Fraud and Financial Crime Trends 2024: What Came True and What Did Not

One year ago, Feedzai asked me to check my crystal ball and predict the major fraud and financial crime trends that would shape the year ahead. 

I knew it would be a busy year for fraudsters and for fraud-fighters, and indeed it was. In the UK alone, criminals have already stolen £571.7 million in the first half of 2024. Meanwhile, US consumers have already lost $7.7 billion to different fraud and scams this year. As more fraud data rolls for 2024 in, it’s likely this fraud prediction will play out as predicted.

But what about the other fraud and financial crime predictions for 2024? Did these fraud foretellings come to pass as predicted? Or did I miss the mark? I’ve assigned each prediction a letter grade and given myself a report card assessing my predicted performance.

Key Takeaways

• AI’s role as a mainstream AML tool is moving slowly in the right direction
• GenerativeAI has not yet been embraced as a scalable fraud tool
• Fraud-as-a-Service has officially arrived
• Fraud and scam liability shifts are encouraging customers to take more responsibility
• A slow but certain cultural shift in collaboration in financial services is underway
• Fraudsters are sticking with reliable methods instead of personalized attacks on banks
• Banks are embracing more streamlined and sophisticated solutions
• Fraudsters are focused on payment rail attacks over card fraud
• Despite a push for new authentication methods, traditional options like OTPs are widely used
• Cryptocurrency’s role in fraud schemes is giving criminals a way to go undetected

AI Goes Mainstream in AML

I predicted banks would embrace advanced AI and machine learning (ML) more in 2024. These technologies would enhance fraud detection by automating tasks, improving accuracy, and uncovering emerging patterns. 

Additionally, I said machine learning as an anti-money laundering (AML) solution would go mainstream. This trend would be driven by external players like Google introducing innovative tools like AI-powered solutions for AML. This prediction was fueled by global turmoil like the ongoing Russia-Ukraine conflict and the humanitarian crisis in Gaza, which saw many banks re-evaluate their risk appetites.

• Reality: While there has been progress in adopting machine learning for fraud detection, it has been slower than I had anticipated. Many financial institutions still maintain traditional approaches to AML (as supported in Feedzai’s State of Anti-Money Laundering Report 2025). Instead, banks and financial institutions treat AI as an addition to their AML workflows rather than AI going mainstream.

While the trend is moving in the right direction, it’s progressing slower than I had predicted.

• Grade: B-

GenAI Unleashes Fraud at Scale

Last year, the financial services industry was abuzz over the potential that Generative AI (GenAI) had to revolutionize impersonation fraud. With readily available tools capable of generating fake images, videos, and voice recordings, I expected fraudsters would enhance their deceptions, passing authentication checks more easily. 

• Reality: In the past few years, there has been much hype over advanced technologies like GenAI and its potential as a fraud weapon. And with good reason. Take the case of a bank employee in Hong Kong who was tricked by a deepfake into paying millions to a fraudster posing as the company’s CFO. 

Stories like these are why concerns over GenAI’s potential as a fraud tool are well-founded. However, GenAI is not being adopted for fraud at the scale predicted. Instead, fraud is still very much a person-to-person interaction. In other words, a fraudster may use deepfakes or voice cloning to target individuals rather than groups. Fraudsters may have grown accustomed to their tested and true tactics and are still deciding whether to fully adopt GenAI.

• Grade: C-

Fraud-as-a-Service Gains Traction

I also predicted that GenAI would accelerate the rise of Fraud-as-a-Service (FaaS) by breaking down barriers for criminal call centers, enabling them to tailor attacks to specific banks. With the ability to rapidly learn account opening workflows, screen layouts, and onboarding processes, fraudsters were expected to exploit GenAI to create credible identities and scripts for new account fraud. I anticipated growing global regulatory interest in AI as nations sought strategies to harness its benefits while mitigating its misuse in financial and economic crime.

• Reality: Fraud-as-a-Service has officially arrived and is as concerning as predicted. Fraudulent operations are now thriving at a global scale, with scams, account takeovers, and call centers increasingly working at a faster pace than ever. The sheer growth in scam volume validates our prediction, making this an undeniable trend in financial crime.

Meanwhile, the compartmentalization of services within the ecosystem shows how FaaS has evolved into a well-oiled machine. Fraudsters are more likely to stay in their lanes to maximize efficiency by providing specific services in the financial ecosystem. This might include identity collection, synthetic identity creation, or call center expansion to push phishing operations for scams and fraud. This fragmented approach makes it easier for bad actors to operate without drawing attention to the broader network.

• Grade: A

Fraud Liability Split/Shift Takes Shape

Scam liability regulations, like the UK’s Payment Systems Regulator (PSR) model, were expected to motivate banks to prioritize prevention over detection. I anticipated real-time monitoring, behavioral biometrics, and inbound payment fraud monitoring would take center stage, transforming traditional reactive approaches. Additionally, I expected this liability shift to spark similar consumer protection measures in jurisdictions like the US and Australia.

• Reality: Fraud and scam liability shifts are taking shape, just as we predicted. Banks are adapting with strategies like real-time monitoring that focus on following money trails. One notable change is the introduction of “excesses” on scam reimbursements, similar to insurance deductibles, where banks won’t cover small claims upfront. This subtle shift encourages customers to take more responsibility and deter low-value or frivolous claims.
• Grade: A

Increased FinServe Collaboration

I predicted that collaboration would take center stage as banks, fintechs, and regtechs joined forces to combat cross-border fraud through shared data and insights. Regulatory changes were expected to clarify and encourage data-sharing by addressing hesitations tied to laws like GDPR. 

• Reality: While progress is evident, the pace has been slower than expected. Collaboration within financial services has gained momentum, with a notable shift in mindset from “Can we share data?” to “How can we share data effectively?” Banks are increasingly exploring safe ways to exchange insights, driven by initiatives like PSD3 and organizations like GASA, which have set important precedents. 

This cultural shift is a significant step forward as institutions test new methods to maximize benefits while safeguarding customer data. However, collaboration remains largely insular, with banks sharing primarily within their own circles and regulatory bodies doing the same. The expected broader cross-pollination —such as partnerships between banks, governments, telecom, social media, and other tech players—has yet to materialize. 

• Grade: B+

Personalized Attacks on Banks

I predicted that fraudsters would leverage personalization to develop more targeted attacks on banks. This included attacks like custom malware designed to exploit specific security systems. The anticipated shift was expected to push banks toward greater collaboration between fraud prevention, AML, and cybersecurity teams. I highlighted the need for internal and external integrated efforts to counter increasingly agile and sophisticated cyber threats.

• Reality: A significant rise in personalized attacks on banks didn’t materialize on a noticeable scale in 2024. Nor did an expected surge in ecosystem-level threats, such as those targeting major networks like Mastercard or Visa, also come to pass.

Why didn’t this prediction play out as anticipated? One possible reason is that fraudsters are still profiting from existing methods, remaining focused on tried-and-true tactics that yield reliable returns. Another possibility? Some of the most skilled cybercriminals may have shifted their focus to other tactics like benefits or insurance fraud. 

• Grade: C-

Capability Consolidation & Future Flexibility

One year ago, banks were expected to shift from adopting multiple point solutions to focusing on the right, strategic technologies to address financial crime. This approach emphasized consolidation to reduce costs, improve system coherence, and enhance future flexibility. Seamless integration between solutions was going to be critical to prevent fraudsters from exploiting gaps between disparate systems.

• Reality: This prediction has proven spot on. Driven by fatigue from managing redundant systems and juggling multiple vendors, financial institutions are shifting away from the “one problem, one solution” mindset. Instead, the focus has turned to strategic consolidation at both the data and tools levels. 

This has enabled banks to streamline operations and make smarter, more flexible decisions about how to use their resources effectively. This consolidation trend has extended to areas like case management, alert systems, and reporting tools. 

• Grade: A

Card Scams: A Shifting Focus

I predicted that as faster payment scams became easier to detect, fraudsters would shift their focus to other channels, like purchase scams. This activity accounts for nearly half of all scams. With initiatives like the UK’s Online Fraud Charter, big tech firms like Amazon, Meta, and Google were expected to implement stricter verification measures and take proactive steps to eliminate fraudulent content from their platforms and protect payment rails. 

• Reality: This prediction didn’t pan out, as the anticipated move toward cards as a main fraud channel hasn’t materialized. The expected tech collaboration to create stronger barriers against payment rail fraud fell short, leaving fraudsters with fewer obstacles to navigate. Instead, payment rail attacks like purchase scams and romance scams continue to thrive, showing that fraudsters are still exploiting these channels effectively. In the case of romance scams, they are playing the long game.
• Grade: C

Telcos Reassess Their Role & Traditional Authentication Gets Overhauled

This actually combines two predictions from last year. First, 2024 was going to be the year that telecom companies would reassess their role in authentication as consumer behavior shifted from phone calls to text-based interactions like SMS. I also anticipated tech giants such as Google, Amazon, and Apple were expected to take on a larger role in authentication through federated identities. 

Second, with phones increasingly used for banking and authentication via OTPs and passcodes, I anticipated telcos would face growing pressure to ensure the security of these evolving services. This reassessment was expected to align their safety measures with how consumers now rely on their platforms rather than their original design purposes.

• Reality: Despite ongoing discussions about alternative authentication options like behavioral biometrics, SMS-based OTPs remain widely used and deeply entrenched. The reliance on traditional authentication reflects an “if it’s not broken, don’t fix it” mindset, as banks and telcos see little urgency to disrupt the status quo. 
• Grade: D

Ongoing Usage of Cryptocurrency in Financial Crime Schemes

Despite increasing regulatory scrutiny, cryptocurrency was expected to remain a key tool in fraud schemes due to its perceived legitimacy and the growing acceptance of digital currencies like stablecoins. Feedzai’s research found that 40% of scam losses flow to crypto exchanges, nine times riskier than traditional recipients. I anticipated that crypto’s persistence would be fueled by a belief in its future and a lack of robust controls, as evidenced by issues with platforms like Binance, where insufficient KYC and suspicious activity report (SAR) measures enabled risky transactions.

• Reality: This prediction proved accurate, but in a nuanced way. Crypto remains the go-to method for laundering money, with fraudsters often attempting to move funds through exchanges first before resorting to more traditional means. Despite hopes that the rise of stablecoins and high-profile court cases would prompt industry-wide changes, there has been little progress in displacing crypto’s appeal for illicit activities. In fact, as crypto values have rebounded, its use in fraud schemes has become even more entrenched, giving criminals an easier way to blend into the crowd.

Interestingly, the hypothesis about crypto’s value affecting its appeal held in reverse: fewer legitimate transactions took place when values dipped, making criminal activities more visible. With values on the rise again, fraudsters benefit from the perception of legitimacy that high activity levels provide. 

• Grade: B

Conclusion

Unfortunately, this is not my best report card. But bear in mind, my predictions were made with the information I had to assess at the time. 

What does the year 2025 have in store for fraud and financial crime? Stay tuned to find out. Hopefully, I’ll be able to bring my grade point average up by next year!

• Article: GASA Global State of Scams Report: $1T Lost to Scams
  
• Resource: The State of Anti-Money Laundering 2025
• Solution Sheet: Instant Payments Without the Fraud Risk
• Solution: Transaction Fraud