The boundaries between our digital and physical worlds have dissolved. Keeping chip and pin information safe is no longer enough. Invisible wars are being waged in every aspect of our connected lives.
While technology has made people's lives easier, it has also opened new avenues for sophisticated attacks. All forms of financial crime, including money laundering and fraud, are costly problems Expected to cost $15.63 trillion by 2029.
The rise of attack techniques such as social engineering, deepfakes, and other artificial intelligence (AI)-powered threats will only exacerbate this problem.
Traditionally, financial services institutions have relied on rules-based systems and manual checks. However, in the face of new attacks, these protection methods are no longer effective, lacking the speed and scale of modern criminal networks.
From the old and reliable reactive method…
For decades, the financial services industry has relied on two fundamental pillars to prevent fraud: know your customer (KYC) and anti-money laundering (AML). These were typically reactive manual processes that organizations had to follow. This means the threat may have been missed or there is a risk of false positives.
But in today's digital-first world, these traditional manual processes are being outwitted and overwhelmed. Older protection methods are primarily reactive and not designed for AI-based attacks. This means that advanced threats are slipping through the cracks of manual reviews, while at the same time compliance teams are drowning in false positives, leading to inefficient systems.
To win this new game, fintech companies must build on traditional KYC and AML practices. This is where proactive processes powered by AI and machine learning come into play. AI provides a holistic, risk-based assessment of people's identities by analyzing millions of data points in real time and identifying patterns in the data. This pattern recognition is essential to effective fraud detection and prevention.
…towards a proactive AI-driven approach
At the point of onboarding, KYC should be an AI-powered process so that you are the first to be alerted to potential risks or discrepancies. Advanced AI platforms can go beyond what humans can see and automatically verify thousands of different ID document types around the world and check if they are legitimate. This, combined with liveness detection, ensures that an individual is who they say they are. This is also a good approach to combat serious threats like deepfakes.
AML is similarly AI-driven and must provide continuous monitoring. This allows banks to track the flow of funds across complex networks and flag suspicious transaction patterns that deviate from typical behavior. Anomalies are reported on an ad hoc basis, reducing the burden on compliance teams and allowing them to focus their time on addressing real threats. Similar to fraud detection, AI can identify and predict future fraud as it occurs, protecting both customers and financial services institutions from financial and reputational loss.
This layered approach is critical because no single check can detect all fraud. Fundamentally, it's all about AI predicting the next actions of a nefarious attacker and protecting both customers and organizations from financial and reputational damage.
Build protection within your AI systems
As digital banking becomes more and more pervasive in society, it is essential to ensure that customer data remains secure. Therefore, for AI models to be truly trusted and effective, they must be built on a foundation of scalability, security, human ethics, and integrity.
Importantly, the effectiveness of an AI system is based on the quality of the data fed to it. Therefore, building secure AI models for any financial institution requires robust data governance and protection as a foundation. The data input into the model must be accurate and complete, and this is further enhanced by strong human oversight.
Effective AI systems need to be transparent, and this is where explainable AI comes in. This means that organizations must be able to understand and justify model outputs internally and externally. This explainability element also ensures that the model is reliable, ethical, and free from bias.
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 a proactive, intelligent approach. Using AI and machine learning, you can automate ID document verification, security features, biometrics, and health checks at the point of onboarding.
Combating financial crime is an ongoing and evolving challenge for organizations, where customer protection and business profits are paramount. and 90% of financial institutions are already using AI to combat emerging fraud threatsyou can stay one step ahead. This represents a fundamental change in financial crime prevention to ensure a safer and more transparent global financial system.
By building scalable, secure, and ethical AI systems, all financial institutions, both traditional and fintech, can build a resilient foundation to respond to current threats and protect against future threats. Threat actors are constantly rewriting the rules of the game, so the only winning strategy is for organizations to create their own rules.
