How can machine learning help in fraud detection for fintech companies?

By Kunal Varma, MoneyTap

Today’s fast-moving era demands everything quick and easy. Technology and financial services are no exception.

The need for real-time, instant and 24×7 access to mobile wallets, instant credit and other banking services and products are real. Keeping up with this demand, fintechinnovations have made it possible to have the luxury of seamless access to financial services.

However, this luxury has made us vulnerable to cyber crimes, online frauds and data theft. Fintech and finserv companies are realising this vulnerability and they are turning to machine learning and Artificial Intelligence for better security.

The most common frauds include card skimming, virus attacks with malware to steal user’s confidential data and phishing. Identity thefts and user’s personal data theft are a great danger too.

Every financial institution follows the below steps in fraud detection:

– Observe and analyse user’s action.
– Determine if it is in line with past behaviour or there is a deviation.
– Decide if it should be treated as a fraudulent activity or not.

The traditional system follows a predefined set of rules used as checkpoints for fraud prevention.

For example, the financial institution may have a rule that if a customer adds more than a certain number of credit cards to his account in a day, raise a red flag. Other warning points could be behavioural things like unusually large transactions or atypical locations.

However, with so many transactions happening in digital space each second, this system cannot keep up. It also requires human adjustment. Cybercriminals can easily circumvent around these red flags. Hence the financial organisations require machine learning as a much-advanced approach to fraud detection.

Advanced Fraud Detection System:

The distinct feature of machine learning is its capability to self-learn. As more and more data accumulate, the algorithms get better resulting in an overall increase in efficiency and accuracy in detecting fraudulent activities.

ML-based algorithms can read the subtle correlations between the user’s behaviour and the likelihood of a fraudulent action. It can read and analyse large data in seconds including images and texts.

There are two types of machine learning used for advanced fraud detection system: supervised and unsupervised. Supervised ML is fed historical data labelled as fraudulent or not-fraudulent and the algorithm then uses this data to recognise any fraudulent activity.

Unsupervised ML is just fed large data and it can recognize the anomalous behaviour or any malicious attack by learning and building the data. These two types can be used independently or in combination to create a robust fraud detection system.

Machine Learning Reduces False Positives

While detecting the possibility of fraud, the traditional system sometimes reads a normal transaction as fraud and stops it. This is called false positive. This decline is undesirable as it often results in a shift in customer loyalty. Because machine learning algorithms are more accurate, it helps to minimise the huge losses incurred by banks because of false decline or false positives.

Future machine learning based security system can also include face recognition. ML can analyse and remember the network of veins in user’s eyes. This can help minimise the possibility of misuse of user’s confidential information.

There is no doubt that machine learning is the weapon to fight increasingly sophisticated and intelligent frauds happening worldwide. As the fintech and finserv companies expand and become the face of digital India, adopting machine learning might be the best way to move ahead.

(The author is co-founder, MoneyTap. Views expressed above are his own)