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Significance of machine learning for fraud detection in digital banking

Banking fraud has been an ever-growing issue with far-reaching consequences for the banking and financial industry in terms of financial losses and credibility.

Cybercriminals are getting even smarter day by day and leveraging technical advancement for their own benefits. Hence there is a clear need for better fraud detection models and robust fraud management models in the banking and financial industry given that most transactions are digital nowadays.

In recent years, there has been an exponential increase of transactions due to the emergence online payment systems. Proportionately, fraudsters have become smarter and savvy; traditional bank fraud detection systems are no longer sufficient enough to combat sophisticated fraudulent behavior. To keep pace with continuously evolving technology and the complexity and volume of cyber intrusions, machine learning and other predictive algorithm analyses are greatly beneficial.

How does machine learning work to detect fraud?

Fraud detection and risk management programs using machine learning start by gathering and grouping previously recorded data delineating between legitimate and fraudulent transactions.

It is good to have as much data as possible, so that theoretical fraud patterns can be continuously verified and re-verified. Once fraud patterns are confirmed, the machine learning algorithm is “trained,” or taught to group, analyze, and report on data according to the established fraud pattern. The program is now capable of routinely and efficiently identifying fraudulent transactions from legitimate transactions in a bank’s broader fraud management framework. The algorithm will need to be updated from time to time, as the profile of threats and intrusion attempts continue to evolve over time.

Benefits of machine learning in fraud detection 

Many modern analytics are still largely dependent on humans to analyze data and detect suspicious transactions and fraudulent activity. This dependency is prone to issues like slow speed and human error. The use of machine learning can solve these issues, avoid cost through the use of technology versus human resources. Specifically, machine learning benefits include: 
Speed – Machine learning algorithms have the ability to continuously collect and analyze new data in real-time, optimizing fraud detection before intrusion, and avoiding costs of remediation.
Cost Efficiency – Machine learning algorithms can detect subtle changes in patterns and perform repetitive tasks across large amounts of data. Algorithms can analyze thousands of payments per second, which is more work than several human analysts can do in the same amount of time. 
Scalability – As previously mentioned, the more data, the better the process. The program improves as more data comes in, enabling it to detect fraud faster with better accuracy. 
Accuracy – Machine learning algorithms can be trained to analyze and detect patterns across seemingly insignificant data. They can identify subtle or non-intuitive patterns which would be difficult, or maybe even impossible, for humans to catch. This increases the accuracy of fraud detection, meaning that there will be fewer false positives and frauds that go undetected. 
Final Thoughts:
Nallas can be a reliable technical partner in the digital transformation of banking businesses. We combine best practices of digital engineering & industry expertise to help clients drive digital transformation at scale. With an elite team of engineering professionals, we provide cutting-edge solutions to overcome security breaches for the banking and financial industry.