Purpose

The 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/approach

The 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.

Findings

Finally, 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/implications

Additionally, 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 implications

Structured 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/value

The 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.

中文翻译：

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印度银行内部主导的网络欺诈：一种有效的基于机器学习的防御系统，用于欺诈检测、优先级排序和预防

目的

该研究旨在概述在最近涉及印度知名银行的重大价值欺诈事件中引起主流关注的不同类型的内部主导的网络欺诈。作者试图识别和分类网络欺诈及其驱动因素，并将它们关联起来以制定最佳缓解计划。

设计/方法/途径

选择用于识别和分类的方法是通过详细的文献回顾和与风险和警戒官员以及网络单元专家的焦点小组讨论。作者通过基于机器学习的 k 最近邻 (K-NN) 方法评估了印度银行业网络欺诈的未来，并对网络欺诈的未来进行了优先排序和预测。预测未来揭示一些特定网络欺诈的主导地位将有助于获得适当的欺诈预防模型，使用以关联方为中心（受害者和罪犯）根本原因方法。该研究使用相关性分析并将欺诈与其各自的驱动因素进行映射，以确定特定于资源的有效缓解计划。

发现

最后，本文总结了一个概念框架，用于在研究范围内防止内部主导的网络欺诈。网络欺诈缓解生态系统将有助于政策制定者和欺诈调查人员通过及时快速地检测和预防网络欺诈为银行创造更稳健的环境。

研究局限性/影响

此外，该研究支持印度储备银行和印度政府启动的网络安全计划和计划，以确保对银行生态系统的保护，即 RBI 直接计划、综合监察员计划、cyber swachhta kendra（僵尸网络清理和恶意软件分析中心）、国家网络协调中心 (NCCC) 和安全监控中心 (SMC)。

实际影响

本研究提出的结构化和有效的内部主导的网络欺诈缓解计划将保护银行、员工、监管机构、客户和经济资源，节省银行当局和政策制定者的时间和金钱，并节约资源。此外，这将提高印度银行业的声誉并延长其寿命。

原创性/价值

由内部人员主导的创新网络欺诈缓解方法可快速识别网络欺诈、确定其优先级、确定其突出的根本原因、绘制具有各自根本原因的欺诈图，然后提出策略以确保具有成本效益和省时的银行生态系统。