Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

Abstract

Machine Learning and Deep Learning methods are widely adopted across financial domains to support trading activities, mobile banking, payments, and making customer credit decisions. These methods also play a vital role in combating financial crime, fraud, and cyberattacks. Financial crime is increasingly being committed over cyberspace, and cybercriminals are using a combination of hacking and social engineering techniques which are bypassing current financial and corporate institution security. With this comes a new umbrella term to capture the evolving landscape which is financial cybercrime. It is a combination of financial crime, hacking, and social engineering committed over cyberspace for the sole purpose of illegal economic gain. Identifying financial cybercrime-related activities is a hard problem, for example, a highly restrictive algorithm may block all suspicious activity obstructing genuine customer business. Navigating and identifying legitimate illicit transactions is not the only issue faced by financial institutions, there is a growing demand of transparency, fairness, and privacy from customers and regulators, which imposes unique constraints on the application of artificial intelligence methods to detect fraud-related activities. Traditionally, rule based systems and shallow anomaly detection methods have been applied to detect financial crime and fraud, but recent developments have seen graph based techniques and neural network models being used to tackle financial cybercrime. There is still a lack of a holistic understanding of the financial cybercrime ecosystem, relevant methods, and their drawbacks and new emerging open problems in this domain in spite of their popularity. In this survey, we aim to bridge the gap by studying the financial cybercrime ecosystem based on four axes: (a) different fraud methods adopted by criminals; (b) relevant systems, algorithms, drawbacks, constraints, and metrics used to combat each fraud type; (c) the relevant personas and stakeholders involved; (d) open and emerging problems in the financial cybercrime domain.

VTAIM: Volatile Transaction Authentication Insurance Method for Cyber Security Risk Insurance of Banking Services

“…RF identifies the exact cause of faults that provide optimal information for further process. RF enhances the robustness and effectiveness level of the system by improving the accuracy rate in detecting defects in banking services [14,15]. Banking security relies on user and platform-based authentication for authenticated transactions.…”

Use of Federated Learning and Blockchain towards Securing Financial Services

“…Almost any business may fall prey to cybercriminals, but they often choose their targets based on which ones would net them the most money or have the biggest impact [4]. Hackers often go after banks and other similar financial services because they meet the above requirements.…”

Financial cybercrime in the Islamic Finance Metaverse

“…Cybercrime and malicious hacking have significantly intensified. Financial crimes have led to a significant number of regulators that revise rules and account for illegal trafficking and money laundering [2]. There have been economic sanctions, which target both countries, public and private entities.…”

“…While there has been considerable attention around the virtual universe, understanding the environment behind the virtual universe and what value it creates is of critical importance. The land prices related to digital land have been on the rise, with Republic Real Estate launching a fund for investors to purchase virtual land [2]. The fund will purchase virtual land across various online metaverses and transform them into hotels, stores, and other uses.…”