Fraud Detection: AI algorithms can analyze transaction patterns and detect unusual activities or potential fraudulent transactions in real-time.
Credit Scoring: AI can assess a customer's creditworthiness by analyzing various data points beyond traditional credit scores, leading to more accurate and inclusive credit decisions.
Customer Service: AI-powered chatbots and virtual assistants can provide instant responses to customer inquiries, handle disputes, or assist with account management.
Personalized Offers: By analyzing spending patterns and behavior, AI can tailor promotional offers and rewards that are more relevant to individual cardholders.
Optimizing Credit Limits: AI can analyze spending habits and financial behaviors to determine appropriate credit limits for cardholders, reducing the risk of defaults.
Transaction Categorization: AI algorithms can automatically categorize transactions, making it easier for cardholders to track and manage their spending.
Risk Management: AI can help banks assess and manage the overall risk associated with credit card portfolios by identifying potential vulnerabilities and recommending mitigation strategies. By integrating AI into credit card processing, banks can enhance efficiency, improve customer experience, and mitigate risks more effectively.
Credit card processing involves several steps to facilitate a transaction between a cardholder and a merchant. Here's a simplified overview:
1. Card Swipe or Input: The cardholder presents the credit card for payment. The merchant swipes the card through a card reader or manually inputs the card details if it's an online transaction.
2. Authorization Request: The merchant's payment system communicates with the credit card network (e.g., Visa, Mastercard) to request authorization for the transaction amount. The network forwards this request to the card-issuing bank.
3. Bank Approval: The card-issuing bank reviews the authorization request. If the card has sufficient credit, is not reported stolen, and the transaction seems legitimate, the bank approves the transaction.
4. Transaction Processing: Once approved, the transaction is processed, and the merchant receives an authorization code.
5. Batching: Throughout the day, the merchant accumulates approved transactions. At the end of the business day, these transactions are batched together to be settled.
6. Settlement: The merchant sends the batched transactions to their acquiring bank (also known as the merchant bank). The acquiring bank then sends the transactions to the credit card network for processing.
7. Funding: The credit card network routes the transactions to the appropriate issuing banks to transfer funds. The acquiring bank credits the merchant's account for the settled transactions, minus any fees.
8. Statement and Fees: At the end of the billing cycle, the merchant receives a statement detailing the transactions and associated fees. These fees often include interchange fees (paid to the card-issuing bank) and merchant service fees (collected by the acquiring bank).
This process ensures secure and efficient handling of credit card transactions, allowing businesses to accept electronic payments from customers. Additionally, credit card issuing banks employ various methods and sophisticated technologies to detect and prevent fraud.
Fraud Analytics: Banks use advanced algorithms to analyze transaction patterns. If a transaction appears unusual, such as an unusually large purchase or a purchase from a location the cardholder doesn't typically frequent, it may be flagged for review.
Real-time Monitoring: Many banks monitor transactions in real-time. If a transaction seems suspicious, the bank might contact the cardholder immediately to verify the transaction's legitimacy.
Geolocation Services: Some banks use geolocation data to verify the location of a transaction. If a card is being used in a location different from the cardholder's usual location, it may trigger a fraud alert.
Transaction Velocity Monitoring: Rapid consecutive transactions, especially in high amounts, can be a sign of fraudulent activity. Banks monitor transaction velocity to detect such patterns.
Device Fingerprinting: Banks analyze the characteristics of the device used for a transaction, such as its location, type, and behavior. If a device is associated with suspicious activity, transactions from that device may be flagged.
Machine Learning and AI: Advanced machine learning models are trained on vast amounts of data to detect fraudulent patterns. These models can continuously learn and adapt to new fraud tactics, enhancing detection accuracy over time.
Card Verification: Security features like CVV (Card Verification Value) and 3D Secure (often referred to as Verified by Visa or Mastercard SecureCode) add an extra layer of verification for online transactions.
Identity Verification: For high-risk transactions or when there's suspicion, banks may employ additional identity verification methods, such as sending a one-time password (OTP) to the cardholder's registered mobile number or asking security questions.
By combining these methods and constantly updating their fraud detection systems, credit card issuing banks strive to protect cardholders from unauthorized transactions and minimize the financial impact of fraud.