Internal-led cyber frauds in Indian banks: an effective machine learning–based defense system to fraud detection, prioritization and prevention

• Published in: Aslib journal of information management Vol. 75; no. 2; pp. 246 - 296
• Main Authors: Chhabra Roy, Neha, Prabhakaran, Sreeleakha
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 23.03.2023; Emerald Group Publishing Limited
• Subjects: Banking; Banking industry; Banks; Correlation analysis; Cybercrime; Cybersecurity; Employees; Fraud; Fraud prevention; Literature reviews; Machine learning; Malware; Regulatory agencies; Root cause analysis; India; Fraud drivers; K-Nearest Neighbour (K-NN); Fraud severity mapping; Prevention framework; Fraudster-infrastructure-target model; Cyber frauds
• ISSN: 2050-3806; 1758-3748
• DOI: 10.1108/AJIM-11-2021-0339

PurposeThe study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian banks. The authors attempted to identify and classify cyber frauds and its drivers and correlate them for optimal mitigation planning.Design/methodology/approachThe methodology opted for the identification and classification is through a detailed literature review and focus group discussion with risk and vigilance officers and cyber cell experts. The authors assessed the future of cyber fraud in the Indian banking business through the machine learning–based k-nearest neighbor (K-NN) approach and prioritized and predicted the future of cyber fraud. The predicted future revealing dominance of a few specific cyber frauds will help to get an appropriate fraud prevention model, using an associated parties centric (victim and offender) root-cause approach. The study uses correlation analysis and maps frauds with their respective drivers to determine the resource specific effective mitigation plan.FindingsFinally, the paper concludes with a conceptual framework for preventing internal-led cyber fraud within the scope of the study. A cyber fraud mitigation ecosystem will be helpful for policymakers and fraud investigation officers to create a more robust environment for banks through timely and quick detection of cyber frauds and prevention of them.Research limitations/implicationsAdditionally, the study supports the Reserve Bank of India and the Government of India's launched cyber security initiates and schemes which ensure protection for the banking ecosystem i.e. RBI direct scheme, integrated ombudsman scheme, cyber swachhta kendra (botnet cleaning and malware analysis centre), National Cyber Coordination Centre (NCCC) and Security Monitoring Centre (SMC).Practical implicationsStructured and effective internal-led plans for cyber fraud mitigation proposed in this study will conserve banks, employees, regulatory authorities, customers and economic resources, save bank authorities’ and policymakers’ time and money, and conserve resources. Additionally, this will enhance the reputation of the Indian banking industry and extend its lifespan.Originality/valueThe innovative insider-led cyber fraud mitigation approach quickly identifies cyber fraud, prioritizes it, identifies its prominent root causes, map frauds with respective root causes and then suggests strategies to ensure a cost-effective and time-saving bank ecosystem.