For the banking industry, the emergence of artificial intelligence has come as a mixed blessing. On the one hand, it accelerates data analysis, improves customer service, and strengthens decision-making. On the other hand, banks’ traditional anti-fraud strategies began to fail under the weight of AI-facilitated threats, raising the need to reconsider their fraud detection and prevention strategies.

We explore the shift in fraud prevention measures, evaluate the impact of traditional and AI-enhanced fraud on organizations and their customers, and investigate how banks adapt their fraud to the emerging patterns of financial crime.

Key takeaways

Banking fraud has evolved from phishing emails and stolen cards to more sophisticated methods, including voice cloning, deepfakes, and automated scams.

AI enhances conventional fraud as well. Known scams become faster, more convincing, and easier to scale, making it more difficult to detect and prevent.

Artificial intelligence also transforms fraud prevention in banks. ML algorithms detect anomalies in real-world data, reduce false positives, and adapt to evolving fraud tactics.

Modern fraud prevention strategies in banks combine AI fraud detection, employee awareness training, unified fraud intelligence, and upgraded customer protections.

Fraud prevention in banks: overview

For decades, the financial sector has lagged behind technological innovation due to legacy systems, fragmented ownership models, and competing priorities such as growth, customer experience, and risk. Even today, fragmented data and siloed tools, along with organizational and structural constraints, hamper the adoption of new technology.

Organizations that fail to adapt suffer from stolen funds, damaged trust, and regulatory penalties, as fraudsters don’t miss opportunities when they see them. With the rise of AI, the stakes get even higher, and the battle between criminals and banks becomes an AI arms race. The winner will be the side that learns faster, adapts to change, and implements the technology more effectively.

Today, traditional fraud prevention practices that banks apply become inefficient when not backed by modern technology, including AI. However, AI itself raises new risks for data quality, governance, and cybersecurity.

Measures banks take to prevent scams. Source

Regulatory evolution of AI use in diverse domains, including banking, brings financial institutions new challenges. Now, they not only have to prove that they fight fraud, but also show that the results produced by AI systems are trustworthy and auditable. The recent advancement in regulatory compliance – the amendment to the EU AI Act – gives businesses that develop high-risk AI systems more time to prepare for new requirements coming into force. Despite some deadlines shifting, some transparency and governance obligations still come into force on August 2, 2026.

Traditional vs AI-enhanced fraud in banking

AI-driven scams have recently overshadowed conventional, human-driven fraud, yet traditional forms of fraud remain a significant challenge. With years of experience, banks can better adjust their existing detection rules and detect suspicious activity earlier, given manual limitations and established patterns. Still, even human fraud can be sophisticated enough to be missed by security systems.

Traditional fraud activities include:

Account takeover. Criminals get unauthorized access to the customer’s account to steal funds or misuse personal information. The breach occurs through stolen credentials, phishing, or social engineering.

Identity theft. Unauthorized misuse of personal information – ID numbers, names, or account credentials to impersonate victims and open fraudulent accounts, apply for credit, or support criminal activities.

Payment fraud. Deceptive or unauthorized activities connected with financial transactions across digital wallets, cards, bank transfers, and online banking.

Authorized push payment (APP) fraud. Customers are manipulated into authorizing payments to fraudulent accounts through manipulative persuasion or deceit.

Insider fraud. Occurs when employees or trusted individuals misuse legitimate access or authority for personal gains. Can lead to significant losses for financial institutions.

Business email compromise. Criminals impersonate employees or executives to make unauthorized payments or reveal sensitive information.

Scam calls. Fraudsters contact victims, exploiting human trust and urgency, to manipulate them into transferring money, disclosing passwords, or sharing verification codes.

On top of that, some traditional scam methods can intertwine, introducing hybrid scams. For example, a customer can receive a fake bank email followed by a phone call claiming to verify information. This type bridges the gap between conventional scams and more complex, AI-assisted financial crime. AI, in turn, introduces entirely new types of bank fraud and enhances existing ones, making them more convincing.

The Association of Certified Fraud Examiners (ACFE) identified the four top AI fraud schemes that have seen a significant increase in the last two years across industries. 55% of their survey respondents expect further growth in GenAI-driven document forgery and deepfake social engineering over the next 24 months.

The fastest-growing AI-powered fraud schemes over the past two years. Source

The core AI-enhanced financial crime patterns are:

AI-generated phishing emails. Scammers are using GenAI to create large numbers of convincing fraudulent messages. Phishing-as-a-Service has also emerged as a cybercrime business model with ready-made phishing software solutions.

Voice cloning and deepfake impersonation. Criminals create realistic synthetic audio, video, or images that imitate trusted individuals – CEOs, bank employees, tech support, accountants, government officials.

Synthetic identities. Fraudsters create fabricated identities by combining real and fake information. Artificial profiles can pass verification, so scammers can conduct transactions or obtain credit.

Automated social engineering. AI-powered tools are used to create, personalize, and manage deceptive interactions with victims.

AI-assisted mule account creation. Technology helps criminals create, manage, or operate accounts through which illicit funds are transferred.

What does it mean for banks? The shift in the banking fraud landscape presses organizations to adapt their strategies, introduce new fraud prevention techniques and tools, as well as move to real-time risk assessment.

Why traditional fraud prevention falls short

Even before AI, the world noticed the rising amount of data, transactions, and digital payments. One of the major international card networks, Visa, reported $257.5 billion transactions in 2025, an increase of $44.9 billion from 2023. In parallel, the total value of digital payments continues to climb at a CAGR of 4.31%, projected to reach $46.25 trillion by 2031. All that seething growth and development shifts the way banks and financial services providers operate. The same is true for fraudsters.

AI has made this environment even more dynamic. The problem is that the old fraud prevention strategies fail to work efficiently in the new conditions. Reactive fraud controls worked well in a much slower world. Still, they can no longer accurately detect fast, scalable, and adaptable AI-assisted fraud.

Static rules

Rule-based systems turned out to be slow to adapt to the changing fraud patterns. They rely on predefined conditions like unusual transaction amounts, locations, or spending patterns to define threats, and work well only with familiar schemes. When it comes to new challenges, they need to be manually updated to operate accurately, giving cybercriminals the opportunity to employ new tactics before the update.

High false positives

As spending patterns evolve, legacy systems can flag dynamic user behavior as fraudulent, suspicious or risky, therefore deteriorating customer experience. For example, mobile banking has made midnight logging in or purchases from multiple locations in a day completely normal. For banks, the excessive number of false positives produced by such systems increases operational costs. It overburdens fraud teams, whose skills can otherwise be directed toward more complex, high-value tasks.

Limited context

Traditional fraud prevention systems evaluate individual transactions, missing the broader customer context and making it difficult to identify complex fraud schemes. The modern banking industry applies complex analysis of behavioral patterns, device data, geolocation, historical activity, and cross-channel interactions to detect sophisticated fraud and reduce false positives.

Siloed fraud monitoring

Legacy systems and isolated data sources limit the visibility of fraud across channels. As a result, anti-fraud software may struggle to detect complex, multi-level fraud patterns. Centralized systems, on the other hand, allow for monitoring of payment cards, online banking, wire transfers, and customer identities through a single system and detect coordinated or multi-stage fraud attacks.

GenAI and the fraud detection gap

The latest developments in AI have made fraud detection increasingly challenging. This is largely because generative AI models significantly enhance the ability to create fraudulent content, while offering limited assistance in combating fraud. The nature of the latest large language models (LLMs) and other generative models plays a role in this: they rely on associative rules, which are useful for creative tasks (and fraud can be seen as a creative task to some extent), but are notorious for generating hallucinations. These hallucinations pose a serious risk when working with sensitive banking data, making the application of such models highly limited due to the potential dangers.

How AI-based fraud detection in banking makes the difference

Across the customer and transaction lifecycle, three areas are the most susceptible to fraud: customer onboarding (identity verification), authentication and account access, and payments and transactions. At each level, fraud detection using AI can make the difference by verifying identities, scoring login risk, and analyzing payment patterns to block suspicious transactions in time.

Nevertheless, to operate properly, AI engines and ML models need access to large volumes of high-quality data to learn from. The data should also be feature-rich, diverse, and relevant, so that the algorithm can make accurate decisions.

Behavioral analytics

Modern behavioral analytics analyzes user behavior and sets the benchmark for what is considered “normal” customer activity and what requires additional checks, or even blocks the transaction or freezes the account. In this process, AI helps to detect subtle anomalies by analyzing millions of user events per day. The common behavioral signals include:

Keystroke dynamics

Device characteristics

Transaction behavior

Network information

Mouse movements and touch gestures

Navigation patterns within an application

Geographic location and travel patterns

Advanced ML techniques such as graph neural networks, deep learning, clustering, and classification models further reinforce behavioral analytics. They enable banks to find hidden fraud rings, group similar behaviors to detect unusual activity, and recognize complex patterns.

Recently, Nasdaq combined a fraud detection platform with BioCatch’s behavioral intelligence capabilities to distinguish legitimate users from fraudsters better. Such strengthening of anti-fraud measures is caused by the unsettling statistics from 2022 to 2025, which show the rise of AI-related fraud (60% growth), an increase in cryptocurrency fraud cases (80%), and a sudden growth of account takeovers (50%).

GenAI-assisted investigation

LLMs, AI agents, and analytics tools facilitate fraud case analysis, making investigators’ work faster and more accurate. Professionals don’t need to spend hours checking transaction histories, account relationships, and communication records. GenAI acts as an investigation assistant. It retrieves and summarizes data, helps identify hidden connections between seemingly unrelated cases, and drafts investigation reports. In turn, human investigators remain the final decision-makers.

Algorithmic identity verification

Beyond behavioral analytics, ML models perform well in identity verification. They analyze facial features to prevent impersonation, verify documents, automatically checking them for consistency, and recognize biometric patterns with high accuracy. ML algorithms enhance multilayered systems, allowing organizations to provide secure digital banking services.

ML for identity verification helps banks and financial organizations maintain customers’ trust: 93% of Mastercard survey respondents say they worry about future identity theft or fraud, while 71% report they wouldn’t use a company’s mobile app or website if they experienced fraudulent activity. At the same time, users expect thorough and accurate identity verification; 92% expect a smooth, frictionless experience. It puts banks under pressure, as they should balance user experience with security.

Real-time transaction monitoring

Shifting to real-time analysis enables banks to analyze transactions as they occur. In contrast to traditional fraud prevention and detection tools, ML models can identify suspicious activity in milliseconds and detect new fraud types.

The algorithms process dozens of signals from actual transactions, including amount, currency, and payment type. They check whether the current payment matches typical customer behavior, using parameters such as normal transaction frequency and typical transaction locations. The system also uses special security signals, including device ID, IP address, and browser information, to efficiently counter credit/debit card fraud and money laundering.

Adaptive risk scoring

AI-powered risk-scoring engines help banks dynamically assess the fraud risk associated with customer behavior, including login attempts and transactions by analyzing contextual factors. The system learns from historical data about user transactions, typical spending amounts, and common merchants. When a transaction happens, the engine collects a wide range of signals, ranging from device intelligence to identity indicators. Then, the ML algorithm analyzes them together and assigns a fraud probability score with advice on further action.

Predictive analytics

One more important aspect of using artificial intelligence in fraud detection is predictive analytics. When combined with big data in banking, predictive analytics produces accurate forecasts of future events.

It can help with the following:

Conducting a SWOT analysis to identify potential weaknesses and ways to resolve them;

Identifying fraudulent activities that will most likely occur based on your historical data;

Anticipating customer behavior;

Assessing credit risks and one’s creditworthiness.

Predictive analytics is mostly used in lending, though it can also highlight other risk areas that might be overlooked.

Simulation models

Another powerful technique in AI-based fraud detection in banking is creating a virtual environment that mimics the real one. By using this simulation, banks can safely test their fraud-prevention strategies and emulate cyberattacks to assess how well their systems are protected. The best part of such simulation models is that organizations do not risk their financial assets while still accurately predicting risks and evaluating the effectiveness of their bank fraud detection tools.

Supervised vs unsupervised ML for fraud prediction

Financial institutions deploy supervised and unsupervised machine learning to teach ML models how to recognize potential fraudulent activity and flag it.

Supervised machine learning

The supervised machine learning technique implies that ML models are trained on pre-labeled data sets. In this way, the model is “told” which data is legitimate and which is fraudulent. The goal of the ML model is to recognize suspicious transactions and independently detect them in the future. Examples of fraudulent transactions include flagged IP addresses or transfers to a high-risk address. As a result of the training, an ML model can identify anomalies that match the known fraud indicators.

Unsupervised machine learning

Since fraudulent schemes are constantly evolving, supervised machine learning alone is not sufficient to detect newly discovered threats. This is where unsupervised learning steps in. The technique analyzes raw, unlabelled data. The model learns to detect subtle, non-obvious fraudulent patterns and is therefore more likely to detect previously unrecognized threats.

Supervised learning is highly effective at detecting known threats. In contrast, unsupervised learning is well-suited to newly discovered or emerging ones. A combination of both methods strengthens fraud detection by addressing a broader spectrum of risks. But before you build and start an ML model, you’ll have to collect and prepare the data it will work with. Working with an experienced team of ML experts that have rich experience in providing financial software development services can help ensure that the designed ML model is 100% compatible with your system and is reliable and secure.

Real world AI applications for fraud detection

Below are some examples of AI-powered fraud systems and features that help banks with accurate, real-time fraud detection.

Google’s AI scam detection on Android

Technology companies support anti-fraud efforts for banks and financial institutions. To protect users from impersonation scams, Google introduced a fake call detection feature for Android (available in the Phone by Google app). The software detects spoofed numbers with a changed caller ID, notifies the user, and can automatically end such calls.

How does it work?

You receive a call seemingly from your bank

Android sends the request to the app to verify the call

In case no confirmation is received, the app ends the call

Google plans to roll this feature out on Android 11+ devices with Revolut, Itaú, and Nubank, and will later expand it to additional banks. As you can see, collaboration between technology companies and financial institutions can strengthen fraud prevention, protecting customers from AI-enabled scams.

NVIDIA’s AI platform

One of the major technology companies, NVIDIA, offers a specialized AI platform for fraud detection for banks, payment providers, and ecommerce companies. The solution combines GPUs, AI frameworks, machine learning, graph analytics, and real-time data processing to detect suspicious transactions accurately.

The common use cases include credit card fraud detection, digital payment fraud prevention, Anti-Money Laundering, and more. Using a broad NVIDIA infrastructure, the company provides faster AI model training and inference and efficient scaling for large transaction volumes.

Mastercard’s Decision Intelligence

In the age of AI and advanced fraud, global payment technology companies did not stay on the sidelines. Mastercard introduced its Decision Intelligence product for customers. The solution is a risk score platform with AI models that were trained on the company’s vast data. Mastercard expanded its use beyond Mastercard transactions, enabling all issuing banks to improve fraud management, reduce false positives, and lower customer service costs with the Decision Intelligence solution.

American Express’s ML-based fraud detection tool

Considering the growing number of credit card frauds that cost US customers roughly $11 billion per year, American Express decided to use machine learning to improve its fraud detection processes. The company deployed deep learning models to develop a powerful AI-based fraud detection tool. The tool combines RNNs with LSTMs for immediate detection of anomalies in massive volumes of transactional data. As a result, American Express was able to improve fraud detection accuracy by 6% in certain segments and is now using an improved and more powerful ML model called Gen X.

PayPal’s fraud detection solution

Another example of a global financial company using AI for fraud detection is PayPal. Back in 2019, the company partnered with a reliable software provider to design a fraud detection solution that would function 24/7 across the globe and would be able to detect potential fraud in real time. As a result, PayPal was able to not only cover massive volumes of customer transactions but also to lower the server capacity, eventually being able to improve real-time fraud detection by 10%. Today, the company offers a variety of risk and fraud management solutions to ensure their clients’ data and transactions remain as safeguarded as possible.

The US Department of Treasury’s AI implementation

In Fiscal Year 2023, the US Department of Treasury managed to recover $375 million by implementing AI in its fraud detection process. The decision was impacted by the fact that in 2021, due to the pandemic, the check fraud has drastically increased by 385%. To address the issue, the Treasure used artificial intelligence and has been enhancing its fraud detection policies since then.

Limitations of AI tools for fraud detection

With all the advantages, the introduction of AI in banking is not perfect by any means. No software can guarantee 100% fraud detection, as scam tactics and technology evolve. Here’s where AI still falls short:

Lack of explainability when finding positives. Some AI models operate as “black boxes,” making it difficult to understand the reasoning behind their decisions.

Missing regional specifics. AI may not fully recognize regional differences in user behavior that can affect customer interactions and financial assessments.

Heavy reliance on data quality. When the model learns from inaccurate or imbalanced data, it will produce unreliable results and make faulty decisions.

Bias and fairness issues. AI can learn bias from historical data and make unfair decisions when applied for credit scoring or customer risk assessment.

Limited human judgment. AI cannot understand unique customer situations, personal circumstances, or needs. Human oversight is needed for complex decisions.

Overall, neither traditional-only, nor AI-based fraud detection tools are sufficient when used in isolation. A robust anti-fraud strategy blends technology, data analysis, and human expertise.

How to build a modern fraud prevention strategy

So, how to prevent frauds? An adaptive fraud prevention strategy combines diverse components, so the technology is just a part of the puzzle. As the number of fraud schemes continues to grow, resulting in $579.4 billion in global losses, banks must protect customers, funds, and assets.

Technology adoption

The vital step in modernizing your fraud prevention and detection tools and systems is investing in technology. The goal is to shift from static rule-based solutions to real-time, AI-driven detection, behavioral analytics, and predictive risk scoring. You can focus on ML models, implement GenAI to assist human analysts, or introduce live transaction monitoring systems.

When choosing between custom AI development or third-party platforms, large banks tend to combine these approaches. For example, a bank might use a proprietary foundation model, but fine-tune it on its customer and transaction data. Nevertheless, custom anti-fraud development is needed when prebuilt solutions cannot meet an organization’s needs – when there are institution-specific fraud patterns, complex system integration, or stringent data privacy and security requirements.

Average spending on fraud detection technology. Source

Internal measures

Educating employees about potential threats is by no means a new approach to fraud prevention. However, the age of AI enabled new scam methods like deepfakes and vishing your employees may not be aware of.

Customer-facing protection

Personalized fraud alerts