GBG, the global technology specialist in fraud and compliance management, identity verification and location data intelligence, recently announced its expansion of AI and machine learning capabilities for its transaction and payment monitoring solution, Predator.

Asia Blockchain Review recently caught up with June Lee, APAC’s Managing Director Of GBG for her thoughts on things. This is the third and final part of our interview series.

Give us an overview of the current situation with AI and ML solutions in the APAC region that are specifically focused on payment monitoring. Are companies in APAC aware of the need for these kinds of strategies? 

Machine Learning (ML) is gaining importance for organizations looking to use such technologies in the fight against fraud as the regulators increased their openness to these advanced analytics techniques.

With the move to digital real-time payment, fraudsters and organized rings have increased their range of attacks via alternative channels; this in turn fueled the growth of AI and ML adoptions with the need for real-time assessment.

Fraud and Anti Money Laundering (AML) machine learning platform are an active area of investment for businesses in the region to help identify, alert and investigate suspicious and fraudulent activity; a research by the Association of Certified Fraud Examiners‎ (ACFE) shows that predictive analytics and ML is gaining in adoption.

ML has the ability to help identify new fraud typologies (such as identity theft, account takeover and online banking fraud) as an overlay to existing technologies to help reduce false positives, improve operation efficiency and customer experience.

In the current COVID-19 time, a number of our customers shared that they are unable to operate the same way as they did in the past. Companies in APAC have always been driving digital transformation, and COVID-19 has further accelerated the adoption.

Indeed, in terms of planned budget, Machine Learning has been touted as among the top three biggest investment to combat fraud by 31% of financial institutions in APAC in 2020. Organizations are generally aware of the gaps in their technology adoption and the internet of things (IOT) world they need to thrive in.

They are seeing an increase in data explosion. Respondents in our research attests that their average number of transactions is at 462 million per year. The projected transactions are at a 6% increase.

For peak period, the current number of transactions is at 611 million per year, also with the 6% projected increase for the next year. With the massive amount of data to crunch per second, we see machine learning becoming more integral in preventing financial frauds across sectors like finance and banking, eCommerce, cryptocurrency and online gaming in APAC.

Finally, what do you see in your crystal ball for the future of these kinds of solutions that GBG and other companies offer in this space? How will a company like yours stay ahead of the curve?

While we have invested in AI and ML technologies for some time in all our solutions and would still continue to do so, our R&D team is also looking at ways to upscale our ML to meet new needs, improve performance and user experience to grow with our customers.

As online fraud is eight times more frequent than in physical branches, we need to continuously re-invent ourselves to deal with the ever-changing landscape. We will continue to discuss with customers on new needs and collaborate with other innovative technology and data organizations to provide comprehensive services to anticipate and address our customers’ future needs.

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About the author

Anil Prabha

Editor In Chief

Anil started his career in journalism all the way back in 2003. After traversing the sphere of editorial, corporate communications and advertising, he has now come full circle and is back in the world of journalism. He believes in the power of the written word, and its ability to enthrall, delight and inform the reader.