Step 1: Collect Data from Different Sources
AI fraud detection will start by collecting data relating to the transaction, customer account, device and user.
Some common data points include:
Transaction value, time of transaction, currency and method of payment
Customer account details including transaction history
IP address, device ID, geo-location
Login attempts and browsing habits
How often they pay and who they’re paying
Step 2: Feature Engineering
Feature engineering helps turn raw data into useful signals. This could be the average amount spent in the last 30 days, how many transactions have been made in the last hour, how far away from their usual location they are and if they’ve used this device before. Often having good features is more important than the algorithm being used.
Step 3: Model Training
The model is trained on historical data. If supervised learning is used it will be trained on labelled data (Fraud or Genuine). If it is unsupervised learning then it will not be labelled however it will learn what normal activity looks like and will identify any outliers.
Many banks use both methods. The supervised learning will identify known patterns and the unsupervised will identify new patterns.
Step 4: Generate a Fraud Risk Score
The AI fraud detection model will give it a risk score or probability of fraud. This allows the system to determine how it should treat the transaction.
For example:
Risk Level | Possible System Response |
Low Risk | Approve the transaction |
Medium Risk | Request additional authentication, such as an OTP |
High Risk | Block the transaction or send it for manual review |
Step 5: Decision and Action
The decision layer will take the risk score and decide what to do. Low risk scores will be approved, medium risk will ask for extra verification (one time password or biometrics) and high risk will block or pass to a human. This ensures that honest customers do not get declined unnecessarily.
Step 6: Feedback and Retraining
The decisions made by the analyst and confirmed fraud will be passed back into the training set. As fraudulent tactics are always changing the model should be regularly retrained and monitored.
Now after understanding the working of AI in fraud detection, now let’s explore what different types of AI and ML techniques are used.
AI and Machine Learning Techniques Used in Fraud Detection
There isn’t one algorithm that can solve all fraud problems. The choice of technique will depend on your data, the speed of decisions required and how much you need to explain results to the regulators.
1. Supervised Machine Learning
This requires labelled data.
Some commonly used supervised machine learning algorithms in AI to detect fraud include:
2. Unsupervised Learning and Anomaly Detection
Unsupervised methods such as Isolation Forest, Clustering Methods and Autoencoders look at what is normal within the data and identify any anomalies.
This means you don’t require labelled data which can be useful as there is rarely any confirmed fraud and it takes time to label data. This method is useful to uncover new types of attacks; however, it will usually give more false alerts than a well tuned supervised model.
3. Deep Learning
Neural networks are used in deep learning to find complicated patterns from big data sets. It can also recognise patterns between different features and can analyse data that might be harder for simpler algorithms.
Some deep learning approaches used in AI fraud detection include:
Artificial Neural Networks (ANNs): This model learns patterns from the features of transactions to enable classification of fraudulent activities.
Long Short-Term Memory (LSTM): This model analyses the sequence of transactions/user activities to identify abnormal patterns.
Autoencoders: This model learns how to encode normal data and can be used to detect abnormal observations.
Some research shows that American Express improved fraud detection by 6% using LSTM models, and that PayPal improved real-time detection by 10% with AI running around the clock.
4. Graph Analytics and Graph Neural Networks
Fraud often occurs due to relationships between accounts. Multiple accounts with the same phone number/device/address can indicate a fraud ring.
Graph analytics and graph neural networks can be used to visualise these relationships and uncover clusters which seem innocent on an individual basis.
These are widely used for cases of money laundering and synthetic identities.
5. Natural Language Processing and Transformer Models
NLP can be used within a fraud detection system to process and understand unstructured data like emails, payments, messages and documents.
By using NLP techniques, suspicious text, strange communication patterns and potential phishing can be detected.
Transformer models can be used to understand how words relate to each other and other features. This can help detect complex patterns.
Working Python Example
The following code creates a small AI fraud detection model using synthetic data. A random forest is trained on labelled data. An Isolation Forest is then run which doesn’t require labelled data. This can be run by installing scikit-learn, pandas and numpy.
python
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
rng = np.random.default_rng(42)
n_legit, n_fraud = 9500, 500
legit = pd.DataFrame({
"amount": rng.lognormal(mean=3.5, sigma=0.8, size=n_legit),
"hour": rng.normal(14, 4, n_legit).clip(0, 23),
"distance_from_home_km": rng.exponential(8, n_legit),
"txns_last_hour": rng.poisson(0.5, n_legit),
"is_new_device": rng.binomial(1, 0.03, n_legit),
"label": 0,
})
fraud = pd.DataFrame({
"amount": rng.lognormal(mean=5.2, sigma=0.9, size=n_fraud),
"hour": rng.normal(3, 2.5, n_fraud).clip(0, 23),
"distance_from_home_km": rng.exponential(120, n_fraud),
"txns_last_hour": rng.poisson(3.5, n_fraud),
"is_new_device": rng.binomial(1, 0.6, n_fraud),
"label": 1,
})
data = pd.concat([legit, fraud], ignore_index=True)
X, y = data.drop(columns="label"), data["label"]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, stratify=y, random_state=42
)
# Supervised: learns from past labelled fraud
clf = RandomForestClassifier(n_estimators=200, class_weight="balanced", random_state=42)
clf.fit(X_train, y_train)
scores = clf.predict_proba(X_test)[:, 1]
pred = (scores >= 0.5).astype(int)
print(classification_report(y_test, pred, target_names=["legit", "fraud"], digits=3))
# Unsupervised: flags unusual behaviour without labels
iso = IsolationForest(contamination=0.05, random_state=42).fit(X_train)
anomaly = (iso.predict(X_test) == -1).astype(int)
print("Anomalies flagged:", anomaly.sum(), "| fraud caught:", int((anomaly & y_test.values).sum()))
On synthetic data like this we can achieve around 98% precision and recall on our fraud class with the random forest. The Isolation Forest will catch most of the fraud without any labels. This data is very clean so do not take those results as a benchmark, just an example of how it could work. Teams will also adjust the score to get the right balance between missing fraud and declined transactions.
Popular AI Fraud Detection Tools
Several platforms and libraries support AI based fraud detection. Some provide ready-to-use fraud management systems, while others offer tools for building custom machine learning and anomaly detection workflows.
Tool or Platform | Key Capabilities | Common Use |
IBM Safer Payments | Uses machine learning, behavioural analysis, and transaction monitoring to identify suspicious payment activity | Banking and payment fraud prevention |
Feedzai RiskOps | Combines transaction, identity, and behavioural signals for fraud and financial crime monitoring | Banking, payments, and identity fraud |
Featurespace Platform | Uses behavioural analytics and real-time transaction monitoring to assess fraud risk | Payment, card, and application fraud |
Microsoft Fabric | Provides data streaming, machine learning, risk scoring, and monitoring capabilities for fraud detection workflows | Building custom fraud detection systems |
PyOD | Python library offering multiple anomaly detection algorithms | Developing custom anomaly detection models |
AI Fraud Detection vs Traditional Rule-Based Fraud Detection
Traditional fraud detection systems rely on rules that are created in advance to help identify fraudulent activity. AI-based fraud detection systems use machine learning models to look at past data, identify behaviours and make predictions about potential risk in new activities.
Both can help prevent fraud but differ in their ability to identify suspicious activity.
Factor | Traditional Rule-Based Detection | AI Fraud Detection |
Decision logic | Fixed if-then rules written by analysts | Patterns learned from data |
Signals considered | A handful per rule | Hundreds of signals at once |
Adaptability | Manual updates after new fraud appears | Retrains on fresh data and finds new patterns |
Detection of new fraud types | Weak until a rule is added | Better, especially with anomaly detection |
False positives | Often high because rules are rigid | Lower when models are well tuned |
Scalability | Rule lists get long and hard to manage | Handles very large transaction volumes |
Speed | Fast for simple checks | Real-time scoring in milliseconds |
Explainability | Easy to explain | Harder, needs explainability tools |
Setup effort | Low and quick | Higher, needs data, skills, and infrastructure |
Maintenance | Constant rule tuning | Monitoring, retraining, and drift checks |
This is used within AI fraud detection in banking, by using a combination of rules (for the ‘black and white’ scenarios such as sanction screening) and AI (for the grey areas). This enables teams to have faster, transparent results which can give improved detection.
Conclusion
Overall Artificial Intelligence can be used to help organisations stay one step ahead of fraud. However, reliable data, continual monitoring of models, robust security measures and human supervision are still a necessity. Fraudsters will always try to find new ways to trick businesses and customers and so fraud detection systems need to evolve.
