Mohammad Fahim Faisal · Mohammad Neyamath Ullah Saqlain · Mohammad Arif Sobhan Bhuiyan · Mahdi Hassan Miraz · Muhammed J. A. Patwary

14 citations0/0 files storedInternational Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)

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Financial Distress and Bankruptcy Prediction Banking stability, regulation, efficiency Imbalanced Data Classification Techniques

Abstract

The applications of machine learning have now reached variety of industries, including banking and financial organisations. While credit approval is a key concern of the banking industry, machine learning is widely regarded as one of the most effective methods for credit approval. In fact, due to significant amount of research being conducted in this domain, enhanced new algorithms and/or approaches are continuously being proposed by the researchers. Therefore, to compare, contrast and synthesise the performance of these machine learning algorithms, this literature survey covered 52 articles since as far back as 2000. We have also recommended the application of fuzziness-based semi-supervised learning, which has never been previously utilised in the credit approval process, as per our survey findings.

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2000Neural network credit scoring models 2014Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending 2020A benchmark of machine learning approaches for credit score prediction 2012An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data 2020Machine learning techniques for credit risk evaluation: a systematic literature review 2009A data driven ensemble classifier for credit scoring analysis 2020An Approach for Prediction of Loan Approval using Machine Learning Algorithm 2019DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring

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