Images of cash, banks indicating how fraud & anti-money laundering become FRAML

Here’s How FRAML Can Work for Your Financial Institution

Speaking with a BSA officer last week, I posed the question, “what does FRAML mean to you?” Her response, “better customer visibility.” Truth be told, I expected the answer to be generic (i.e., a combination of Fraud and AML teams). Her response pushed me to give FRAML some deeper thought.

It feels like FRAML has been discussed around many financial crime-fighting circles over the past decade with no clear-cut definition. It’s generally perceived as combining fraud and AML operations — a prospect that can feel overwhelming to many financial institutions. But FRAML means so much more.

What is FRAML?

At its most basic level, FRAML aligns internal fraud detection and anti-money laundering (AML) operations with the goal of fighting financial crime together. But an important distinction here is that it is not necessarily about the combination of Fraud and AML teams and operations. It could be if a financial institution views that as a more effective setup; however, it does not have to be, and success can be achieved in current structures.

Rather, FRAML is about automating data sharing and insights to reduce financial crime and ensure a frictionless journey for customers — regardless of where or how customers interact with the bank. This automation enables teams to establish a holistic customer identity and view customers in a more informed manner.

Considering that we live in a world upended by COVID-19, forced into remote working arrangements, historic unemployment levels, and rampant financial crime, the need for collaboration and information sharing has never been stronger. Perhaps it’s time to give FRAML another look?

Financial criminals thrive on Fraud and AML team silos

Fraudsters and financial criminals have upped their game. According to Experian, pandemic-fueled asset finance application fraud increased by 181%. Simultaneously, money launders have targeted financial and societal instability — and profited greatly. Take, for example, the 57 people charged with stealing over $175 million from the PPP (Paycheck Prevention Program), where individuals and sophisticated fraud rings exploited social safety nets meant to protect businesses from recent economic uncertainty. And this is just a sample of the activity we know about.

In response, algorithm-based detection methods like machine learning scoring engines (or anomaly detection engines), and intelligent investigative tools, like visual link analysis (VLA), have enabled forward-thinking financial institutions to combat the growing complexities of financial crime. However, machine learning can only be at its most effective when ingesting the right data.

Omnichannel systems prevent more financial crime

In recent years, massive strides have been made to increase data visibility for these systems (and for the systems’ users). Omnichannel systems — systems that enable data and insights from multiple payment and activity channels to be brought together and used for detection and investigations — are being implemented to dramatically increase detection and investigation accuracy. Rather than leave employees to manually piece together disparate data from disconnected payment systems, omnichannel systems automate the collection of customers’ data across all products into one system — enabling analysts to make better decisions and work more efficiently.

So, how can banks continue to expand data visibility and allow fraud prevention and AML compliance teams to create a unified customer identity?

How FRAML breaks data silos

FRAML solutions aim to build valuable “risk” profiles around their customers by bringing together historically siloed portions of the business. Rather than looking at fraud risk or an AML-related risk, banks can begin to ask the question: is there any risk to my customer or my business?

Benefits of a FRAML risk profile view

Build stronger customer relationships

By understanding customer activity across the entirety of their business, financial institutions can construct an enterprise risk profile that both fraud detection and AML teams can leverage to make smarter, more informed decisions. A unified customer identity will also mitigate potential friction points in the customer experience. It reduces the need to contact customers for additional information that is available within the financial institution but was not previously known to financial crime-fighting teams. Commercial benefits are also evident. When existing customers look to expand their footprint within a financial institution, engaging new products and services, the reviews can be conducted with minimal customer disruption to deliver additional lifetime value.

Superior operations

Fraud has shown to be a key indicator of potential money laundering behavior. But fraud data is often stored on an entirely separate system and is consequently invisible to a transaction monitoring analyst attempting to review alerts. A significant effort is required to collect all of the necessary data before conducting a proper review. Breaking down operational silos, capturing data across all customer channels, and presenting that information in an orchestrated manner creates a FRAML risk profile. It also enables institutions to give the right information to the right teams at the right moment. Financial crime-fighting teams can then utilize those FRAML risk profiles to make smarter, more informed decisions, leverage economies of scale, and remain productive and nimble in their operations.

Take AI application and analysis to the next level

Earlier this year, the Association of Certified Anti-Money Laundering Specialists (ACAMS) held a session on FRAML. They referred to it as “today’s compliance power couple.” I couldn’t agree more. And financial institutions can leverage the advancements in fraud detection and prevention in the AML space. Labels, modeling, and behavioral science can shift us from detecting financial crime to preventing it. AI technologies can pull together vast amounts of data in an orchestrated manner, conduct advanced analysis, and create a FRAML risk profile in a fraction of the time required initially. And with the advent of tools like analytical APIs, financial institutions can understand performance and data gaps in near real-time.

How Financial Institutions Can Prepare for FRAML

At this point, you may be wondering if FRAML will work at your organization. Before adopting a FRAML solution, setup, and approach to create 360-degree risk profiles, here are a few questions to ask:

  1. How would adopting a FRAML solution change the way your teams interact?
    • How will the cultures blend?
    • Are there any processes or procedures that could be put in place to encourage information sharing?
    • Where is internal alignment required?
  2. Where would the touchpoints and handoff points be for your Fraud and AML teams?
    • These would have to be defined before solution implementation to ensure smooth execution.
  3. How is data currently stored between payment types and across channels? 
    • Would data migration steps be necessary?
    • It is essential to engage your technical teams to understand the size of this effort.
  4. What would broader FRAML-enabled risk scoring look like for your business?
    • What other teams could benefit from the more-informed score? Your information security teams may find this information useful.
  5. How could this translate to more targeted and relevant customer education, messages, and product offerings?
    • An end-to-end view of customer activity is a gold mine for commercial and marketing teams.

 

If we’re serious about preventing financial crime, it’s time to take our abilities to the next level. It’s time to graduate. It’s time to consider FRAML.

 

Want to learn more about how to adapt to the post-COVID financial landscape? Join banking experts from N26, Paysafe, Emailage, and Feedzai in our webinar on-demand: Understanding The Role Of AI in a World Of New Consumer Behaviors.

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  • AML
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