Fraud is more costly than ever. A 2024 TransUnion survey found that 801 business leaders reported a combined $359 billion in losses last year, accounting for 6.5% of company revenue. For community and regional banks, these trends raise serious concerns about financial risk, reputation, and long-term resilience.
Fraudsters are continually adapting to new detection methods, finding new ways to infiltrate your institution. One example is synthetic identity fraud, which accounts for nearly 7% of all business revenues lost to fraud.
Fraud tactics are evolving faster than many financial institutions can respond. Traditional detection methods often rely on outdated data and catch issues only after the damage is done.
But fraud prevention isn’t just about catching fraud — it’s about making the right decisions at the right time. Approaches like synthetic fraud are engineered to evade such checks by crafting identities that match verification data.
Fraud detection also often falls to multiple departments. While each group may have robust tools, the lack of communication between these systems creates blind spots. This allows fraudsters to manipulate one department without triggering alarms in another. For Directors, the concern is not just about individual workflows but about whether the institution has a cohesive fraud strategy that works across departments and touchpoints. This level of coordination is essential to both customer experience and fraud loss mitigation.
Finally, some assume certain types of fraud are a problem for just one part of the workflow. Take loan origination fraud. Should fraud screening happen up-front with identity verification? Should it be tied to credit risk modeling? Or should it be part of a banking strategy that validates account ownership before funding? Preventing fraud, including loan origination fraud, requires ongoing validation.
Banks and credit unions have long struggled to balance fraud prevention and credit risk management. Fraud decisions often fall to credit risk teams, who don’t always have the specialized tools to identify sophisticated schemes. The result? Fraudsters slip through the cracks, while legitimate borrowers encounter unnecessary friction.
One of the biggest blind spots is the lack of real-time fraud screening. Many lenders still rely on traditional credit risk models, missing key behavioral and identity-related red flags. Synthetic identities and credit washing can bypass approval workflows, leading to significant financial losses.
However, the challenge isn’t just about tightening fraud controls; it’s about doing so without disrupting the borrower experience. The key is embedding fraud prevention in decisioning workflows, stopping fraudsters without slowing down legitimate borrowers.
To combat modern fraud effectively, banks must move away from operating in silos. Instead, fraud prevention and credit risk management should work together, using advanced technology to detect fraud while ensuring a smooth customer experience. One regional lender recently embedded fraud screening into its origination and credit decisioning process. As a result, it cut fraud losses by 28% while reducing manual review time by nearly half. Results like this show what’s possible when fraud and credit risk are treated as interconnected parts of the same process.
For example, artificial intelligence-driven fraud detection is gaining traction — and for a good reason. AI-powered systems can detect inconsistencies in real time, halting fraudulent applications before they escalate. By leveraging data analysis and machine learning models, they adapt to emerging fraud patterns, providing a robust defense against sophisticated fraud tactics.
Banks and credit unions can also improve risk assessment by integrating fraud solutions directly into decisioning workflows, allowing fraud detection to work with credit risk models rather than as a separate, disconnected process. Lenders can assess creditworthiness and fraud risk simultaneously, reducing false declines while preventing bad actors from slipping through. This proactive approach minimizes fraud losses and enhances operational efficiency by eliminating redundant manual reviews and streamlining approvals.
Finally, layered identity verification strategies reduce false positives and streamline approvals. The strongest strategy combines device intelligence, behavioral biometrics and transaction monitoring for enhanced accuracy.
To build a strong fraud prevention strategy, banks and credit unions need to transition from outdated, siloed systems to a unified, proactive approach. This approach includes:
1. Integrating systems so data flows freely across departments, ensuring red flags raised in one area become visible throughout the organization.
2. Adopting AI and machine learning to analyze vast datasets and detect subtle inconsistencies in geolocation or device usage.
3. Analyzing activity in real time to flag suspicious behavior before it escalates.
4. Pooling data and sharing insights via consortium data sharing to quickly identify coordinated fraud attacks and emerging threats.
With the right strategy and technology, your institution can close critical gaps and strengthen its defenses.
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