The lines between our digital and physical worlds have dissolved. No longer is it enough to simply keeping chip and pin information secure; there’s an invisible war being waged on every facet of our connected lives.
And whilst technology has made people’s lives easier, it has also opened new avenues for sophisticated attacks. Financial crime, in all of its forms, like money laundering and fraud, is an expensive problem that is expected to cost $15.63 trillion by 2029.
With the rise of attack methods like social engineering, deepfakes and other artificial intelligence (AI) powered threats, this problem is only going to grow.
Traditionally, financial services institutions have relied on rules-based systems and manual checks. But in the face of new attacks, these protection methods are no longer effective – they lack the speed and scale of modern criminal networks.
From old, reliable reactive methods…
For decades, the financial services industry has relied on two foundational pillars to prevent fraud: Know Your Customer (KYC) and anti-money laundering (AML). These have typically been reactive, manual processes for organisations to follow. This has meant that threats may have been missed, or there is a risk of false positives.
However, in today’s digital-first world, these traditional, manual processes are being outmanoeuvred and overwhelmed. The older protection methods are fundamentally reactive, and are not designed for AI-based attacks. This has meant that sophisticated threats slip through the cracks of manual reviews, and at the same time compliance teams are buried under false positives, leading to an inefficient system.
To win this new game, fintech firms need to build upon traditional KYC and AML practices. This is where AI and machine learning driven proactive processes come into the fray. By analysing millions of data points in real-time and identifying patterns in data, AI moves to a holistic, risk-based assessment of people’s identity. This pattern recognition is essential for effective fraud detection and prevention.
…to a proactive AI-driven approach
At the point of onboarding, KYC must be an AI-powered process, so that potential risks and inconsistencies are flagged at the start. An advanced AI platform can automatically verify thousands of different ID document types globally, checking if they are legitimate, going beyond what humans can see. This, coupled with liveness detection, ensures that an individual is who they say they are; it’s also an approach that’s fit to combat serious threats like deep fakes.
AML should equally be AI-driven and provide continuous monitoring. As such, banks will be able to trace the flow of funds across complex networks, and can flag suspicious transaction patterns that are different from typical behaviour. With anomalies being flagged on an ad hoc basis, it means that the burden on compliance teams will be reduced, and they will be able to focus their time on dealing with the real threats. Similarly with fraud detection, AI can identify and predict future fraudulent activity as it happens, which protects both customers and the financial services institution from monetary and reputational losses.
This layered approach, is crucial because no single check can catch all fraud. It’s fundamentally all about AI anticipating nefarious actors next move, protecting both the customer and the organisation from financial and reputational losses.
Building protection within AI systems
As digital banking becomes increasingly ingrained in society, it will be imperative to ensure customers’ data is kept safe and secure. So, for AI models to be truly trusted and effective, they must be built upon a foundation of scalability, security, human ethics and integrity.
Importantly, the effectiveness of any AI system is based upon the quality of the data fed into it. As such, building a secure AI model for any financial institution means having robust data governance and protection as the foundation. The data that is fed into the model should be accurate and complete, and this is further strengthened by strong human oversight.
An AI system that is effective should be transparent, and this is where explainable AI comes into play. This means that organisations must be able to understand and justify the outputs of the model internally and externally. This explainability element will also ensure that the model is trustworthy, ethical, and not biased.
How AI is redefining the fight against financial crime
AI is redefining the fight against financial crime by moving from a reactive rules-based approach to one that is proactive and intelligent. AI and machine learning can be used to automate the verification of ID documents, checking security features, biometrics, and liveness at the point of onboarding.
The fight against financial crime is a continuous and evolving challenge for organisations, where the protection of customers and a business’ bottom line come first. With 90% of financial institutions already using AI to combat emerging fraud threats, it allows them to be one step ahead. This represents a fundamental shift in financial crime prevention to ensure a much safer and more transparent global financial system.
Through building scalable, secure, and ethical AI systems, all financial institutions, traditional and fintechs, can keep pace with current threats, and build a resilient foundation to protect against future threats. As threat actors constantly rewrite the rules of the game, the only winning strategy is for organisations to create their own rules.
