Key Challenges for Adoption of AI Fraud Detection Technology

Online fraud is advanced, nuanced, and constantly changing. As a result, it necessitates a constructive rather than a reactive response. Traditional businesses that are dipping their toes into the online space for the first time must change their attitude and process.

Fremont, CA: Cybersecurity is a broad subject that covers a wide range of problems and vulnerabilities. From stealing customer information through data breaches to hacking into major elections, no part of the digital world is secure.

Each of these distinct vulnerabilities is also linked to one another. Any time a cybercriminal hacks an email, leaks confidential information, or steals information for personal gain, it sends a message that such actions pay off and encourages fraudsters to push the boundaries and see how far their illegal activities can go.

AI is increasingly being designed to identify and prevent fraud, preferably before it occurs. However, two major obstacles prevent many businesses from implementing advanced AI fraud detection technology:

Insufficient Data Infrastructure to Support Machine Learning

Big Data has become a top priority for major digital players such as Google and Facebook, and they have the resources to support it. However, this is not the case for Main Street, USA. Many small to medium-sized companies who are just getting started with their online presence are not fully aware of the risks they may face online and may even feel they are too small to be noticed by a cyber-criminal.

To be sure, these companies likely gather information about their clients, website traffic, and social media engagement. However, they may lack the data infrastructure required to analyze user activities and behaviors in order to develop a baseline understanding of what fraud looks like. Since AI and machine learning operate by "learning" from data, a lack of data to feed the system can stymie the learning curve, especially in the case of supervised machine learning.

Many businesses that are aware of the risks associated with online fraud wonder whether they should begin with an AI machine learning solution or whether they will be better off introducing gradual solutions first, thinking (incorrectly) that machine learning is too technologically advanced for their current state.

New Entrance of Traditional Businesses in the Online Space

Traditional companies have had fraud prevention and identification policies in place for several years, but such safeguards were not planned for the modern world. Traditional companies are struggling to find their feet in a modern world, one with rapidly changing risks and challenges, as digital transformation accelerates through a wide swath of industries and the increasingly digital nature of consumer interaction.

Online fraud is advanced, nuanced, and constantly changing. As a result, it necessitates a constructive rather than a reactive response. Traditional businesses that are dipping their toes into the online space for the first time must change their attitude and process.

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