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CIO Applications Weekly Brief

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  • Machine Learning
Editor's Pick (1 - 4 of 8)
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Democratizing Machine Learning Algorithms for Integrated Data-Sharing

Democratizing Machine Learning Algorithms for Integrated Data-Sharing
Andre Mutovic, CIO, ION Media Networks

The Tango of AI and Big Data

The Tango of AI and Big Data
Monica Khurana, CTO/CIO in Financial Services, Guardian Life Insurance

Transforming the Art Museum in the 21st Century.

Transforming the Art Museum in the 21st Century.
Jane Alexander, CIO, Cleveland Museum of Art

Machine Learning: The Future from the Perspective of Model Building

Machine Learning: The Future from the Perspective of Model Building
Markus Levy, Director of Enabling Technology, NXP Semiconductors

The State-Of Machine Learning Adoption in the Enterprise

The State-Of Machine Learning Adoption in the Enterprise
Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation

Inside the Latest in Machine Learning

Inside the Latest in Machine Learning
Troy Lau, Group Leader in Machine Intelligence, Draper

Recent Trends in Machine Learning

Recent Trends in Machine Learning
Sears Merritt, Head of Data, Strategy & Architecture, MassMutual

Balancing Accuracy With Interpretability in Machine Learning Models

Balancing Accuracy With Interpretability in Machine Learning Models
Joshua Mabry Expert in Advanced Analytics practice

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No to Frauds by Implementing ML

By CIO Applications| Tuesday, June 25, 2019
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machine learningMalware and fraudulent attacks are on the rise as the amount of data has exponentially risen throughout the web. Only way to tackle them is through the deployment of ML algorithms.

FREMONT, CA: Online fraud is not as simple as people think, as fraudsters adapt quickly to changing techniques. Their sophisticated toolbox now includes ML algorithms and bots to commit fraud at scale. The legacy move toward fraud-fighting entails static systems like rule engines. Rule engines are stiff and hard-coded and once implemented, treat fraud as though it is either black or white.

Many industries have realized that rule-based engines are inadequate as they have proved to be inflexible and are not capable of scaling. Those businesses, which were earlier nonchalant and ignorant to fraudulent behavior, have shifted to ML in keeping with changing times. This is crucial in an era when new types of threats―account takeover, fake content―arise almost every day. The rules system can only be applied to a single vector of fraudulence, whereas Machine Learning (ML) is successful across multiple channels.

Businesses launch a new product or an application first and worry about fraud detection and prevention later, so risk assessment teams are often left to pick up the after-effects after an attack that has already taken place. To make the circumstances even worse, fraudsters have begun to adopt advanced technologies such as ML and bots, and businesses have to fight fire with fire. Companies that do not succeed to focus on fraud alleviation rather than avoidance gets left behind in the race.

When appropriately managed, ML can clearly distinguish between genuine and fraudulent behavior while acclimatizing over time to new or previously unnoticed tactics. The facets of digital fraud prevention are quite complex as the ML programs need to interpret patterns in the dataset and identify between normal and abnormal behavior. The computational process requires thousands of calculations to be performed precisely within milliseconds. Without a proper perception of the domain, as well as fraud-specific techniques, enterprises can employ ML algorithms to aid them to prevent malware attacks and fraudulent assaults.

Check out: Top Fraud And Breach Protection Companies

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