Point of View

When it comes to anti-money laundering and anti-fraud, together is better

The time is right for consolidation under one anti-financial crime umbrella

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Fraud, or any scheme to defraud, is a predicate crime for money laundering. Conversely, of course, many money launderers commit fraud. So, bottom line, where there is fraud there is money laundering and, often, vice versa. For this reason, the Financial Crimes Enforcement Network (FinCEN) has issued advisories and guidance on various fraud-related activities, such as mortgage fraud, identify theft, tax-refund fraud, healthcare fraud, and elder abuse. Even when there is no known connection between a fraudulent transaction and money laundering, financial institutions have an obligation to file a suspicious activity report (SAR) relating to the fraud, assuming the transaction meets a minimal threshold.

Historically, the anti-money laundering (AML) compliance area (which naturally rolls up to the chief compliance officer) and the anti-fraud department (which typically is housed in the chief risk officer’s domain) have viewed their missions as separate ones, in spite of the fact that they perform similar work, centered around discerning patterns that may indicate a problem, investigating system-generated alerts, and identifying bad players. Ensuring that money is not lost through fraud, which requires real-time solutions, typically was seen as a key component of the core business process. Identifying a case as fraudulent is easier, as the customer will report or verify it.

A historical division between AML and anti-fraud has morphed into a cultural and mindset split leading to disconnects and data silos

Conversely, AML was considered a regulatory-driven cost center, wherein identification of suspicious activity might be difficult and audit trail and documentation were critical. The two areas often did not communicate, work together, or share case management or monitoring systems, and competed for budget, resources, and senior management attention. Thus, it typically was the case that anti-fraud investigators did not know that a person was also being investigated for money laundering, and vice versa.

In recent years, there has been a healthy trend toward combining the two functions (plus cybersecurity) under an enterprise-wide “anti-financial crime” umbrella. Indeed, third-party providers that traditionally had distinct AML and anti-fraud solutions increasingly are offering platforms, monitoring systems, and case management tools that more readily integrate with fraud prevention. This trend has been encouraged by regulators, including FinCEN, which expects financial institutions to promote “communication and collaboration among internal AML, business, fraud prevention, and cybersecurity units.”

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Combining AML and anti-fraud functions has been encouraged by regulators as they expect financial institutions to promote communication and collaboration among internal AML, business, and fraud prevention units

Merging the two functions takes advantage of several synergies. A key one is that much of the data required to detect money laundering is the same as that needed to prevent fraud. For example, similar product types, such as international wire transfers and stored-value cards, are typically considered high risk and monitored more closely. Also, delivery channels, such as online and remote access, are higher risk for both money laundering and fraud. AML and anti-fraud also leverage the same transactional parameters, account and customer information, peer group definitions, watch lists, and certain scenarios or detection models. From both business and compliance perspectives, there is great value in a united database that provides a holistic view of a customer’s total relationship with the bank (ie all products, transactions, accounts in different lines of business, and more, are visible) and any concerns that the relationship raises. 

AML know-your-customer procedures and documentation of customer’s expected activity can serve as an important fraud tool. Moreover, AML and anti-fraud programs share many policies and procedures, including referral of information to law enforcement, termination of customers for inappropriate activity, and due diligence monitoring. Both teams use similar tools and protocols for completing workflows and resolving cases, and alert and case analysis and investigations leverage many of the same skill sets. 

AML and anti-fraud professionals tend to be knowledgeable in similar laws and adept at conducting research and complex analytics, interviewing people, and writing comprehensive eports.

The potential to eliminate redundancies by combining AML and anti-fraud efforts is apparent. Moreover, such consolidation can lead to more targeted and actionable alerts and investigations, with all alerts for the same subject being displayed to the analyst. With more information available, investigators are able to reach resolution more quickly. Also, fraud detection rates improve, as well as the ability to better identify sophisticated schemes. For example, multiple low value events may not be registered as frauds, while an enterprise-wide system aggregates exposure and helps detect patterns.

Consolidation of AML and anti-fraud efforts can lead to more targeted and actionable alerts and investigations, with all alerts for the same subject being displayed to the analyst

Moreover, there are potential cost savings realized through more efficient use of resources, including systems, data management, audit consolidation, reduction of IT staffing costs and software maintenance fees, and elimination of duplicate alert reviews and case investigations. 

AML and anti-fraud units working in a coordinated fashion facilitate easier hand-off of alerts, cases, and investigations and greater sharing of leads and information. This should result in, among other things, fewer duplicate SARs and more thorough SAR filings. The breaking down of silos can also lead to better identification of fraud and money laundering schemes that cross channels, products, and lines of business, and therefore greater visibility for management through aggregated reporting, providing a more holistic enterprise-wide view.

Additionally, there are opportunities to cross-train, mitigate the risk of inadequate coverage, and facilitate load balancing across individual units. Consolidation enhances the ability to develop more well-rounded analysts and investigators, as well as to improve employee morale and retention by providing more opportunities for learning and advancement. For example, the institution would have “financial crime analysts” responsible for reviewing both AML and fraud alerts rather than AML or anti-fraud analysts.


Of course, challenges exist. One might be described as cultural. Individuals in an AML group tend to have a legal and compliance background, while those on the fraud side generally are more operational. Nomenclature and sense of identity are different. Leadership from one discipline may lack the knowledge and experience to manage the other area effectively. And management may see one program as more important than the other, leading to insufficient allocation of resources. An AML leader might well worry that AML could be overshadowed by an urgency to reduce fraud losses.

The list of challenges extends to the need to merge and redesign processes, and recognizing and accommodating
necessary differences. Combined AML and fraud management systems require renewed integration with host systems, a case management system that supports an integrated approach, while still providing the specific workflows vital for AML and fraud analysts. 

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Moreover, the institution must leverage technology across disciplines. In this regard, financial institutions continue to ramp up their efforts to use artificial intelligence and digital tools for a myriad of purposes, including reducing false positives, rendering customer segmentation and alerts generation smarter, and automating manual investigative processes. This move to more real-time and intelligent decisioning and workflow should be coordinated between AML and anti-fraud to best take advantage of available synergies.

The list of challenges extends to the need to merge and redesign processes, recognizing and accommodating necessary differences, and renewing integration with host systems. And the institution’s case management system must support an integrated approach, while still providing the specific workflows vital for AML and fraud analysts

The way forward

So, how can an institution maximize the benefits of AML and anti-fraud consolidation, while minimizing cost, burden, and inefficiency? The answer is – proceed slowly and methodically. A move to combine the two groups may not be right initially. It would require, among other things, creating a single transaction monitoring system running both fraud and AML rules and an entire new set of policies, procedures, and processes.

A better tactic might be to keep the groups separate, but formalize the relationship, with both areas reporting to the same senior executive. There would be regular meetings between the two groups to discuss strategies, provide feedback for improvement, and share information about cases. The institution would maintain separate databases and transaction monitoring systems, but now AML and fraud staff would be specially trained to look out for, understand, and share information and red flags for the benefit of both departments. If an AML alert does not turn out to be positive, it still could indicate the presence of fraud, and vice versa. 

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In the financial crime compliance programs there may be a reponderance of data misalignment. RPA can alleviate this issue by quickly and accurately formatting data into a single, standardized system

This approach would also allow for gradually developing a unified case management system that facilitates appropriate hand-off of files between analysts and investigators.

Other steps useful to maximize the benefits of this transformation journey include:

  • Conducting a data assessment to ascertain quality, identify underutilized sources, and determine how to close any gaps
  •  Conducting an assessment of the alert remediation process to fully understand the workflow architecture and identify additional opportunities to embed artificial intelligence and other digital tools
  •  Developing additional management information system (MIS) and other performance tracking key performance indicators (KPIs) to create enterprise-wide managerial measures of success and enable improved benchmarking
  • Creating a centralized rules engine that is more dynamic and real-time, with existing rules optimized and new rules introduced to cover all current vulnerabilities

Importantly, technological innovations can aid the convergence and make compliance resilient, responsive, and sustainable. For example, digital solutions, such as robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI), can assist in data conditioning and consolidation. In financial crime compliance programs where the AML/anti-fraud divide is severe, typically there is significant data misalignment. RPA can alleviate this issue by quickly and accurately formatting data into a single, standardized system.

Data conditioning can be further improved by implementing AI, which can be used to fill in missing information, a problem which plagues both AML and anti-fraud units. These gaps, whether they are found in internal forms, transaction details, or third party subscriptions, can be confounding to lower level investigators. Utilizing AI to enrich client data and subject profiles will result in fewer false positives, reduced cycle time, and the alleviation of repetitive work that drains morale and causes investigator burn-out.

Consolidating AML and anti-fraud would require creating a combined monitoring system, coupled with an entire new set of policies, procedures, and processes. Financial institutions must proceed slowly and methodically to maximize the benefits of consolidation

Moreover, by creating a single stream and more accurate alert flow, investigators and managers will be better able to approach their workflow in a properly risk-weighted or risk-segmented manner. Such a reimagined case management solution, which might incorporate intelligent optical character recognition, natural language processing, and computational linguistics for meaningful extraction and analysis of unstructured information, would allow for faster and more accurate decisioning and improved agent and customer experience. Additionally, through modern digital interventions, AML and fraud case management can be intelligently augmented, allowing for streamlined case allocation, tracking, and suggested cross unit communication. This would result in decreased information silos, further reduced cycle times, and the potential to intelligently automate the dreaded “task trackers,” which plague managers and team-leads alike. These efforts can further inform thematic or key risk indicator reports, helping senior management to quickly understand the burdens and successes of their various units.

Furthermore, if these innovations are implemented in a way that allows for their underlying principles to be easily understood, key stakeholders can opt to onboard and offboard new vendors and third-party data subscriptions with the confidence that these backend systems can be adjusted with minimal disruption to BAU.

The bottom line is that consolidation of an institution’s AML and anti-fraud areas reflects the modern trend, meets regulatory expectations in a better fashion, and offers the opportunity to achieve a number of significant synergies.
But it must be approached carefully and thoughtfully, incorporating modern digital tools and developing agile risk management frameworks that allow the ability to anticipate and act at speed and achieve greater compliance in an increasingly complex global banking environment.

Through modern digital interventions, AML and fraud case management can be intelligently augmented, allowing for streamlined case allocation, tracking, and suggested cross unit communication

How Genpact can help? 

Genpact helps banks consolidate AML and anti-fraud functions into a single, enterprise-wide program with platforms, monitoring systems, and case management tools that bring together data and talent and take full advantage of modern digital technology. We can apply either an integrated, end-to-end solution or individual modular offerings for banks across the value chain – from strategy to transaction detection, alert triage, investigation, and SAR filings – to help drive targeted AML and anti-fraud outcomes. These offerings include Smart Investigator, our case management solution, which can reduce cycle time while eliminating backlogs, and Smart Data Aggregator & Modeler, our data engineering and model development solution, which can reduce data aggregation times and cut false positives without losing critical data. To learn more, contact our team.