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April 18, 2025

Now’s the time to shore up fraud prevention strategies

There’s never been a greater need, or a better opportunity, to overhaul fraud prevention operations.


Today, three-quarters of all banks report that consumer fraud is up. A 2024 report by TransUnion found that one in seven new credit card accounts were suspected to be fraudulent. That’s just a single drop in an ever-expanding bucket. In March, the US Federal Trade Commission released its 2024 fraud data, confirming businesses and consumers are dealing with staggering losses.

US consumers alone lost more than $12.5 billion to fraud in 2024—a 25% year-over-year increase. The biggest chunk of those losses came from investment fraud, which topped $5.7 billion. Financial institutions bear the brunt of this fraud, according to a 2024 study from LexisNexis: financial institutions lose $4.41 for every $1 lost by consumers.

Americans were hardly alone. According to the Canadian Anti-Fraud Center, consumers in that nation lost $638 million to fraud last year, up from $578 million in 2023. One study found more than half of all Canadians were targeted by fraudsters in just the second half of 2024.

The sources of fraud vary. Aside from age-old paper-based tactics such as check fraud, cyber criminals are using machine learning (ML) and artificial intelligence to create advanced phishing and spoofing attacks that are difficult to identify before it’s too late. And thanks to sophisticated criminals who offer fraud-as-a-service, it doesn’t take much to get in on the con.

Banks and insurance companies must also worry about money laundering, hacking, fraudulent credit card disputes and wire fraud. One of our clients, for instance, accumulated fraud losses of around $3.3 million in a single year because the company’s external accounts were not being validated as customers made payments. We helped that client improve matters dramatically with a real-time validation initiative.

As a result of this surge, fraud prevention is becoming a big part of financial firms’ growth strategies. Banking and insurance companies, already facing increasing regulation and compliance rules, must learn to navigate the new ecosystem of models that includes third parties and vendors as well as real-time payment adoption and transaction processing. Companies that can improve fraud protection reduce financial and reputational losses, build trust with customers and partners, and gain operational efficiencies.

Indeed, many organizations are seeing a silver lining in the rise in crime: it’s the perfect opportunity to significantly transform their fraud prevention strategies and gain competitive advantage. But many organizations don’t know where to start. They want to move fast and make changes, but they worry about scale and struggle with the need to build and maintain customer trust. There are ways to do it correctly, though.

Using AI for fraud prevention

The fight against financial fraud, especially as cybercriminals become more sophisticated, starts with technology. AI and ML have been used in the fraud and anti-money laundering space for several years, but we’re finally starting to see an evolution of these models.

For example, where organizations use AI for transaction monitoring, their financial information system team can focus on rule-tuning and optimization because they better understand the data in front of them.

One of our clients recently used automation to cut manual fraud processing by 90%. That organization, one of the largest banks in the world, monitors tens of thousands of credit and debit card transactions daily, and each one needs to be screened. With limited IT resources, the bank was having difficulty automating this task; it had to continually update its fraudulent charges and anti-money laundering fraud rules to identify criminal activity.

We helped the client build and develop several automation capabilities that resulted in a significant reduction in fraud exemptions as well as processing time. In the end, the client reported garnering $8.10 for every dollar it spent.

Our customer’s automation is focused on automating time-sensitive processes, but there are other elements used in the fraud detection process as well. Using data from customer risk profiles, businesses can identify trends and patterns that may suggest fraud. They may create dynamic thresholds that change as the relationship with a given customer deepens. A $3,000 transaction may always be flagged with new customers, but once it’s apparent that they’re making larger transactions on a regular basis, the threshold may be moved to $5,000 or even $10,000.

Finding common denominators

There’s a crucial caveat for institutions tweaking their fraud risk parameters: it requires a renewed focus on data quality. After all, AI is at the heart of this work, and its performance relies on the data it’s fed. Even something as simple as using internally focused AI-based chatbots—another tool we implemented for our global banking client—demands that businesses up their game when it comes to data. Internal users can only get the information they need (and get it more quickly) if they have access to the right data.

However, financial institutions can’t forget about the basics. If your organization is having problems with check-fraud you need to sit down with the people doing the day-to-day work and look at your processes. Where are there gaps in those processes? Are there parts of a process that can be completed elsewhere? Are there parts that require a technological fix? Some easy fixes include:

  • Implementing manual check verifications such as signature verification and account balance requests
  • Requiring two approvals for incoming checks that exceed specific dollar amounts
  • Tapping automation tools that flag duplicate check numbers and unexpected check amounts
  • Auditing for duplicate data transfers

Organizations should also ensure that connections with third-party payment vendors are secure, without any duplicate information.

Find a top-down viewpoint that you can run with, start a transformation pilot program, and go from there. By incorporating fraud management into existing platforms, you’ll be able to rapidly identify fraud and create early warning systems so people can jump in and stop fraud before it happens. Since many organizations struggle with tech staffing, outsourcing this can help move your project forward more quickly. Our banking client was able to reassign 425 full-time employees after we helped it streamline and automate processes.

Financial crime operations—the human element

There’s a people component to fraud protection, too, but even here, technology can help. Customers need to be educated about what fraud looks like and how to ask for help if they are concerned. Banks and financial institutions need to help consumers stop clicking random links and sharing personal information—and this will only happen through ongoing education. Adding technology such as instant texts when AI flags a transaction pulls customers into the task of protecting themselves and their assets.

During my time working with banking and insurance companies, I’ve been part of many anti-fraud programs. I’ve watched Cognizant teams go in and transform operations, automate and improve processes, boost compliance, and introduce AI and ML across the board.

The most successful of these programs are proactive on the part of clients, allowing us to be tactical and create recommendations and plans that save money and prevent headaches for our clients. They are using their resources more effectively and reducing the number of transactions sitting in the queue waiting to be analyzed.
 



Dr. Amita Oppal Gill

Market Leader, North America - Intuitive Operations & Automation, BFS

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Amita is a seasoned leader & executive with over 25 years of global experience within the financial and professional services industries. Amita is known for her ability to lead large scale transformations, leveraging her deep industry experience in financial services along with her operational, technology, people leadership, innovation and strategic expertise.



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