Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior

Sanjay Kumar, Rafeeq Ahmed, Salil Bharany (), Mohammed Shuaib, Tauseef Ahmad, Elsayed Tag Eldin (), Ateeq Ur Rehman and Muhammad Shafiq ()
Additional contact information
Sanjay Kumar: Department of Information Technology, Rajkiya Engineering College, Azamgarh 276201, India
Rafeeq Ahmed: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
Salil Bharany: Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143005, India
Mohammed Shuaib: Department of Computer Science, College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia
Tauseef Ahmad: Department of Information Technology, Rajkiya Engineering College, Azamgarh 276201, India
Elsayed Tag Eldin: Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
Ateeq Ur Rehman: Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

Sustainability, 2022, vol. 14, issue 21, 1-24

Abstract: Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated.

Keywords: financial crime; outlier detection; fraud prediction; credit card fraud; non-performing assets; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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