Credit Card Fraud Detection Using Artificial Intelligence -
Digital Business Transformation Solutions | Transpire Technologies
Credit Card Fraud Detection Using Artificial Intelligence


The Challenge

With the emergence of online payment systems, credit card fraud has become a significant issue in the modern era. It affects not only organizations but also consumers, banks, and merchants. Traditional means of detecting credit card fraud are insufficient. Machine Learning Models and Artificial Intelligence can be used in detecting illicit transactions.

In this case study, we will use ML algorithms to make an AI-based fraud detection system that monitors expenditure trends, identifies abnormal activities associated with card transactions, and flags them well ahead.

The Solution

Criminals commit credit card fraud in different ways and in several industries. AI-based systems are used in many detection methods that combine fraud detection datasets to provide visibility into valid and non-valid payment data, enabling better decisions.

Our data science team developed a model using ML algorithms that helped reveal and prevent fraudulent transactions.

Requirements for AI-Based Fraud Detection System

Running an AI-based system needed critical requirements. These were:

Amount of Data: We gathered internal historical data to train ML models. This data includes previous fraudulent and normal transactions so that we could train these models based on previous activities.

Quality of Data: We collected and sorted the data properly so that models were not subject to bias due to the nature and quality of the data.

Using Datasets to Train ML Models

We used a combination of supervised and unsupervised ML methods to make an AI-based detection system for advanced credit card fraud identification.

Unsupervised ML Methods

These methods usually use unlabeled data and help find patterns and dependencies in fraud detection data sets. This enabled us to group data samples by similarities without needing manual labeling. The methods we used:

One-Class SVM (Support Vector Machine): One-Class SVM is an ML algorithm that identifies outliers (anomalies) in data. We used this method to deal with imbalanced data-related issues for fraud detection.

Principal Component Analysis (PCA): PCA is a method for anomaly detection. We used this method to find correlations among features, including time, location, and amount of money spent. The combination of these features enabled the system to detect variability in the outcomes.

Isolation Forest (IF): It is another anomaly detection method different from other methods, as it detects anomalies precisely instead of profiling the positive data set. We used this algorithm to define fraudulent transactions by comparing them with legitimate transactions.

Supervised ML methods

These methods use labeled data samples, enabling the system to predict these labels unseen before data. The methods we used:

Naive Bayes: This is a probabilistic ML algorithm based on Bayes Theorem. We used this algorithm to measure the probability of the occurrence of credit card fraud and make a system based on it.

K-Nearest Neighbors: This algorithm uses proximity to make predictions or classifications about the grouping of individual data. It enabled composing larger datasets in less time and required less work from the developer to tune the mode.

We used a different combination of ML algorithms to make an AI-based system that could detect anomalies in transactions and allow or disallow a transaction to be made based on the decision.

Benefits & Outcomes

The ML algorithms used showed a higher level of accuracy.

By engaging ML techniques, the system was able to detect often subtle correlations between customer behaviors and fraudulent potential.

The anti-fraud detection system was able to monitor suspicious activity in real time.

The system could scan the operations for fraudulent activity and immediately flag them to take further steps.

Reduced risk of financial loss

Customer satisfaction due to simplified authentication and low probability of fraud.


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