Binary BAT algorithm and RBFN based hybrid credit scoring model
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Binary BAT algorithm and RBFN based hybrid credit scoring model Diwakar Tripathi1 · Damodar Reddy Edla2 · Venkatanareshbabu Kuppili2 · Ramesh Dharavath3 Received: 9 January 2020 / Revised: 19 July 2020 / Accepted: 4 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis. Keywords Optimization · Credit scoring · Feature selection · Classification
Diwakar Tripathi
[email protected] Damodar Reddy Edla [email protected] Venkatanareshbabu Kuppili [email protected] Ramesh Dharavath [email protected] 1
SRM University AP, Amaravati, Andhra Pradesh 522502, India
2
National Institute of Technology Goa, Ponda, Goa 403401, India
3
Indian Institute of Technology (ISM), Dhanbad, India
Multimedia Tools and Applications
1 Introduction The term credit is used in banking system or in financial institutions. Which represents to the amount that is borrowed by a customer from a financial institution. Credit limit to a customer is decided by system on the basis of customer’s credentials like annual income, customer’s property etc. Credit scoring is a process to access the risk associated with credit products by applying statistical or machine learning techniques on historical dataset [41]. It tries to separate the effects of various applicant characteristics on criminal behaviour and defaults. The main concern of credit score prediction model is to determine whether credit consumers belong to either legitimate (creditworthy) or suspicious (non-creditworthy “it may commit undesirable behaviour in near future”) consumer group. It is not a single step process, various systems do it in various steps such as application, behavioural, collection and fraud scoring [46]. Application scoring concerns for the evaluating the legitimateness o
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