Companies tend to keep their fraud and AML teams separate, but could it hinder your fraud prevention?
In 2021, 85% of financial institutions reported uncovering fraudulent activity in the account opening process. It’s an expensive problem for US banks—new account fraud was estimated to result in $3.5 billion in losses in 2021.
Banks are simultaneously struggling to combat money laundering. In 2019, 60.5% of the banks’ fines were due to anti-money laundering regulations violations.
As costs continue to mount, many financial institutions and businesses seek to revamp their approach to these crimes. It’s clear that historical approaches aren’t sufficient to combat the evolving methods used by money launderers and cyber criminals.
FRAML presents a compelling approach to addressing these risks. FRAML unites the best practices of fraud mitigation and anti-money laundering using advanced AI technology. Here’s what you need to know about FRAML and the future of combating fraud and money laundering.
Fraud vs. money laundering
Fraud and money laundering are two crimes often linked to one another. Fraud is defined generally as the wrongful or criminal act to deceive someone for one’s own financial or personal gain. Legal definitions of fraud vary across countries, at the federal and state levels in the US, and even among states. However, most have, at their core, the use of deception to make a gain by unlawful or unfair means.
Many types of fraud exist, but all forms share that the perpetrator knowingly receives a benefit to which they’re not rightfully entitled. This benefit may be financial gain but could also be the acquisition of other benefits, such as obtaining a driver’s license, a passport, or other travel documents, or qualifying for a mortgage by using falsified documents or making false statements.
Fraud can also be executed in pursuit of money laundering. Money laundering is defined as: “The funneling of cash or other funds generated from illegal activities through legitimate financial institutions and businesses to conceal the source of the funds.”
The prevention of fraud and money laundering is shared across the organization. Anti-money laundering (AML) is often primarily handled by compliance, whereas fraud is the purview of security. But, in reality, criminals—and customers—don’t make this distinction. Hence the need for a new approach: FRAML.
What is FRAML?
FRAML stands for Fraud and Anti-Money Laundering. This term describes the convergence of fraud and compliance to fight financial crime.
“A typical enterprise has both fraud and compliance departments. The fraud team is primarily responsible for fraud losses, while the compliance team helps the organization to stay on the right side of financial crime legislation, most notably the regulations that govern money laundering and tax evasion,” wrote one expert.
However, keeping these teams separate can prevent successful anti-fraud and AML efforts. While these departments have different goals — the fraud department aims to reduce loss, while compliance works to meet regulatory standards — they have many common goals, such as:
- To minimize the impact of financial crime on customers
- To protect the organization’s reputation
- To improve efficiency and keep costs low
- To operate in compliance with industry regulations
Likewise, the departments require the same information and often take similar action when there’s a suspected crime. Companies and financial institutions that break down fraud and anti-money laundering siloes run more effectively.
“The fraud and compliance functions need to come together and take a holistic approach to the people, processes, and solutions they use,” said Matt Cox, senior director of fraud, cyber and compliance Europe, Middle East and Africa (EMEA), at FICO. “Then when the customer opens an account, or spends or moves money, the bank can check for money laundering and potential fraud at the same time.”
AML fraud detection solutions and best practices
The need for AML fraud detection solutions that combine the efforts of these two functions has never been greater. According to a report from PYMNTS.com, “Criminals have taken advantage of the gap between the development of modern ways to shop and bank, and marketplaces’ adoption of cutting-edge anti-money laundering (AML) and anti-fraud technologies to launder trillions in funds yearly.”
Combatting fraudsters and money launderers starts by designing a unified FRAML hub to address all relevant fraud and noncompliance risks. Organizations can strengthen their FRAML function by sunsetting legacy tech that hasn’t evolved to meet the new threat landscape. Today’s top AML fraud detection solutions use advanced behavioral analytics powered by AI with real-time data to identify behaviors that carry a high risk for money laundering or fraud.
FRAML best practices
Modern FRAML approaches and fraud risk management require five elements — identification, quantification, monitoring, optimization, and performance.
1. Identification
Regularly review data such as device identities, customer behavior, and cross-channel transactions to identify money laundering and fraud vulnerabilities. This data can also be used for real-time risk scoring.
2. Quantification
Bring together all transactions in a single dashboard to gain access to granular risk vulnerability insights and for better long-term risk modeling.
3. Monitoring
Unify channel transaction monitoring at scale and apply FRAML strategies to new fraud risks as they emerge, such as deposit fraud.
4. Optimization
Find a solution built to grow with your company that retains your analytics structures as the business implements new AML and anti-fraud practices over time. This reduces the drain on resources to learn and integrate new tools constantly.
5. Performance
AML and anti-fraud tools should be able to amplify security, compliance, and AML protections without sacrificing the user experience.
What do these elements look like in practice? Fraud.net’s Transaction AI and Application AI are examples of solutions that take a layered, holistic approach to FRAML. These tools are part of Fraud.net’s all-in-one customizable toolkit designed to update and expand with your business.
Transaction AI offers real-time, continuous fraud and AML transaction monitoring. It empowers organizations to stay ahead of fraudsters by tracking and trending suspicious activity with easy-to-use tools. The platform’s AI engine can extract billions of insights from your unique data sources, so you can better detect anomalies in your customer data and prevent fraud.
Transaction AI shares real-time risk scores and alerts for every transaction, enabling your team to stop fraud and/or money laundering before it can occur. One organization achieved a 66% decrease in time spent investigating fraud within the first 90 days of implementing Transaction AI.
Coupled with Application AI, these tools provide a powerful FRAML solution. Application AI gives a real-time risk assessment of applications to verify legitimate customers while stopping fraudulent ones before they can cause further harm. Application AI can authenticate account openings, loan applications, bank accounts, credit card applications, and merchant and supplier onboarding. This module solves everything, from duplicate and fake accounts to AML & KYC to credit checks.
Fraud.net’s solutions are designed for each stage of the transaction lifecycle. Learn more about how Fraud.net can help you build a comprehensive FRAML approach by signing up for a demo today.