METHOD FOR DETECTING FRAUDULENT CREDIT CARD TRANSACTIONS

Abstract

ABSTRACT A method (100) for detecting fraudulent credit card transactions. Further, the method comprising collecting transaction data from a plurality of credit card transactions in real-time. Further, the method (100) comprising the steps of pre-processing the collected transaction data to extract relevant features indicative of transaction patterns. Further, the method (100) comprising the steps of applying a machine learning model trained using unsupervised learning techniques on the pre-processed transaction data. The machine learning model comprises a neural network architecture. Further, the method (100) comprising the steps of identifying anomalies in the transaction data based on the output of the machine learning model. The anomalies indicate potential fraudulent transactions. Further, the method (100) comprising the steps of generating an alert for the identified potential fraudulent transactions to enable quick intervention by financial inst

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of financial technology, and in particular, relates to a method for detecting fraudulent credit card transactions using machine learning techniques that leverage unsupervised learning and neural network architectures to enhance the accuracy and efficiency of fraud detection systems, ultimately aimed at minimizing financial losses and improving security for consumers and financial institutions.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0 , Claims:1. A method (100) for detecting fraudulent credit card transactions, the method comprising the steps of:
collecting transaction data from a plurality of credit card transactions in real-time;
pre-processing the collected transaction data to extract relevant features indicative of transaction patterns;
applying a machine learning model trained using unsupervised learning techniques on the pre-processed transaction data, wherein the machine learning model comprises a neural network architecture;
identifying anomalies in the transaction data based on the output of the machine learning model, wherein the anomalies indicate potential fraudulent transactions; and
generating an alert for the identified potential fraudulent transactions to enable quick intervention by financial institutions or cardholders.

2. The method (100) as claimed in claim 1, wherein the machine learning model is further optimized using a K-means clustering algorithm to enhance the accuracy of anomaly detection in the transaction data.

Documents