Financial institutions are making significant progress in their preparedness to tackle fraud, especially through investments in technology like AI, machine learning, and identity risk solutions to upgrade real-time fraud detection and prevention. However, despite these advances, the rapid evolution of AI-fuelled fraud poses a great challenge to keep up to speed.
On the people side, institutions are training employees to recognise emerging schemes and running customer awareness campaigns. Despite this, a shortage of skilled fraud prevention experts persists.
Processes have improved with better internal controls and faster fraud detection, yet many institutions remain reactive rather than proactive, which is crucial for keeping up with increasingly complex threats.
FRAML (Fraud and Anti-Money Laundering) is an integrated approach that combines fraud prevention and AML efforts, enabling institutions to identify overlapping risks, share valuable data, and create a unified strategy for preventing financial crime.
By consolidating fraud and AML data and fostering collaboration between teams, institutions gain a more holistic view of suspicious activity. In today’s landscape, where fraud and money laundering tactics often overlap, FRAML provides a more comprehensive defence. To achieve this, institutions must focus on data integration, cross-functional collaboration, and the use of advanced AI and machine learning to detect complex threats.
Key advancements in this area are focused on integrating AI and machine learning to better address the complexities of financial crime.
As one approach, Hybrid AI combines machine learning with knowledge-based systems. This approach allows for better detection of fraud and AML violations, particularly in fast-moving environments like instant payments, while reducing false positives.
Success in adopting a FRAML approach for financial institutions centres on several key steps. Integrating data from both fraud and AML functions is essential, as centralised data allows for better pattern recognition and faster, more accurate risk detection. Investing in advanced technologies like AI, machine learning, and hybrid systems is critical for real-time detection and reducing false positives, particularly in fast-moving transactions.
Equally important is cross-functional collaboration between fraud and AML teams, ensuring shared communication and joint strategies that lead to more efficient detection. Institutions should also focus on proactively monitoring emerging threats by regularly updating detection algorithms. A balance between automation and human oversight is key, with automated systems handling routine tasks and human expertise required for complex cases.
Of course, AI plays a major role also in this domain. With its ability to combine the computing power of machine learning with human oversight, Hybrid AI approaches are at the forefront of any technological solution offerings. In addition, real-time monitoring is essential for quick anomaly detection, especially in fast-paced environments like instant payments which are becoming more and more dominant. And lastly, automated case management streamlines investigations, improving efficiency and ensuring traceability.
What is important with any of these elements is that their implementation needs to be integrated across the organisation, inter-departmental, and across channels. A fraud investigator needs to be aware of any sanctions’ violations, and vice versa.
An investment in the proper technology infrastructure is good and essential. However, to successfully implement FRAML, financial institutions must adopt a holistic mindset that views fraud and AML as complementary efforts. This requires breaking down silos and fostering cross-functional collaboration where teams share data and insights. Leadership should promote a culture of continuous learning and adaptability, ensuring staff stay agile in the face of evolving threats. Without this mindset shift, even advanced technologies will fall short.
This editorial was first published in The Paypers' Next-Gen Technologies to Detect Fraud and Financial Crime Report 2024. The report explores how banks, fintechs, and PSPs are using AI and emerging technologies to detect and combat sophisticated fraud.
INFORM operates across various industries, including finance, telecommunications, insurance, aviation, automotive, logistics, manufacturing, and retail. INFORM employs over 1,000 staff from more than 40 nations.
INFORM is a global pioneer in the field of AI-powered optimisation of business processes and intelligent decision-making. This makes the company a leader in providing smart, Hybrid AI-powered fraud prevention and AML compliance solutions. With RiskShield they offer a multi-channel platform that detects and manages suspicious activities, minimising losses and optimising efficiencies using advanced analytics, machine learning, and intuitive rule management controls.
Every day we send out a free e-mail with the most important headlines of the last 24 hours.
Subscribe now