Credit Card Fraud Detection Using Machine Learning

Overview of Credit Card Fraud Detection

Online transactions entail the transfer and storage of delicate data (financial information, authentication information, passwords, etc), and the ill-usage of this data leads to a host of frauds, credit card fraud being one of the major threats. Online Shopping is a fast-growing trend. Mode of payments involving Credit Card, Debit Card and Net Banking is prone to frauds. Credit Card Fraud occurs Online as well as Offline. Hackers don't leave a chance to steal information and breach security.

Evasion can be bankruptcy, behavioral, application or theft. Case investigations, customer phone support, and damage to an institution’s reputation also contribute to the cost of fraud. With the cost of fraud climbing and cardholder trust backsliding, companies need to understand steps to ensure their business and their cardholders are protected. Let’s review and explore what are challenges and build a Credit Card Fraud Detection System using Machine Learning.

Challenges for Credit Card Fraud Detection

Fraud Detection transactions are rare and represent a small fraction of total transactions within an organization.
Analyze past credit card transactions with fraud ones.

Solution Offered for Enabling Fraud Detection with Deep Learning

Fraud Detection Model follows the Random Forest algorithm which takes hundreds of decision trees and aggregates and builds the model to avoid overfitting due to class imbalance.
Analysis of at least 20-50 last transactions of the customer is done to identify an underlying pattern.
Python and Machine Learning used to build the model.

Credit Card Fraud Detection Process and Techniques

Real-Time Fraud Detection requires time between transactions, History of transactions, custom transaction behavior. Fraud detection involves the identification of vulnerabilities, finding hidden defects, detection of transactions and evaluation of workloads.

Steps for Credit Card Fraud Detection

Fraud Cleaning including Credit Card Number, CVV, address, phone number.
Data Extraction using the training dataset, testing, and cross-validation through predictions.
Build Models using Logistic Regression, Decision Tree, Random Forest, and Neural Networks.
Feature Engineering
Streaming Data Ingestion
Real-Time Fraud Prediction using Spark Streaming
Storage of Credit Card Events