Fraud detection is one of the most powerful applications of artificial intelligence (AI) in the financial services sector. The ability of ML algorithms to process and analyze large volumes of data enables systems to identify unusual behavior with more speed and accuracy than traditional methods. This helps banks flag potential fraud risks early, protecting themselves and their clients from major losses.

Why Use AI for Fraud Detection in Banking?

Traditionally, banks used rule-based systems to detect fraud. These systems relied on predefined thresholds and patterns, which caught common fraud passably but struggled with newer or more sophisticated schemes. These limitations put banks at risk of flagging transactions incorrectly or missing instances of fraudulent behavior entirely.

AI eliminates these blind spots by learning continuously from old and new data. ML models can process vast amounts of data, learn patterns, and apply their knowledge to new information. They can analyze transactions in real time, adapt to evolving fraud tactics, and reduce false alarms. Leveraging AI for fraud detection empowers banks to protect customers more effectively, respond to threats faster, and free analysts to focus on the most complex cases.

Technologies Used in Fraud Detection

Fraud detection involves multiple types of AI technologies. ML and deep learning build the foundation by helping with pattern recognition, while natural language processing (NLP) and behavioral biometrics supplement the process by analyzing language cues and user actions to identify unusual activity.

Machine Learning

ML is a branch of AI that lets computers estimate outcomes and make decisions without needing specific instructions for every scenario. ML models learn patterns and relationships from training data, then use their insights to respond to new information.

Banks and other financial institutions use ML models to scan massive amounts of relevant data, such as transactional data, customer profiles, behavioral data, and historical fraud labels, in real time. As they encounter information, they learn to differentiate normal consumer activity from activity that appears unusual or high-risk. Their ability to refine understanding based on new data allows them to better detect suspicious behavior more accurately than rule-based systems and other traditional methods.

Deep Learning

Deep learning is a branch of ML that specifically uses layered neural networks to enable analyses of complex relationships within large datasets. Each layer within the network extracts a different level of detail, deepening the system’s understanding of the patterns it observes. This structure helps them better analyze datasets with subtle or high-dimensional information.

Banks and other financial institutions use deep learning to flag fraud signals that are too subtle for simpler models. Deep learning models can spot minute irregularities in spending, monitor sequences of actions that lead to suspicious events, and detect new fraud strategies without human intervention.

Natural Language Processing

NLP allows systems to understand and respond to human language. These systems use computational linguistics, ML, and deep learning to help computers parse the elements of verbal inputs, such as words, phrases, intent, and context. As they train on written language, spoken language, and structured text, they learn to classify, summarize, and extract insights from unstructured verbal information.

NLP helps banks detect fraud by reviewing verbal data, such as customer messages, claims, and investigator notes. Their knowledge of language allows them to detect patterns that suggest fraud, such as inconsistent details or suspicious phrasing. This helps analysts flag fraud risks outside transactional and behavioral data.

Behavioral Biometrics

Behavioral biometrics use a combination of AI models, including ML and deep learning, to detect patterns in the ways users interact with devices. It studies typing rhythm, scrolling habits, touchscreen pressure, and mouse movement to build a consistent profile of normal user behavior. This allows systems to flag behavioral deviations.

Banks compare typical user behavior with current user behavior to confirm that the person using an account is the rightful owner. Unfamiliar movement patterns and inconsistent login behavior signal that a fraudster may be using the device. This extra layer of protection helps banks detect fraud risks that traditional authentication methods cannot catch on their own.

How Does AI for Fraud Detection Work?

AI-supported fraud detection involves multiple steps, beginning with data collection and ending with continuous learning. Each step helps the system understand data more clearly and improves its ability to spot unusual activity.

Step 1: Data Collection

The first step in building an AI fraud detection system is collecting relevant data. Analysts have a massive number of datasets, including:

• Transaction data (e.g., credit cards, debit cards, wire transfers, ACH payments, online bill payments, ATM deposits and withdrawals, account balances)
• Customer information (e.g., personal details, account type, linked accounts, tenure, credit history)
• Device and network data (e.g., IP addresses, device IDs, browser fingerprints, operating system information, hardware information, login locations, network patterns)
• Behavioral habits (e.g., typing rhythm, mouse movements, touchscreen interactions, login patterns, session timing, navigation habits)
• External sources (e.g., credit bureaus, financial reporting agencies, blacklists, watchlists, fraud databases, consortia data shared among banks and payment networks)
• Communication data (e.g., customer emails, chat logs, support tickets)
• Text data (e.g., complaint or claim descriptions)

Internal data shows banks how their customers normally behave. Meanwhile, external data provides information on known fraud cases, risky entities, and industry-wide trends to help banks identify potential threats. The more information the model collects, the better it can distinguish normal from suspicious patterns.

Step 2: Data Preprocessing

Raw data often includes errors, missing values, and inconsistencies. Analysts must clean and optimize data to ensure machine readability and accuracy. This means correcting errors and duplicates, filling in missing values, and standardizing formats for dates, currencies, and categorical values.

Analysts may also integrate data from disparate sources (transaction records, customer info, device data, external databases) into one unified dataset. This gives the model a complete view of all information and enables it to uncover connections between data points in disparate sources. For example, combining transaction records with login patterns will allow AI to flag when high-value transactions accompany unusual login activity.

Data preprocessing also involves data transformation, which scales numeric values to a consistent range to avoid having large values overpower small values. Analysts must also encode categorical values into a numerical format to make them machine-readable.

Step 3: Feature Engineering

After the initial data pre-processing, analysts must single out meaningful features from raw data. This process, called feature engineering, creates, selects, and refines the variables or features that the AI model will use to detect suspicious activity. These variables capture meaningful patterns in data, such as

• Transaction velocity: The number of transactions per hour/day.
• Amount deviations: Transactions that significantly exceed normal amounts
• Geolocation anomalies: Purchases from unusual locations.
• Device fingerprint anomalies: Activity on new devices or browsers.
• Behavioral patterns: Typical activity times, frequency, and transaction types.

Well-designed features emphasize the difference between normal and fraudulent transactions. This improves the model’s precision in detecting fraud, reduces false alarms, and helps banks protect customers more effectively.

Step 4: Pattern Recognition and Anomaly Detection

With the data collected and cleaned, analysts can begin teaching AI how to recognize patterns that distinguish legitimate transactions from suspicious activity. This involves feeding the models curated datasets with both normal and fraudulent examples. The model studies important features and learns which combinations of values signal potential fraud.

After training, the model becomes effective at processing and classifying transactions based on the patterns it has learned. It studies the relationships between features, such as transaction velocity, timing, and device behavior, to determine whether a transaction aligns with normal customer activity. As it encounters more data, it becomes better equipped to single out subtle anomalies and adapt to new information without constant human input.

Step 5: Real-Time Transaction Scoring

After a model learns to detect patterns, it applies its knowledge to evaluate new transactions in real time. In this step, the model compares each incoming transaction’s features with the patterns it recognizes as fraudulent, then assigns the appropriate risk score. The higher the score, the stronger the risk of fraud.

Banking systems use these scores to guide actions. For example, they may automatically block high-risk transactions, send them to human agents for review, or alert customers for verification. With real-time scoring, banks can act on potential fraud cases before serious damage occurs, enhancing customer protection.

Step 5: Continuous Learning

The final step in AI fraud detection involves refining the model’s understanding to improve future accuracy. To update the model’s understanding of current fraud trends, analysts feed new outcomes back into the system, then label transactions as either fraudulent or legitimate. They update the model’s knowledge regularly to ensure it stays accurate, efficient, and ready to respond to new risks.

Real Examples of AI Fraud Detection in Finance

AI has helped banks safeguard themselves and their customers against multiple types of fraud, including transaction fraud, account takeover, identity theft, and money laundering. Below are a few examples of AI fraud detection in the real world.

Detecting Fraudulent Transactions

AI uses pattern recognition to flag transactions from stolen credit cards. In 2024, for example, Mastercard announced that it would develop a generative AI model to help detect suspicious transactions. The model, called Decision Intelligence Pro, uses a recurrent neural network trained on millions of Mastercard transactions. It reviews the cardholder’s purchase history to see if the current transaction fits their usual spending locations. According to Mastercard, the system has improved fraud detection rates by up to 300%.

Identity Verification

Another type of fraud AI helps with is identity theft. The global intelligence company Au10tix, for example, launched an AI-powered document authentication feature that verifies non-ID documents like utility bills and bank statements. Through performing forgery tests and validating metadata, it can detect altered or synthetic documents and help banks prevent bad actors from creating fraudulent accounts using stolen or forged information.

Money Laundering Prevention

AI can even help banks detect financial crime. HSBC, for example, uses an AI-powered system called Dynamic Risk Assessment to scan transactions for patterns consistent with money laundering, such as unusual transaction flows and rapid fund movement across accounts.

The system also helps identify crime networks by flagging connections between accounts. With AI, HSBC can flag subtle relationships that traditional models might miss. It has helped the bank catch two to four times more financial crime than before, while reducing false alarms by 60%.

Leverage AI with Bronson.AI

AI can help financial institutions with more than just fraud detection. Technologies like ML, deep learning, and NLP can also help organizations automate workflows, prevent risk, and deepen customer intelligence. With these tools, you can optimize operational efficiency and empower informed decision-making.

Book a consultation with Bronson.AI to uncover the different ways AI can enhance your financial services business. Our experts can help you build a tech stack that aligns with your business needs and goals. We guide you through every step of the AI journey, ensuring smooth adoption with minimal disruption.