Interview

The importance of a holistic approach to fighting fraud and ensuring compliance

Monday 4 November 2024 10:43 CET | Editor: Alin Popa | Interview

Roy Prayikulam, SVP Risk & Fraud at INFORM, explains how the FRAML framework combines fraud and AML efforts, helping financial institutions tackle evolving financial crime more efficiently.

What is currently happening in financial services in terms of types of fraud and attacks that are plaguing the system?

In 2024, financial institutions and services are still battling a variety of fraud typologies. What they all have in common is the growing level of sophistication. Whether it is the still widespread synthetic identity fraud or authorised push payment (APP) fraud, account takeovers, or payment fraud in contactless and mobile transactions, AI-enabled schemes are driving financial crime to new levels of complexity. Business email compromise (BEC), for example, is becoming more advanced with AI, and 'Fraud-as-a-Service' is providing tools to less-skilled criminals. Meanwhile, procurement fraud, money mulling, money laundering, and identity theft remain persistent threats.

How prepared are financial institutions to tackle these fraud challenges in terms of technology, people, and processes?

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.

How can a FRAML approach tie into fighting these problems? 

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.

What are the latest technological innovations driving the effectiveness of a FRAML approach in financial services?

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. 

By analysing large volumes of data, machine learning algorithms can detect subtle patterns that indicate fraudulent or suspicious activity. This is just one side of the coin, however. When new patterns emerge, for example through the integration of a new payment method, human knowledge is fundamental. Therefore, the AI is complimented with invaluable input from experts in the system. 

‘For financial institutions to truly protect themselves from today's advanced fraud schemes, they need to abolish silos between fraud and AML teams.’

What advice would you give financial institutions in adopting this framework and reap its benefits?

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.

Are there any specific tools or solutions you believe are game-changers in this space?

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.

How should they start, and what are the common pitfalls to avoid in the implementation process?

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.

 

About Roy Prayikulam

As SVP of INFORM's Risk & Fraud Division, Roy Prayikulam brings over 14 years of experience in financial crime prevention. He has managed critical projects for top financial institutions in more than 25 countries worldwide. His expertise spans business development, project management, and risk management, Roy’s consultative approach has benefited prominent card processors in the Netherlands and Germany, establishing him as a key figure in the industry.

 

 

About INFORM Risk & 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.


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Keywords: fraud management, APP fraud, account takeover, artificial intelligence, AML, KYC
Categories: Fraud & Financial Crime
Companies: INFORM
Countries: World
This article is part of category

Fraud & Financial Crime

INFORM

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