The State of Fraud and Financial Crime in the U.S.
Volume, value and false positive benchmarks for financial institutions in America, 2021 - 2022.
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Volume, value and false positive benchmarks for financial institutions in America, 2021 - 2022.
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“Without standardized fraud reporting, it is extremely difficult to understand the extent and nature of fraud across the industry. The Federal Reserve worked with the industry to develop the FraudClassifierSM model to help organizations speak the same fraud language within organizations and foster a common taxonomy across the industry. This study leverages the model’s holistic approach to classifying fraud involving payments, looking at both authorized and unauthorized party fraud. Benchmarking studies such as this one not only help in understanding a comprehensive picture of fraud today, but also how the trends are evolving over time.”
Mike Timoney, Vice President of Payments Improvement, Federal Reserve Financial Services
With a lack of standardized reporting to analyze fincrime trends in the market today, financial institutions in the U.S. have struggled to outsmart fraudsters and criminals.
Featurespace partnered with PYMNTS to create the most trusted benchmark of financial crime and fraud challenges, trends and prevention performance, by the industry, for the industry.
We interviewed executives across 200 U.S. financial institutions to create the first ever benchmark for volume and value of fraud and financial crime, using the FraudClassifier model from the Federeral Reserve to ensure accurate and relevant data.
Now, for the first time, financial institutions have the fraud and financial crime trends data they need to help make the world a safer place to transact.
Combatting fraud and money laundering is becoming increasingly challenging for financial institutions (FIs). Volumes of fraud continue to rise and losses are increasing for FIs of all sizes, alongside this, the regulatory burden is increasing as nations tackle the impact of fraud on the consumer, and financial crime on the economy.
For the first time, we have a definitive understanding of the trends in fraud and financial crime (especially value and volume of fraud losses) affecting FIs in the U.S. Banks and credit unions of all sizes are affected, but not equally. Smaller and medium-sized FIs are experiencing particularly high rates of fraud, and larger institutions are spending heavily to combat the threats. The current total cost of fraud and financial crime, when we consider FTEs and operational costs alongside losses, is unsustainable for FIs managing cost to income ratios in a volatile global economy.
What’s clear from the research is that FIs recognize the role that technology modernization plays – particularly the implementation of machine learning – in achieving operational efficiencies that can help prevent a tidal wave of fraud and financial crime from overwhelming their organizations. What’s also clear is that the frequency and intensity of attacks are on the rise to such an extent that some executives have become hesitant to innovate. This has led to a kind of frustrated paralysis among some executives in the U.S. The reason is not passivity, but a pervasive sense of overwhelm amongst concerned people.
As an industry we need to move faster, and together. The research highlighted a need for fraud, Anti-Money Laundering (AML) and data science professionals to talk and collaborate more effectively on strategies and technology to innovate.
Even with important advancements such as the FraudClassifier Model in the U.S., there continues to be a lack of standardization in fraud reporting across FIs. This creates challenges when it comes to assessing the true cost of fraud and identifying new attack points, as well as inhibiting collaboration with international partners to prevent crime from migrating across borders.
The rising volume of fraud attacks necessitates innovation to outsmart organized criminal networks. Releasing resource for innovation is challenging when daily operational and regulatory demands are so high. Competing priorities for investment make it difficult for individual FIs to combat new fraud trends effectively.
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Enter issuing, acquiring, network and processing partners who are offering innovation as a service. The next innovations in their platforms are fraud prevention and Anti-Money Laundering (AML) as a Service.
The global rise of digital transaction volumes has created great opportunities for service providers, and as fraud and financial crime has increased in lockstep they are now providing scalable and efficient fraud prevention and AML solutions to financial institutions. For Payments as a Service providers, anti-fraud and compliance investments not only help manage operational cost but can even be monetized.
“Experience in implementations, processing platforms, integrated data, data science, and model governance is critical and cannot be accomplished with AI and machine learning alone. Issuers look to purchase a platform they can rely on, and they trust TSYS and Featurespace experts who have a track record in successfully deploying and maintaining the advanced adaptive machine learning model.”
Maria Adele Di Comité, Research Director IDC Financial Insights
Scams now represent 2 of the top 5 categories of fraud volumes and losses for FIs, yet scam typologies are almost impossible to prevent effectively with traditional rules-based systems. The market must act now to mitigate rising risk, particularly as liability and reimbursement regulation looks set to change in favor of consumer protection.
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FIs in the U.S. need to modernize fraud prevention systems to combat rising scams if they want to remain compliant, preserve customer experience, and reduce losses. Scams may take many forms such as investment, romance, or impersonation but the one thing they all have in common is that they target the customer to initiate and authorize the transaction.
Protecting against these is therefore a different challenge to traditional fraud typologies, and legacy approaches can result in increasing false positive ratios. Machine learning, and in particular adaptive machine learning, is crucial to accurately identifying when a customer is acting out of character including when they are authorizing the transaction, and even if their behavior has changed over time.
“Scams are a particularly challenging threat for financial institutions because by bypassing the institution’s authentication controls, the attacker forces the institution to rely on an incomplete picture of the potential risk of a payment. In order to accurately and effectively detect scams, financial institutions would need to be able to predict why a customer is issuing a payment order as opposed to how the customer is issuing a payment order. Unfortunately, the level of sophistication required to accurately predict a customer’s intention does not presently exist in a manner that can be considered to be “commercially reasonable” which is the legal standard that financial institutions are required to meet in the U.S. market.”
Trace Fooshee, Strategic Advisor Aite-Novarica Fraud & AML Practice
Historically separate entities, fraud and AML professionals now have more in common than ever, highlighting the same challenges and investment priorities when it comes to making the world a safer place to transact. Volumes, values and validity reign, with managing the false-positive ratio a key component of both fraud and fincrime strategies. Fraud and financial crime metrics include:
Greenfield technology environments make implementing these combined fraud and Anti Money Laundering (AML) platforms much simpler than in legacy environments. In traditional financial institutions, where fraud and AML teams operate independently, executives specializing in money laundering are among the most likely to give high priority to innovation, but struggle to overcome technology debt and legacy constraints. A combined strategy and investment could be the answer to this challenge.
Fraud and AML professionals are prioritizing the same technology modernizations – cloud and machine learning – and as such, converging into a holistic fraud and financial crime platform is more commonplace amongst FIs looking to innovate and manage the Total Cost of Ownership for their fraud and AML operations.
“Unified EFM and AML risk scoring is all about unusual/anomalous pattern detection. Whether heuristic (rules-based) or AI (machine learning [ML]-based), developing ethical, responsible, and properly version-controlled transaction risk scoring models is expensive. FRAML convergence allows FIs to use a single, integrated platform for authoring rules, developing, training, and verifying AI/ML models at a 20% to 30% lower cost, in Forrester’s estimation. AML model effectiveness monitoring can help FIs identify declining AML model performance but will also help explain lagging EFM model performance. Combined model governance in FRAML also improves model efficiencies.”
Top Trends Shaping Fraud Management And Anti-Money Laundering – Forrester, August 6, 2021, by Andras Cser, Vice President, Principal Analyst
Implementing innovations is the secret to outsmarting risk. Financial institutions that launch innovative solutions before others experience the lowest average fraud losses in BPS by far, compared to those who wait even a short time. That’s because fraudsters are innovating too, constantly testing FI’s defences and changing tactics once previous scams, schemes and typologies no longer work. Fraud and financial crime prevention teams have to innovate ahead of the criminals if they want to reduce losses, and that means being first to market.
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Within technology strategies, the use of cloud to aid innovation and speed to market is a clear benefit. And organizations who prioritize cloud experience lower rates of transactions resulting in fraud losses. Cloud is also a path to simplification for FIs whose fraud and financial crime technology stacks have become unwieldy and ineffective.
Bigger institutions use, on average, 3.5 different technologies and report the highest total cost of fraud. So in fact, the answer to innovation is the right technology strategy, leveraging the cloud for speed and simplicity.
“Risks can be mitigated through data analysis, although fraud will always be with us as criminals seek to get around every new control. To help counter this threat, continuing to invest and quickly implement new technology and detection methods will always be of the highest importance.”
Adeola Adebonojo, General Counsel and Chief Risk Officer at Contis
The volume and velocity of regulation impacting fraud and financial crime teams continues to grow. However, with daily operations and compliance demanding so much time and investment, releasing resource for innovation is a struggle. In fact, regulatory constraints are the top factor inhibiting financial institutions from innovating. 60.5% of FIs cite regulatory constraints or difficulties as factors inhibiting innovation or adding new features to existing solutions. This concern is echoed in FIs of sizes and innovation appetites, and by both fraud and AML professionals.
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The challenges around balancing compliance and customer experience become clear when we consider that financial institutions which experience higher rates of scams (such as relationship fraud) are also more likely to see regulatory constraints as an inhibiting factor for innovation.
The potential cost of new reimbursement mandates for victims of scams threatens to overwhelm FIs who are already struggling to invest in the innovations needed to thwart these new fraud typologies. Mid-sized financial institutions are perhaps the hardest hit by the challenges of regulation, especially when coupled with higher-than-average fraud rates, losses, and costs.
FIs highlight 3 key inhibitors to innovation from a regulatory perspective
Robust regulatory frameworks are one way to reduce the burden of compliance and encourage innovation in line with national priorities. Read the four enhancements Featurespace recommends to the Bank Secrecy Act and Anti-Money Laundering Regulations to deliver on the national priorities including for fraud.
“Regulatory oversight is key to protecting citizens and economies but must be balanced to ensure the smooth functioning of financial institutions (FI) operations for the benefit of 99.99% of their customers. At times, the volume of new regulations, particularly in relation to fraud prevention and anti-money laundering, can overwhelm an FI’s frontline systems and severely hinder the transacting of huge volumes of legitimate business. Although technology innovations, such as machine learning, offer the opportunity to optimize and automate compliance, current frameworks are failing to keep pace. Regulators should provide more specificity for FI’s aiming to balance cost, customer experience, and compliance through innovations. To achieve this there ought to be greater collaboration between industry and regulators to ensure the best designed regulation is implemented to meet national priorities, while remaining efficient and effective.”
Peter Radcliffe, Chairman P20
Many decades ago in the 60s and 70s when credit cards were invented, the vast amount of financial service transactions were face to face. Think of all the bank branches that used to exist. It was the bank branch where people went to get cash. Then they would use that cash or checks to buy groceries at the local store. Many people would also walk to the electric, water, or gas utility company to pay their bills at the designated window of the utility company. They would pay their dentists and doctors in check or cash when post procedure. Travelers checks were in common use for international or domestic traveling. Additionally, cell phones did not exist, and computers were kept in large, air-conditioned rooms with layers of physical security of access. In all of these cases, the interaction between the bank and the customer and between the retailer and the customer were physical and as such identity could easily be verified.
Now consider how the world of payments has changed. Credit cards became a dominant form of payment. ATM cards became debit cards, and their growth in consumer use and adoption was many times faster than that of credit cards, cash or checks. Cash registers became point-of-sale terminals. Cell phones became massively successful, easily used by all types of customers. Physical purchase volume growth was strong, but became smaller than Card Not Present (CNP) transactions because network capacity and the internet facilitated these transactions. Additionally, banks and non-bank financial service providers as well as retailers saw the operational efficiencies of online financial transactions. However, that came at a different cost. That cost was exponential increases in fraudulent transactions. Simply put, because you don’t physically see your customers anymore, you have to model their identity and behavior digitally in order to properly protect against fraudulent transactions. This modelling has to help determine, in microseconds, whether a transaction is or is not fraudulent.
At the same time, fraudsters are not lingering on dark street corners. They are extremely technically adept and well-organized (illicit) businesses. They make a living at the expense of weaknesses in the payment systems. Many times they are so adept that they know how to bypass arcane fraud models and legacy bank transaction systems. This translates into excessive and direct costs to protect the payments system; and it irreversibly harms the reputations of those firms that are attacked. Regulators are extremely focused on these cost and reputational issues, and have the power to mandate fines and remediation requirements which are difficult and expensive.
Featurespace recognized this complicated problem many years ago. Featurespace is the leading fraud and financial crime prevention innovator in the world for large banks, retailers, non-bank financial service providers, and processors who provide transaction processing support to banks, both large and small. This paper clarifies many of the mysteries and facts surrounding this complicated fraud issue, as well as clearly highlighting the critical need for collaboration and modernization of anti-fraud and anti-money laundering technologies and strategies.
Combatting fraud and financial crime starts with understanding the size and shape of the challenge.
For the first time, financial institutions now have the fraud and financial crime trends data they need to win the fight to make the world a safer place to transact. Featurespace partnered with PYMNTS to create the most trusted benchmark of financial crime and fraud challenges, trends and prevention performance, by the industry, for the industry.
