According to GBG’s APAC Covid-19 fraud risk poll results, digital retail banking services such as e-wallet, e-loan, digital onboarding and digital credit card applications are growing in demand.
37 percent of the respondents also see transaction fraud as the fraud typology that they are most vulnerable to.
According to June Lee, Managing Director, APAC, GBG, standard fraud model deteriorates over time exposing businesses to new fraud typologies and fraud losses.
The global technology specialist in fraud and compliance in its efforts to tackle the issue announced its expansion of AI and machine learning capabilities for its transaction and payment monitoring solution.
‘Predator’, its latest launch aims to make deep learning and predictive analytics available to their entire digital risk management customer journey.
“The ability to easily spot complex fraud and misused identities
in first party bust outs and mule payments, high volume and high velocity frauds such as online banking account takeover and card not present frauds across both onboarding and ongoing customer payments becomes more pressing today,” said Michelle Weatherhead, Operations Director, APAC, GBG.
“In addition, segments like SME lending and microfinancing would be able to harness machine learning to spot irregularity in borrower patterns by assimilating both identity, profile and behavioural type data. GBG Machine Learning is able to analyse large sums of data using algorithmic calculations on multiple features to determine fraud probability in greater accuracy,” said Alex Low, Data Scientist, GBG.