Credit Card Fraud Detection System for Bill Payments App

Author: Shivam Pal

Welcome to the Data-Driven Growth blog! Today, we’re diving into an exciting topic: Building a Credit Card Fraud Detection System. With the rapid advancement of technology, credit card fraud has become a significant concern, especially for applications handling online payments. As fraudsters become more sophisticated, a robust fraud detection system is essential to protect consumers and businesses from financial losses and maintain trust in digital payment systems.

Different Kinds of Credit Card Frauds

Credit card fraud can manifest in various ways:

• Card Not Present (CNP) Fraud: This occurs when the physical card is not required for the transaction, making online purchases vulnerable.
• Skimming: Fraudsters use devices to capture card information during legitimate transactions.
• Phishing: Scammers trick users into providing their card details through deceptive emails or websites.
• Card Theft: Stolen cards used for unauthorized purchases.
• Account Takeover: Hackers gain access to users’ accounts and make transactions without their knowledge.

Potential Losses from These Frauds to Companies and Consumers

The impact of credit card fraud is substantial and multifaceted:

• Financial Losses: Both businesses and consumers suffer direct financial losses. Companies may face chargebacks and lose revenue, while consumers might temporarily lose their money until issues are resolved.
• Reputational Damage: Businesses can suffer reputational harm if customers feel their data is not secure.
• Operational Costs: Companies need to invest in fraud detection systems and handle the operational burden of managing fraud cases.

Key Insights from Data Exploration

1. There were users on the app who had used up to 80+ cards to make payments through a single user ID.
2. 98% of users have used up to 4 cards, and in the rest, 2%, the users’ unique card number goes from 5 to 80.
3. These 2% of users are making around 20% of transactions on the app out of all transactions and contributing to around 25% of revenue transactions.

Our Approach to Building This Solution

In our bill payments app, we adopted a straightforward yet effective approach to detect potential fraud:

• Tracking Unique Credit Cards: We monitor the number of unique credit cards used by each user. The rationale is that fraudsters often use stolen cards for large, one-time payments.
• Threshold-Based Flagging: If a user utilizes more than four unique credit cards, our system flags the account for further investigation. This threshold helps identify suspicious behavior without impacting regular users who might use multiple cards occasionally.

Potential Applications for These Kinds of Systems

Fraud detection systems like the one described have broad applications across various industries:

• E-commerce Platforms: To detect and prevent fraudulent transactions, protecting both the platform and its users.
• Banking and Financial Services: These are used to monitor unusual account activities and prevent unauthorized transactions.
• Subscription Services: To ensure that accounts are not being accessed fraudulently using stolen credit cards.
• Online Marketplaces: To safeguard against fraudulent listings and purchases.

By implementing these measures, we can create a safer environment for online transactions and foster trust among users and businesses alike.

Thank you for reading! Stay tuned for the next article, where we’ll go deeper into the applications of machine learning for business growth.