Machine Learning and Digital Payments

(image source: crn.in)

Payment systems generate a lot of (structured) data because of industry standards, which is almost ready for use in machine learning algorithms. ML has fraud-fighting capacity and evaluation of huge numbers of transactions and helps identify fraud and avoids erroneous ‘false positives’ In fraud detection, a “false positive” occurs when something innocent is wrongly deemed suspicious. Credit card holders encounter false positives when a cardholder accidentally trips the card issuer’s fraud detection system. False positives can cause a cardholder’s transaction to be denied or an account locked down.

ML can draw on vast amounts of available data on payment process to profile customers and guess their product needs, offering new opportunities for upselling.

Machine learning in payments can be utilized in a wide range of situations SLIDES (16-19), ranging from using data to complete the KYC (Know Your Customer) procedures entirely online, near real time authorization of transactions, changing the way people invest, predicting borrower delinquency, to improving customer service. Machine learning algorithms can constantly evaluate huge amounts of data on loan repayments or company stocks or spending patterns and predict trends that can have a huge impact on lending, insurance, and access to credit. The warning systems can also be used by financial institutions to spot irregularities, predict frauds, reduce risk, and provide insights on what to do in case of fraud.