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.

1. Data Science
Session 1
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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.
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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.
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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.
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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 finds applications in
spam detection, image recognition, product recommendation, predictive analytics, etc.
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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.
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7. Copyright © Global Tech Council www.globaltechcouncil.org
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.
7

8. Copyright © Global Tech Council www.globaltechcouncil.org
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 finds applications in
spam detection, image recognition, product recommendation, predictive analytics, etc.
8

9. Copyright © Global Tech Council www.globaltechcouncil.org
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.
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10. Copyright © Global Tech Council www.globaltechcouncil.org
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.
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