Understanding evolving fraud risks in finance - From instant payments to AML compliance

Fraud—it’s getting faster, smarter, and more pervasive. Today’s criminals are taking advantage of the latest technology to bypass traditional detection methods and exploit the reduced time banks have to block fraudulent transactions. The result? A whopping 60% of financial institutions and fintechs reported increased fraud in the past year alone.

But it’s not all doom and gloom. By taking a strategic, collaborative approach, top financial institutions are detecting and preventing fraudulent transactions with greater sophistication than ever before. This guide explores the latest threats in more detail to uncover the most effective strategies deployed by today’s prevention experts.

Payment fraud is increasing in intensity and complexity

Quantifying the impact of payments fraud can be a challenge, especially when considering its impact on operations, compliance, and consumer trust. But the data shows it’s on the rise—and taking more of a toll than ever before.

A Juniper Research report anticipates more than $362 billion in online payments fraud between 2023 and 2028. Every dollar lost to fraud now costs financial institutions more than $5, too, up 25% from $4 only four years ago.

The threat is multifaceted. Bad actors are leveraging AI, machine learning, automation, and other sophisticated tactics across multiple channels to increase their chances of success. Top payment fraud trends in 2026 include:

The rise of instant payments fraud

Instant payments are seeing a rise in popularity, with more than 70 countries offering real-time payment systems worldwide. While these tools provide customers with the speed and convenience they expect, unfortunately, they also create a prime opportunity for fraudsters. Teams have little more than a few milliseconds to detect, analyze, and prevent fraud before the funds are moved to a new account.

Cross-channel attacks and BEC

63% of banks cite business email compromise (BEC) as the top avenue for fraud attempts. Thanks to AI, automation, and deepfake audio and video technology, fraudsters are targeting channels like ACH and wire transfers through increasingly sophisticated impersonation schemes.

Growing focus on alternative payment methods

To get around traditional detection methods, fraudsters are turning towards alternative targets like prepaid cards, loyalty points, and financing options. Since these alternative payment methods tend to have weaker identity security than their more closely watched counterparts, they’re an especially appealing point of attack.

New risks to traditional payment methods

Banks can’t forget about updating their prevention tactics for more traditional payment methods, too. Checks continue to be the payment method most often subjected to fraud, with 63% of organizations facing check fraud in 2024. AI-generated forgeries are making it increasingly difficult for providers to identify fraud as it occurs.

Banks must balance competing interests when mitigating fraud

Understanding these developing threats is only the first step. From there, financial institutions must balance their identification and prevention tactics with a wide range of competing considerations: from customer experience to compliance and beyond.

Speed vs. security

Today’s customers demand fast, frictionless interactions at every touchpoint. According to recent research, over 75% of consumers believe real-time payments provide a superior customer experience. To meet these expectations, banks need fraud risk scoring and interdiction processes to operate near-instantaneously. 

Balancing the customer experience

Achieving a seamless, secure customer journey involves minimizing friction wherever possible. But traditional fraud detection systems often generate false positives, which can disrupt customer activities and erode trust. Modern fraud prevention uses AI-driven behavioral analytics and adaptive risk-based authentication to reduce these false alarms. 

Shifting regulatory demands

In regions like Europe, directives such as PSD2 and PSD3 are emphasizing stronger customer authentication, real-time transaction monitoring, and broader fraud liability. The AMLA is also increasing oversight and enforcement, demanding more comprehensive and automated AML compliance programs. Staying aligned with these mandates requires agile, integrated fraud detection systems that adapt quickly.

The future lies in advanced, AI-powered fraud detection

With fraudsters leveraging increasingly sophisticated technologies like AI, machine learning, and automation, it’s time for financial institutions to respond in kind. According to SEON, 76% of organizations are prioritizing investments in AI, advanced data analytics, and machine learning. Thanks to AI-native decision engines, teams are enhancing detection, automating decisions, and keeping pace with the latest compliance. Here’s how it works:

Real-time fraud detection

Decision engines excel at processing and analyzing vast amounts of transaction data instantly. This capability is key for payment fraud mitigation in instant payment environments, where milliseconds matter. By flagging suspicious activities in real time, decision engines prevent fraudulent transfers before funds move, drastically reducing potential losses and neutralizing threats at their earliest moment.

Adaptive rule management

Payments fraud trends are fluid, demanding solutions that evolve just as quickly as the latest tech. Decision engines offer dynamic rule management, helping companies update fraud detection rules and machine learning models swiftly in response to emerging fraud patterns and updated AML regulations. This adaptability ensures institutions stay compliant and resilient amid rapid change.

Multi-channel orchestration

Fraud spans multiple channels, including payments, accounts, digital identities, and behavioral profiles. Decision engines aggregate data from all these sources, facilitating a comprehensive, 360-degree view of transactional risk. This holistic orchestration is critical for combating complex attacks such as BEC and synthetic identity fraud, which leverage multiple vulnerabilities simultaneously.

Reducing false positives

Traditional fraud controls often create bottlenecks with false positives, disrupting legitimate transactions and frustrating customers. By blending AI-driven predictive analytics with expert-defined risk rules, decision engines finely calibrate risk assessments to minimize false positives. This balance protects customers’ seamless experience while maintaining high detection accuracy, easing operational strain on fraud and AML teams.

Supporting AML compliance with automation

Beyond fraud prevention, decision engines play an important role in AML compliance. Automated suspicious activity monitoring, risk scoring, and case management streamline investigation processes and reporting obligations. These tools increase efficiency and help institutions meet stricter regulatory expectations to reduce costly compliance risks.

Build resiliency to AI-powered payments fraud with FitLogic

To meet the latest wave of financial threats, institutions need a comprehensive, technology-forward strategy that unites prevention with rapid identification.

By combining the speed and adaptability of AI and machine learning with proven rule-based detection, C&R Software’s FitLogic delivers precisely that. As an AI-native decision engine, it’s built for the complexities of real-time financial transactions, offering a modern, scalable alternative to legacy systems.

For teams operating in high-risk, high-visibility areas such as collections, originations, and fraud, FitLogic provides the intelligence and resilience needed to stay ahead. Get in touch with decisioning experts at insights@crsoftware.com to find out more.