Why FRAML Makes Sense for Certain Markets

Recently, I have been following many conversations on social media about the FRAML and concerns around grouping two historically distinct capabilities, but FRAML, integrating fraud detection and Anti-Money Laundering (AML) strategies, is becoming increasingly popular in specific markets, particularly among smaller and mid-sized organisations. While Fraud and Anti-Money Laundering are traditionally handled as separate functions, the growing overlap in tools, technologies, and data necessary for effective monitoring has sparked reconsidering whether a unified approach is more practical. Financial institutions constantly manage threats from fraudsters and money launderers, often using similar datasets and technologies. As regulations become more sophisticated and criminals more creative, the lines between fraud and money laundering have blurred, making convergence an attractive proposition.

The primary idea behind FRAML is efficiency. Both fraud and AML teams have historically operated in silos with their technology, processes, and expertise. However, the need for enhanced data analytics, shared intelligence, and reduced operational costs has led some organisations to consider a combined approach. In markets where reducing customer friction while maintaining compliance is crucial, FRAML offers a pathway to streamline operations without compromising detection quality or risk management.

In theory, integrating fraud and AML can sound like a one-size-fits-all solution but comes with complexities. Some in the industry argue that fraud and money laundering require separate expertise, processes, and governance. Yet, merging efforts to optimise resources can be highly appealing, particularly in smaller markets or less heavily regulated environments. For these markets, FRAML isn’t just about cost efficiency; it’s about building a more robust, more agile defence system to catch bad actors before they exploit gaps in fraud and AML defences.

The industry is healthyly debating whether this convergence is appropriate for all institutions. Large, highly regulated organisations may see more risks than rewards in this approach, while smaller and medium-sized businesses often stand to benefit more significantly. Ultimately, the decision to integrate fraud and AML functions depends on an institution’s size, regulatory obligations, and available resources.

Despite growing interest in FRAML, there is significant pushback from various fraud and AML industry sectors. One concern is that fraud and money laundering are fundamentally different issues requiring specialised knowledge, tools, and techniques. Fraud detection focuses on stopping immediate financial loss, while AML efforts aim to identify and prevent illicit financial flows, often involving complex layers of transactions. This difference in focus means each area demands distinct expertise. Some professionals worry that merging the two functions could dilute this expertise, leading to inefficiencies or missed red flags in both areas.

Another concern is that merging fraud and AML operations could complicate governance and oversight. In regulated industries, compliance functions are typically held to very high standards, and each area operates under its distinct regulatory framework. For example, AML programs are heavily scrutinised by regulators, requiring stringent Know Your Customer (KYC) rules, sanctions screenings, and suspicious activity reporting. Fraud detection, while necessary, tends to have more flexibility. The fear is that combining the two could create tension between operational efficiency and regulatory compliance, with one area potentially overshadowing the other.

Lastly, a combined FRAML approach risks creating “tunnel vision,” where efforts to tackle one issue might overshadow another. For instance, a firm might overly focus on stopping fraud and miss less obvious money laundering schemes, or vice versa. This risk has made some organisations wary of adopting FRAML, especially those with dedicated teams with deep expertise in each area.

Due to the scale and complexity of their operations, keeping fraud and AML separate often makes sense for large, regulated entities. These organisations face stringent regulatory requirements, particularly for AML, where non-compliance can result in substantial fines and reputational damage. Maintaining dedicated teams ensures that each area receives the specialised attention it requires, with experts focused solely on fraud detection or AML compliance.

Moreover, large institutions often deal with vast amounts of data across numerous business lines. It’s more practical for these organisations to maintain separate systems and processes, each tailored to the unique demands of fraud prevention and AML compliance. They have the resources to invest in advanced tools for both areas, ensuring the depth of expertise needed to handle their operations’ complexities. For these institutions, the risks of losing depth in either area outweigh the potential benefits of combining them.

On the other hand, small and medium-sized entities often lack the resources to maintain separate, specialised teams and systems for each function. Combining fraud and AML efforts under a unified FRAML framework offers clear advantages for these organisations. Merging these functions reduces operational redundancies and streamlines processes, making it easier to manage compliance and risk with fewer resources. They can enhance detection capabilities by leveraging shared data and technology without doubling costs.

The overlap between fraud detection and AML is more manageable in smaller entities due to less operational complexity. Both fraud and money laundering often involve similar customer behaviours, transaction patterns, and risk indicators. Combining efforts under FRAML helps these organisations build a more holistic view of customer activity, leading to better detection of illicit activities while reducing the administrative burden. This efficiency is crucial for smaller businesses to stay competitive.

Technology Costs Reduction

One of the most immediate benefits of combining fraud and AML efforts is reducing technology costs. Separate systems for fraud detection and AML can be expensive to maintain, especially for small to medium-sized entities with limited budgets. By unifying these functions, organisations can invest in a single platform that handles both, cutting down on software licensing fees, maintenance costs, and vendor relationships. This integrated approach also simplifies IT management and enables quicker technology upgrades, making FRAML a cost-effective solution as regulatory demands grow and fraudsters become more sophisticated.

Enhanced Customer Impact

Separate fraud and AML checks often disjointed customer experiences, especially during onboarding or transactional monitoring. A unified FRAML approach allows institutions to streamline customer interactions, reducing delays and improving overall satisfaction. More comprehensive monitoring reduces the chances of misidentifying customers as high-risk due to incomplete data. The smoother customer journey resulting from integrated processes is vital for retaining clients in today’s competitive financial services market.

Organisational Costs Reduction

Consolidating fraud and AML teams into one cohesive unit reduces organisational costs. Separate teams often duplicate roles and responsibilities, leading to inefficiencies. Combining the two allows for better resource allocation, cross-training, and streamlined workflows. This cuts down on administrative overhead and ensures a more effective response to fraud and AML risks. This leaner structure makes it easier for smaller organisations to operate within constrained budgets without compromising vigilance.

Synergies in Data Collection and Analysis

Fraud and AML efforts rely heavily on data, and combining these functions allows organisations to maximise the value of their data collection and analysis. Shared data streams provide a more comprehensive view of customer activity, enabling earlier and more accurate detection of suspicious behaviour. A unified approach to data also improves decision-making speed, as teams no longer need to work in isolation with separate datasets. This synergy enhances overall detection rates and boosts operational efficiency.

Advanced Usage Profiles and Detection Capabilities

FRAML can significantly enhance detection capabilities by enabling the creation of more advanced usage profiles. With fraud and AML data feeding into one system, institutions can develop sophisticated behavioural models that detect anomalies across a broader range of activities. This allows institutions to flag high-risk individuals and entities more effectively, reducing false positives and increasing the chances of detecting complex financial crimes. These enhanced detection capabilities lead to better risk assessments and more focused monitoring of potential threats.

While integrating fraud and AML efforts through FRAML presents clear advantages, especially for small and medium-sized entities, it’s not without its challenges. FRAML allows for significant cost savings, improved customer experience, and a streamlined approach to compliance for smaller organisations. The synergies in data collection and enhanced detection capabilities make this approach particularly effective for institutions with limited resources but substantial compliance obligations.

However, for large and heavily regulated organisations, the complexity of their operations and the need for specialised expertise often make it more practical to keep fraud and AML functions separate. Ultimately, integrating or separating these functions depends on an institution’s size, regulatory environment, and capacity to manage the complexities involved in each area.

The rise of FRAML reflects the evolving landscape of financial crime prevention, where efficiency and enhanced detection are critical. For many markets, combining fraud and AML efforts offers a balanced path forward that aligns cost, compliance, and security with the needs of a highly competitive financial world.