Role Of Machine Learning In Fraud Detection

Technological advancements in the field of Machine Learning has led ML to find use in various crucial applications. Machine learning has been instrumental in resolving some of the critical business problems and optimizing various business processes. Machine Learning has been used for a variety of applications such as detecting email spam, customer retention, focused product recommendation, accurate medical diagnosis, etc.

Factors such as increased processing power, big data, and advancements in statistical modeling have all made use of machine learning more prevalent.

Fraud detection is an excellent application of machine learning. Fraud management is a painful, time consuming and costly process for banks. Over the years due to increased online transactions fraud detection and prevention have toped in banks priorities. It’s getting harder for businesses to authenticate transactions. Machine learning powered automated fraud screening systems can prove to be a savior for businesses in reducing fraud.

Until now banks go for manual reviewing process for fraud detection and prevention. Involvement of humans gives insights into fraud patterns and good customer behavior. These insights can formulate automated screening rules. But the manual review has been proved to be costly, time-consuming and is always susceptible to human error, leaving banks vulnerable.

Machine learning enables machines to learn from past experiences and formulate an appropriate response. It uses complex algorithms that recapitulate over large data sets and analyze the patterns in data. The algorithm enables the machines to respond to different situations that they might encounter in the future. Machine Learning find applications in spam detection, image recognition, product recommendation, predictive analytics, etc.

Machine learning significantly reduces the human effort in time-consuming tasks. Machines are much better than humans at analyzing vast sets of data. They are capable of detecting thousands of patterns on a user’s purchasing journey instead of the few captured by creating rules. Cognitive computing technologies to raw data can be used to predict the occurrence of fraud.

Certain crucial factors explain the importance of machine learning.

  • The first factor is speed. It’s very crucial to have a quicker solution to detect fraud. Machine learning can analyze vast numbers of transactions in real time and has the power to continuously analyze and process new data. Moreover, neural networks based advanced networks autonomously update its models to reflect the latest trends.



  • The second factor is the volume of data. Machine learning algorithms and models can handle vast data sets. In rule-based models, the cost involved in the fraud detection system multiplies as customer base increases. Machine-learning improves with more data by picking out the differences and similarities between multiple behaviors.



  • The third factor is efficiency; machines can perform repetitive tasks efficiently. Machine learning is more effective than humans at detecting subtle or non-intuitive patterns to help identify fraudulent transactions. Unsupervised ML models can continuously analyze and process new data and then autonomously update its models to reflect the latest trends.



Machine Learning is indeed proving to be a boon for fraud detection and prevention.