How machine learning can help detect payment fraud?
Blog: Tommy Vu
Every day, banks must fight against fast-evolving and complex financial crime threats like money laundering, identity theft, terrorist financing, credit card fraud and payment fraud. One of the most common types of financial crime, payment fraud, happens when a card’s information is stolen and illegally used for payments.
Needless to say, it’s crucial that banks protect their clients against payment fraud. Clients need to be able to trust that their bank account information is protected and not being used by someone else under any circumstances.
While banks have been quick to adopt machine learning across different services such as process automation, risk management and investment prediction, many have yet to put it to proper use for payment fraud detection.
Why using machine learning to detect payment fraud makes sense
The rapid growth of e-commerce and digitalization in financial institutions has benefitted society in many ways—take, for example, electronic payments, which are becoming more common. Unfortunately, there’s also a downside to this growth: it can create more opportunities for payment fraudsters. This is one reason why the rate of payment fraud is on the rise and the nature of the fraud itself is becoming even more complex.
This increasing complexity, and the fact that fraudsters continually change their tactics in fraudulent transactions, means that human decision-making and old transaction alert systems are no longer fast and effective enough to detect fraud.
With the help of machine learning, however, banks are able to better detect the changing tactics of fraudulent transactions. A tremendous amount of payment transactions happen every day, which increases the volume of banks’ payment transaction datasets at a fast speed. In this data lie many valuable hidden insights, which can be used to detect fraud. Through human decision-making and machine learning algorithms that have the ability to learn from these datasets, payment fraud detection will be faster and more effective.
The benefits of leveraging machine learning in payment fraud detection
- Reduces operational cost – There’s no need to spend as much time and resources on reviewing every alerted transaction due to better accuracy and automated prediction.
- Detects and prevents payment fraud more effectively – Machine learning can quickly adapt to new behaviors of fraudulent transactions and helps to improve reactions to suspicious outliers.
- Reduces false positives
- Works with large datasets – Machine learning is better than humans at processing large datasets and its prediction results improve as datasets grow.
What to consider next?
- How costly is it to miss actual fraudulent transactions?
- How do you think fraud will affect your clients’ trust?
- Is your current system effective enough to detect fraud?
- How much time and resources are spent on the fraud review process?
If the above considerations are relevant to you, it might be time to consider applying machine learning in payment fraud detection. If you need a hand with fraud detection, we’re happy to help by leveraging the benefits of useful machine learning algorithms.