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Machine learning for fraud detection:
tech overview, use cases & challenges
May 20, 2025
Most modern fraud detection systems rely on ML algorithms trained on historical data on past fraudulent or legitimate activities to autonomously identify the characteristic patterns of such events and recognize them once they recur.
Itransition offers machine learning development services to help you enhance your risk management capabilities, protect your business and clients from evolving fraud threats, and prevent financial losses.
Machine learning in fraud detection: trends & stats
18%
of anti-fraud experts already include AI and machine learning in their fraud detection toolkit
ACFE
32%
of anti-fraud experts plan to implement AI and machine learning for fraud detection in the next 2 years
ACFE
>80%
of payments industry seniors see fraud detection and prevention as the top AI use case in their field
Statista
Rule-based vs ML-based fraud detection
ML-enabled anomaly detection applications differ from traditional software in terms of detection technique:
Rule-based fraud detection
-
Traditional rule-based solutions follow an "if/then" logic and trigger a response when a predefined condition is violated.
-
Human specialists must manually compile libraries with hundreds of rules to instruct these tools on how to detect fraud cases.
Example
The fraud detection system of a bank flags supposed customers who use credit cards in unusual locations or increase their payment frequency.
ML-based fraud detection
-
ML solutions rely on more complex and variable rules than traditional systems. Within the ML training process, ML models analyze data on past fraud cases, discover patterns and relationships between data points, and then use this knowledge to identify such patterns once they recur in future scenarios.
-
ML systems can detect imminent criminal actions by identifying anomalies, namely subtle and unconventional behavioral patterns that deviate from the norm but are likely to be overlooked by humans, which could be clues to upcoming fraud.
-
ML-powered solutions improve with experience, since their models self-learn over time as they process new data, including unmapped data points. So, if they encounter new fraud scenarios, machine learning-based anomaly detection systems will quickly learn to detect such threats, automatically integrating and updating the existing rules without human intervention.
Example
The fraud detection system of an ecommerce platform runs into a suspicious credit card transaction that doesn't fit its user’s behavioral patterns based on multiple subtle parameters, such as the product pages browsed before placing an order.
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Technical overview of ML for fraud detection
ML approaches
The main approaches to training machine learning algorithms are supervised, unsupervised, and reinforcement learning, which differ according to the degree of human involvement and control over the ML training process.
Supervised learning
ML-based fraud detection systems are trained with large amounts of labeled data, previously annotated with certain labels describing its key features. This can be data from legitimate and fraudulent transactions described with "fraud" or "non-fraud" labels, respectively. These labeled datasets, which require rather time-consuming manual tagging, provide the system with both the input (transaction data) and the desired output (groups of classified examples), allowing algorithms to identify which patterns and relationships connect them and apply such findings to classify future cases.
Unsupervised learning
These algorithms are fueled with unlabeled transaction data and have to autonomously group these transactions into different clusters based on their similarities (shared behavioral patterns) and differences (typical vs unusual patterns which can correspond to fraudulent activity). This approach is computationally demanding but can be the only choice when dealing with previously unseen and therefore unlabeled fraud attempts, or when annotation is too difficult.
Reinforcement learning
This trial-and-error approach involves multiple training iterations in which the algorithm performs a fraud detection task in different ways several times until it can accurately identify fraudulent and non-fraudulent attempts. Since it does not require labeled inputs, reinforcement learning can be applied without prior knowledge of the current fraud scenario. However, it requires considerable computing power.
Scheme title: Supervised vs unsupervised learning
Data source: medium.com — An Executive’s View: Introduction to Machine Learning
ML algorithms
Identifying the best algorithm or ensemble of algorithms to perform fraud-related data analysis can be a challenging task, as their performance can depend on the scenario in which the ML system is deployed. Researchers are currently focusing on the following options:
Logistic regression
A supervised learning algorithm which calculates the probability of one event out of two alternatives, such as "fraud" or "non-fraud" based on a set of relevant parameters.
Naive Bayes classifier
Another supervised learning algorithm which estimates the probability of an event being fraudulent or non-fraudulent. Naive Bayes can be a great option in scenarios with limited training data. However, logistic regression typically outperforms Naive Bayes when trained on very large datasets.
Decision tree
This supervised learning algorithm uses a tree-like decision-making model in which every bifurcation represents the analysis of a certain metric or condition (spending threshold, location, etc.) to determine whether an operation is fraudulent.
Random forest
A combination of several decision trees to further expand the amount of data types and conditions examined and identify non-linear relations among multiple variables.
XGBoost
Just like random forests, XGBoost aggregates different decision trees to maximize fraud detection accuracy. However, while each tree in a random forest is trained independently and their predictions are then combined, those in the XGBoost are trained sequentially, using the first tree's model as the basis for the next and adjusting the output at each iteration.
Support vector machine
Several systems rely on this supervised learning algorithm for credit card fraud detection because of its excellent performance with large datasets despite it being computationally demanding.
K-nearest neighbor
This supervised learning algorithm can define the nature of an event (be it fraud or non-fraud) by comparing it with similar occurrences recorded in the past. Despite the high accuracy of KNN in detecting fraud, its decision-making process can be difficult to interpret.
Neural networks
Complex, multi-layered architecture and superior big data analysis capabilities make neural networks the go-to algorithms when it comes to spotting non-linear relations and dealing with unprecedented fraud scenarios through supervised, unsupervised and reinforcement learning. Autoencoders are currently one of the most popular types of NNs for fraud detection.
Machine learning in common fraud scenarios
Machine learning can be deployed to keep fraudsters and cybercriminals at bay in a variety of scenarios. Here are some of the most common fraudulent activities this technology helps detect.
1 Market manipulation
Financial institutions have long relied on ML-powered fraud detection tools due to their accuracy, adaptability, and cost-effectiveness.
ML-driven systems can help prevent financial fraud, such as churning, spoofing, and wash trading, by spotting anomalies in stock traders' activity and cross-checking transactions and brokers' data to detect inconsistencies in the information provided.
2 Money laundering
Applying machine learning in banking has proved useful to track anomalous financial transactions that could be a sign of criminal activity, such as large sums of money exchanged among a group of newly-established companies registered in tax havens.
Machine learning models can be trained with data relating to three possible scenarios: lawful transactions, money transfers flagged as suspicious by bank alert systems, and potential money laundering cases reported to the authorities. For each case, machine learning systems analyze the senders’ and receivers' background and their transaction patterns. This way, they can spot the same patterns once they recur in future scenarios and therefore distinguish between legitimate activities and criminal actions.
3 Credit card fraud
Electronic payment fraud represents an increasingly widespread crime taking many forms and affecting almost any industry, from banking to ecommerce. However, machine learning systems have a good chance of containing this escalation by detecting abnormal account behavior that could be a sign of fraud. Some of the outliers to consider are increasing transaction amounts and frequency (especially to purchase luxury goods), payment on a card carried out significantly before the due date, associations with different high-risk accounts, and multiple payment methods added in a short time.
Machine learning-driven systems for payment fraud detection can also update card users' behavioral profiles after each transaction, making future predictions more precise and avoiding false positives.
4 Identity theft
Another type of crime strictly connected to payment card fraud and widespread in many scenarios, including fraudulent loan applications and ecommerce scams, is identity theft. This involves a scammer exploiting the personal information of another individual, including their name and credit card number, without their consent to commit a crime. To deal with this kind of fraud, businesses can probe users' habits and transaction data and look for anomalous behaviors using machine learning systems.
Furthermore, we can leverage machine learning-powered computer vision to analyze identity documents or add additional verification mechanisms such as face recognition and biometrics.
5 Account takeover
Cybercriminals use tools and techniques such as identity theft, phishing, and malware to gain access to a person’s or company’s login credentials and take control of their online accounts for data theft, fraudulent transactions, or other malicious activities.
ML systems can be trained on historical login events to recognize anomalous ones (for instance, from an unusual device or IP address) and therefore detect potentially compromised accounts, recommending further investigation by human reviewers or triggering automated actions.
6 Fraudulent insurance claims
Machine learning solutions can also strengthen fraud detection in the insurance sector, especially in health insurance. Algorithms can identify false and duplicate claims, pointing out a customer who reported an incorrect diagnosis or exaggerated medical coverage costs.
A key application of machine learning in healthcare is the analysis of unstructured data such as medical reports via natural language processing and computer vision. These solutions leverage machine learning to scan documents written by doctors, insurers, or clients, searching for suspicious inconsistencies.
7 Tax fraud
Public authorities can use machine learning to enhance businesses’ tax compliance. For example, ML systems can examine a company’s general ledger for anomalous entries which could be the signs of attempted fraud.
Algorithms can spot a wide range of clues easier and faster than human auditors, taking into account various parameters. These include monthly variations in companies' gross sales, relations among different taxpayers, inconsistencies among purchases, or itemized deductions in an income peer group.
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Real-world examples of ML for fraud detection
Consulting firm Capgemini has partnered with IT company Waylay to build a cloud-native fraud monitoring system powered by machine learning. Designed to quickly detect multiple types of fraud, including credit card fraud, phishing scams, and account takeovers, the solution can process up to 20 million transactions per day with a response time of less than 1 ms per transaction.
Scheme title: Capgemini’s fraud monitoring system operation
Data source: Capgemini
The leading South African banking and finance group implemented an ML-based solution built on top of Amazon Fraud Detector to automate low-risk insurance claim approval and better assess high-risk claims. This resulted in two times more fraudulent cases being identified and the turnaround time reduced from 48 hours to 6 hours, mitigating the business' exposure to risk and offering a superior customer experience.
Scheme title: Amazon Fraud Detector’s architecture
Data source: aws.amazon.com — Amazon Fraud Detector
For years, Nasdaq has been using a deep learning system to monitor trades, recognize fraudulent equity orders, and thus ensure transparent markets. Furthermore, Nasdaq’s subsidiary Verafin, which specializes in financial crime management, licenses its own cloud-based products for fraud detection to third parties. Its offering includes ML-powered solutions to prevent wire fraud, check fraud, and deposit fraud.
It [points] out what we call an ‘interesting event’. It’s not necessarily a prohibited activity, but it’s what the model has deemed to be interesting because it’s not normal market behavior.
How to set up an ML system for fraud detection
1
Business analysis
Identify fraud prevention-related needs and challenges via discovery workshops and process observations
Evaluate your current tech ecosystem
Define the solution’s functional and non-functional requirements
Outline the fraud detection system’s evaluation criteria
2
Initial data analysis
Perform an exploratory analysis to map available data sources (corporate databases and connected devices such as ATMs or POSs)
Identify external data sources (public records, law enforcement, or government watch lists)
3
Product design & implementation planning
Draw up a specification detailing the solution’s architecture, modules, core features, UX/UI design, and integrations with other software
Define a suitable tech stack to build your software
Optionally, deliver a proof of concept to ensure the project’s feasibility and financial viability while pointing out potential adoption challenges
4
Building the ML solution
Execute data preprocessing, including data cleansing, annotation, and transformation
Perform feature engineering to extract the most relevant attributes from data (customer's IP, preferred payment methods, number of failed transactions, average order value, fraud rate of the issuing bank, etc.)
Process your datasets with ML algorithms to train the model to recognize patterns and outliers, or build multiple models until you achieve the desired output
5
Model integration & deployment
Integrate the ML model into its intended solution to power its fraud detection capabilities with the model’s output
Deploy the system to the target environment (on-premises or cloud-based)
Configure all necessary API- or ESB-based integrations with other corporate systems and data sources
6
Support
Closely monitor the system’s operation and perform ongoing maintenance
Provide your staff with user training and support
Following MLOps' best practices, retrain the ML model with new datasets across multiple iterations to fine-tune its output and address model drift issues
Benefits of ML in fraud detection
The distinct mechanisms driving ML-based fraud detection and fraud prevention solutions make them superior to more traditional, rule-based systems in several respects.
Higher flexibility & reactivity
The rule-based approach is not flexible enough to deal with rapidly evolving fraud patterns. After all, the rule sets are manually coded and built on previous fraud scenarios, so they need to be continuously adjusted according to new types of events.
Thanks to self-learning abilities and sheer processing speed, ML-driven systems can deal with this process far quicker than rule-based tools and their human reviewers by adjusting machine learning models on their own based on new kinds of threats.
Wider data pool for analysis
Statistical and rule-based fraud detection methods can process structured data, such as transaction figures, but can struggle when handling unstructured data, for example, written reports, insurance claims, and pictures from IDs and other documents.
In the meantime, machine learning and its related technologies, such as natural language processing and computer vision, can deal well with any kind of data, enabling companies to analyze more criteria and variables across data points.
Lower rate of incorrect outcomes
The traditional approach tends to follow a "black or white" mindset. This results in a massive amount of false positives that don’t represent a real threat but still need to be double-checked through an expensive and time-consuming manual review.
Machine learning-based systems, instead, are far more accurate and cost-efficient even in complex scenarios, as they don’t rely on strict rules but search for patterns and dependencies among a wider amount of variables. This helps them to grasp some nuances, understand the logic behind a suspicious case, and avoid false alarms.
Superior compliance
The need for constant human intervention and interaction with data in rule-based systems can clash with the increasingly strict trading, fiscal, and data management regulations.
With less reliance on manual operations, the adoption of machine learning systems can minimize human errors and mitigate compliance-related business risk.
Challenges of ML for fraud detection
Despite their proven efficiency, ML-based fraud detection systems can be complex to develop and adopt. Here are a few tips to streamline their implementation while navigating potential constraints of this technology.
Issue | Recommendation | |
|---|---|---|
ML model interpretability |
A common dilemma when building an ML-based fraud detection system is to select suitable algorithms since
the best-performing ones are typically associated with the "black box" issue. For instance, random forests
and neural networks can easily identify non-linear relationships and therefore build very accurate models
portraying complex fraud events. However, their sprawling architectures make it difficult to interpret how
they generate outputs from certain inputs, negatively affecting the model’s explainability.
| The black-box nature of ML still remains an unresolved issue for professionals in this field. That said, ML engineers should strive to identify the right metrics to track model performance and thereby shed some light on its operation, along with the reasons behind potential bias and inaccuracies. |
False positives/negatives tradeoff |
Finding a reasonable compromise between security and a hassle-free user experience can be challenging. To
achieve maximum security, fraud detection software must be particularly strict and flag even vaguely
suspicious activities, which can end up being completely legitimate (false positives). On the other hand,
great user experience implies a wider tolerance range when assessing potential anomalies, which can result
in successful fraud (false negatives). Machine learning's low rate of false positives certainly mitigates
this issue, but algorithms are still far from being perfect.
| When creating a fraud detection model, it's important to train it on large validation datasets and identify a suitable threshold, namely the minimum conditions that will trigger a response (such as the number of failed login attempts). The right balance depends on the level of risk your business can afford. For example, a fraud detection system can be more tolerant when scanning large volumes of low-value transactions and more rigorous when probing premium product purchases. |
Data privacy concerns |
ML systems' reliance on large datasets for training and analysis can conflict with personal data
protection standards and applicable legislation (PCI DSS, GDPR, IFRS, etc.), especially in highly
regulated fields like finance and accounting.
| To make sure your solution complies with such regulations, anonymize your datasets through data masking techniques before they’re processed by ML algorithms for training or analysis. Additionally, equip the solution with security mechanisms like data encryption, identity and access management, and multi-factor authentication to prevent data breaches and leaks. |
Choosing between custom & platform-based fraud detection software |
Most companies would probably prefer a fraud detection solution that is fully tailored to their business
needs and scenarios. However, building custom software from the ground up can be demanding in terms of
data availability, ML model training efforts, and computational resources.
| Organizations with very specific fraud detection needs and established workflows should consider implementing a custom solution. On the other hand, any company looking to reduce upfront costs, implementation timeframe, and maintenance efforts can opt for one of the many off-the-shelf, machine learning-powered systems available on the market. These include Amazon Fraud Detector, IBM Security Trusteer, Sift's AI-PoweredFraud Decisioning, and Signifyd’s Authorization Rate Optimization. Alternatively, you can rely on the ML tools and services from major cloud providers (such as Azure Machine Learning or Amazon SageMaker), which offer built-in algorithms, pre-trained models, and scalable computing resources to help you create your own solution. |
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Fighting fraud at scale with machine learning
As our society moves towards full digitalization, fraudulent schemes and cyber attacks are increasing in impact, frequency, and complexity. ML-powered fraud detection solutions have lived up to the hype thanks to their adaptability to new threats, smart context-based data analysis, and real-time identification capabilities. At the same time, ML’s limited interpretability, appetite for data, and substantial training and computing requirements should be approached with proper expertise and compliance in mind. To smooth out the challenges of ML adoption, rely on an experienced partner like Itransition.
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