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December 3, 2020 - Chandreyee Chakravarty

Mitigate Fraud Risks for Finance: 4 Real-World Case Studies

  • A top U.S. credit card issuer lost millions of dollars each year due to fraud and its attendant operational costs. In fact, in just one two-week period, the company received 2,700 fraudulent applications from one highly organized fraud ring. 
  • A well-established, global financial institution with clients in over 200 countries was victimized by a fraud ring that included over 500 fraudulent accounts set up for rapid money transfers.
  • A leading loan provider was in danger of reputational damage because rules-based fraud detection methods were creating customer friction and ruining the customer experience, an indirect, but all too real, impact of fraud.
  • One of the world’s largest online payment platforms had fraud detection systems in place, but those systems were not enough to handle the rapidly evolving threats launched by increasingly sophisticated fraudsters intent on account takeovers. 

These are not hypothetical stories. They are all too real.

Although no industry is immune to fraud, the financial industry is one of the most vulnerable to it. Now that many banking services have moved online, fraudsters are finding new ways to conduct illicit activities, including misusing authentic data to apply for loans and take over accounts.

Fraudulent activity poses significant risks for the industry, both financially and reputationally. Here’s a closer look at the risks financial institutions (FIs) face and how DataVisor is helping to improve fraud detection and prevention.

Financial and Reputational Risks of Fraud for FIs

The 2020 Identity Fraud Survey published by Javelin found that identity fraud reached $16.9 billion in 2019, with account takeovers making up a large portion of this sum. However, the dollar figure is just part of a much larger, more serious story. 

FIs and their customers alike often suffer severe consequences that are often incalculable, including the following:

Growing Financial Losses

Large volumes of fraud cases pose a direct increase to operating costs, especially if each fraud case must be reviewed manually. The resulting loss of efficiency means that valuable resources are diverted from growth initiatives, which has an immeasurable impact across the enterprise.

Poor Customer Experience

Because of increasing fraud, prevention measures may end up thwarting legitimate applications and transactions and create more friction for the customer. This not only leads to frustration for the customer but can also pose reputational risks for the FI. In short order, increased customer frustration leads, in turn, to customer attrition.

Lack of Real-Time Response

Many FIs take a reactive approach to combat fraud. By the time they catch on to ATOs or loan fraud, financial damage has already been done. If a customer becomes victimized by fraud before you can catch it, your reputation might also take a hit.

Why Traditional Fraud Models are No Longer Sufficient

FIs already have fraud detection methods in place. The problem, however, is that many of these methods are reactive and do nothing to catch fraud occurring in real-time, or better yet, prevent fraud from happening in the first place. This is often the case when fraud detection is largely reliant on manual review methods. 

What’s more, because many financial applications and transactions are taking place online, it’s become too easy for fraudsters to obtain real customer data to make their dealings look legitimate. These often evade traditional fraud detection methods because they rely on real data. 

Technology that uses rules-based triggers to find discrepancies isn’t much more effective. The technology is limited by what it already knows and isn’t “trained” to detect broader patterns that could indicate sophisticated crime rings or large-scale operations. Even when following specific rules, false positives can still be an issue and create friction for legitimate customers. 

How FI Use DataVisor to Fight Back Against Financial Fraud

The above risks and challenges of traditional fraud modeling aren’t unique to a specific company. In fact, these are a few of the shared realities for the four FIs mentioned above that are now using DataVisor to fight back against fraud. 

DataVisor’s comprehensive, AI-powered solutions proactively detect first-party, third-party, synthetic fraud, and transaction fraud early on, before any financial loss occurs, no matter how stealthily fraudsters work to change their patterns of attack to bypass legacy fraud systems.

With a holistic approach that includes machine learning, an advanced rules engine, device intelligence, and linkage analysis, DataVisor detects fast-changing attack patterns and delivers swift, accurate, actionable results. 

With DataVisor’s comprehensive fraud solutions, financial services and fintech companies can confidently pursue business opportunities and deliver frictionless customer experience, all without incurring an increased risk of fraud loss.  

Read more about how DataVisor worked with the four financial companies mentioned above in our new downloadable case study booklet and discover how DataVisor is helping them reduce financial and reputational risk with comprehensive fraud prevention solutions.

about Chandreyee Chakravarty
Chandreyee is the Regional Head of Sales at Datavisor. She has held high-profile sales positions within the Identity Management and AI space over the last 20 years globally.
about Chandreyee Chakravarty
Chandreyee is the Regional Head of Sales at Datavisor. She has held high-profile sales positions within the Identity Management and AI space over the last 20 years globally.