Machine learning enhances Credit Card fraud detection by 94%: AMF

Machine learning could significantly improve credit card fraud detection, increasing its efficiency by more than 94%, as revealed in a study by the Arab Monetary Fund (AMF). The research emphasizes the pivotal role of artificial intelligence in bolstering the detection of fraudulent credit card activities. In 2021, credit card fraud caused a global loss of $32.3 billion, marking a 13.8% increase from the year prior.

The AMF's findings recommend further innovation and suggest partnering with leading fintech companies to devise machine learning-driven fraud detection mechanisms. The report underscores the importance of employing AI and ML tools for monitoring credit card fraud in the Arab world, especially given the region's surge in credit card usage.

On a related note, a recent report from GlobalData, a London-based analytics company, projected that Saudi Arabia's card payments industry will grow 14.6% to hit SR532.1 billion ($141.9 billion) in 2023. This growth is attributed to the popularity of contactless payments and Saudi Arabia's transition to a more digital society. In 2021 and 2022, the card payment value in Saudi witnessed a growth of 29.8% and 17.3%, respectively, boosted by economic upturns and increased consumer expenditure.
Ravi Sharma from GlobalData commented in May that while cash was historically favored in Saudi Arabia, a shift is occurring towards electronic payment methods.

Governments enact stricter regulations to curb financial fraud in response to the escalating use of digital payments. For instance, in July, the Dubai Public Prosecution initiated a crackdown on credit and debit card forgery activities, issuing a warning that culprits could face fines ranging from 500,000 dirhams ($136,127) to 2 million dirhams or imprisonment.

Dubai Public Prosecution stated that anyone guilty of forging, duplicating, or replicating payment methods using IT solutions would be subject to these penalties. Furthermore, Saudi Arabia declared heavy penalties this year, including a $1.3 million fine and a five-year prison sentence, for those found guilty of forging or using counterfeit electronic records, signatures, or digital certificates.