Preventing financial crimes in an AI-enabled market

Representational Graphic: Canva

In a globalised and interconnected world, financial crimes pose a serious threat to companies, more so for those in the banking, financial services and insurance (BFSI) sector. Data shows that in 2002, companies shelled $5 billion in fines for failing to thwart these illicit activities - an astounding 50 per cent increase from a year ago. On a global scale, firms experienced a 3 per cent reduction in turnover due to financial crimes and compliance issues, tallying up to a staggering $274 billion in 2022, up from $214 billion in 2020.

Banks grapple with several pivotal challenges when combatting financial crimes (FinCrimes). Disparate systems and redundant processes for customer onboarding, Customer Due Diligence (CDD), and risk assessment present significant hurdles. The persistence of outdated practices like manual handovers compounds inefficiencies in anti-FinCrime efforts. Operational silos further complicate this problem, leading to decentralised and partial coverage of financial functions, ultimately heightening the risk of FinCrime attack.

Additionally, a lack of efficient change management leads to banks being unable to keep up with constantly evolving financial crime regulations. Keeping up with the changes and staying updated is mandatory in the current market.

Advancements in AI have invariably led to scammers utilising the technology to conduct financial crimes on an unprecedented scale. Deepfakes and AI are set to drive $1 trillion in financial fraud and crimes as the technology enables scammers to lower costs and increase reach towards a wide pool of consumers. AI has been particularly used to ‘turbocharge’ fraud, and despite heavy investment in fraud detection, consumers lost upwards of $8 billion last year due to FinCrimes in the US alone. Hence, it is critically important for regulators and the larger banking community to come together, collaborate, and update processes to set up new systems that can tackle AI-fuelled crimes in the coming years.

Road ahead
While the obstacles appear formidable, there are several strategies banks can consider to confront these issues. Embracing an AI- and data analytics-led approach for both first and second lines of defence can prove invaluable. Having an AI-enabled first line of defence would help with predictive analysis, pattern identification, hypothesis validation, and new model creation. Adopting a 360-degree centralized platform approach fully integrates internal and external systems and helps track both internal and external FinCrime attempts. 

A centralised, platform approach can result in:
(i) Eliminating current siloed approaches and operational redundancies to prevent financial crime by at least 50 per cent.
(ii) Reducing compliance costs to prevent financial crime.
(iii) Bringing various aspects of financial crime prevention under a unified function for effective monitoring, prevention, and remediation. This includes AML/KYC, sanctions management, Modern Slavery and Human Trafficking (MSHT), Combating Financing of Terrorism (CFT), Transactions with Politically Exposed Persons (PEP), Fraud Analytics, Account Takeover, Pump and Dump Transactions, Insider Trading, Circular Trading, and more.

Housed by a library of AI models, rules engine, case management workflow, and intelligence and knowledge repository, this central FinCrime prevention would enable real-time detection of suspicious activities, creating the best first line of defence in a bank. It enhances the second line of defence using AI, rules engines, traditional data techniques, and an intelligence repository. It keeps teams updated on new FinCrime regulations and threats from external sources. 

Forward-thinking approach
When it comes to achieving comprehensive financial resilience, taking a forward-thinking approach means paying special attention to strengthening the first line of defence within financial institutions. This essential part of the strategy is all about enhancing and enriching the capabilities of the initial layer responsible for guarding against financial crimes.

The first line of defence isn't just a gatekeeper; it's a proactive force armed with advanced tools and methods. By using Predictive Analytics, institutions empower the first line of defence to become proactive guardians, always on the lookout for new threats and ready to respond quickly. This shift is more than just technology; it's about giving the first line of defence the ability to anticipate, spot, and react to evolving challenges.

Within this strengthened first line of defence, the focus also includes recognizing patterns effectively. By using smart AI algorithms, it becomes skilled at spotting subtle irregularities, uncovering hidden connections, and confirming suspicions. This analytical capability not only improves its ability to detect unlawful activities but also helps create adaptable models that can keep up with ever-changing financial crime tactics.

Moreover, a forward-thinking first line of defence takes a comprehensive approach to risk management. It goes beyond traditional limits to understand risks from various angles, from Anti-Money Laundering (AML) and Know Your Customer (KYC) processes to Counter Terrorist Financing (CFT) and complying with sanctions. This broad perspective enables it to proactively address potential vulnerabilities and ensure strong compliance with evolving regulations.

To sum it up, a forward-thinking approach to financial resilience places great importance on the first line of defence. It empowers this crucial part of the system with predictive capabilities, excellent analysis skills, and a thorough understanding of risks. This transformation isn't just about technology; it's about creating a proactive, capable, and adaptable first line of defence that stands strong against the ever-changing world of financial threats.
(The writer is the global head of BFSI at Experion Technologies)

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