A quick guide to fraud detection & prevention in banking

Fraud detection and prevention are two of the most critical competencies in banking today. The ground reality for banks today is that firms will be dealing with thousands of fraud attempts every single month. And should a criminal succeed, the bank pays the price in reputational damage and regulatory penalties.

In many ways, fraud detection and prevention are just as vital to the long-term health of a bank as customer acquisition and retention.

This article will look at:

• The types of fraud banks have to contend with.
• Some of the methods used to detect and prevent fraud.
• How new technology enables better fraud detection in banking. 

What is fraud detection in banking?

In banking, fraud detection refers to the ability to monitor all transactions and payments in a way that helps banks accurately and quickly notice any suspicious activity worth reporting.

A bank’s ability to do this relies on a combination of:

• Technologies that can work together to rapidly reduce the workload of transaction monitoring at scale by accurately detecting anomalous patterns of activity, from account takeover fraud to Automated Clearing House (ACH) fraud and every variant in between.
• Processes that can synchronize the efforts of customer-facing and compliance teams without subjecting staff to false positives.
• People who have the time and space needed to make clear judgments based on an encyclopedic knowledge of global and local regulations.

Crucially, this core capability is critical to the bank’s wider anti-money laundering and counter-terrorist financing (AML/CTF) efforts. 

Types of fraud in banking

 A big part of what makes fraud detection and prevention so challenging for the banking industry is the sheer range of criminal activity possible. Fraud takes many forms, including:

Even beyond these established methods of fraud, the inescapable reality of the modern financial system is that there will continue to be new forms of fraud and creative crime.

Fraud detection challenges for banks

Fraud and attempted criminal activity poses a colossal, multi-dimensional risk to the banking industry. At the heart of this lies three core challenges:

• The volume and variety of fraud: Banks need to be able to monitor millions of transactions to identify thousands of instances of attempted criminal activity every month, from credit card fraud to synthetic identity fraud. Each attempt at fraud is designed to appear deviously different from the last, and compliance teams are inundated by false positives, false negatives, and everything in between. It takes sophistication, agility, and speed to tackle such a wide array of threats at the scale of billions of customer interactions.
• The impact on customer experience: As is so often the case, efforts to improve a bank’s security almost always have an impact on the customer’s convenience. Longer onboarding processes, frozen accounts, and complex authentication procedures all have a detrimental effect on a customer’s ability to move freely. But they’re essential to a bank’s ability to detect and prevent fraud. Even industry-wide developments like open banking create just as many exciting opportunities as they do vulnerabilities.
• The burden of technical debt: In many ways, banks are engaged in a long-term technological arms race against a global diaspora of criminals. But while criminals can simply adopt new technology to attempt new crimes, banks have the additional challenge of tackling new threats with older infrastructure. To reliably detect and prevent fraud, banks need to be able to balance an aggressive evolution of their technology stack with a pragmatic use of the assets they’ve already invested in.

Methods used by banks to detect and prevent fraud

 In banking, fraud detection and prevention rely on a combination of analytic techniques and technologies.

1. Analytics technology

Commonly used analytic techniques include statistical data analysis methods like parameter calculations, probability distribution and modeling, regression analysis, and data matching. But banks increasingly rely on artificial intelligence through data mining, neural networks, machine learning (supervised and unsupervised), and pattern recognition.

2. Identification technology

Technologically, banks use cutting-edge methods to authenticate, verify, and identify devices and customers. This includes more recent capabilities like behavioral biometrics and device fingerprinting, but it also includes tried and trusted methods like two-factor authentication and encryption.

3. Workflow technology

It’s also worth noting the infrastructure and software needed to take the signals generated by all these exciting technologies and turn them into an accurate stream of useful alerts for compliance officers and analysts to make sense of. 

People still play a critical role in a bank’s ability to detect and prevent fraud. So it’s vital they operate with tools and technologies that make it easier for them to prioritize and remediate the vast number of potential fraud cases affecting their organizations in an easily explainable way to auditors.

The importance of AI and machine learning in bank fraud detection

Artificial intelligence (AI) and machine learning now play a central role in helping banks combat the threat of fraudulent activity. But it’s important to note that it helps banks in a number of different ways.

First, advanced techniques for anomaly detection, identity clustering, and graph analysis allow banks to see patterns in the vast amount of data they ingest. This kind of technology is vital here because the sheer volume of analysis being conducted would be impossible if it were done manually.

Second, automation helps banks scale up their capabilities to speed up their operations. By automating the creation of alerts and suspicious activity reports (SARs) based on the organization’s specific risk-based approach, banks are able to cover more ground than they could previously. Compliance teams clear more cases more quickly, and they can do so with fewer false positives that waste time.

Third, AI and machine learning allow banks to tackle fraud more flexibly. By enabling compliance teams to integrate more data sources and create their own rules for pattern detection, AI allows the bank to evolve and improve at a rate commensurate with criminals.

Advanced fraud detection software for banks

To combat the sheer volume, variety, and ferocity of fraud attempts they’re subjected to on a daily basis, banks need powerful AI and intelligent software. When validating vendors for fraud detection, banks may choose to prioritize solutions that offer the following capabilities:

• Rapid data integration to connect multiple streams across adverse media coverage, sanctions lists, politically exposed person (PEP) lists, and ultimate beneficial owners (UBOs).
• A powerful machine learning model that’s trained on proprietary customer, company, and financial risk data to detect more than 50 types of fraud – across all payment rails.
• Unmatched speed to value with out-of-the-box capabilities and proven processes to help banks go live in as little as two weeks.  
• Advanced capabilities like dynamic thresholds, identity clustering, and graph network detection to adapt to criminals, analyze linked accounts, and track funds across the system.
• Baked-in explainability so compliance teams can rapidly share every alert response with the relevant authorities.