The rise of real-time payments has led to an increase in social engineering frauds, where fraudsters deceive victims into sending money to fraudulent accounts. Given the rapid settlement of these transactions, retrieving the transferred funds presents a formidable challenge. Furthermore, traditional data sources, such as credit reports and transaction history, while valuable, offer a limited perspective on consumer behaviour. Immediate intervention is crucial to prevent such frauds and minimize financial losses. Prompt detection and reporting of fraudulent transactions are essential, as any delay significantly diminishes the chances of fund recovery. Additionally, leveraging alternate data sources provides a more sophisticated approach to identifying and mitigating fraudulent activities, offering deeper insights into users' behaviour, lifestyle, and financial strength.
Participants are encouraged to develop solutions which may in real-time, help in detection and prevention of frauds using AI/ML, API, Data Analytics and other cutting edge technologies. These systems should not only accurately identify fraudulent activities but also minimise false positives, using various data sources such as behavioural, biometric and social media data, quickly report and prevent further frauds, thus enhancing recovery prospects. Furthermore, participants are encouraged to utilize novel technologies to gather alternate data sources, enabling innovative use of information available in public domain to predict fraudulent transactions and identify potential red flags overlooked by traditional data sources.
The rise of real-time payments has led to an increase in social engineering frauds, where fraudsters deceive victims into sending money to fraudulent accounts. Given the rapid settlement of these transactions, retrieving the transferred funds presents a formidable challenge. Furthermore, traditional data sources, such as credit reports and transaction history, while valuable, offer a limited perspective on consumer behaviour. Immediate intervention is crucial to prevent such frauds and minimize financial losses. Prompt detection and reporting of fraudulent transactions are essential, as any delay significantly diminishes the chances of fund recovery. Additionally, leveraging alternate data sources provides a more sophisticated approach to identifying and mitigating fraudulent activities, offering deeper insights into users' behaviour, lifestyle, and financial strength.
Participants are encouraged to develop solutions which may in real-time, help in detection and prevention of frauds using AI/ML, API, Data Analytics and other cutting edge technologies. These systems should not only accurately identify fraudulent activities but also minimise false positives, using various data sources such as behavioural, biometric and social media data, quickly report and prevent further frauds, thus enhancing recovery prospects. Furthermore, participants are encouraged to utilize novel technologies to gather alternate data sources, enabling innovative use of information available in public domain to predict fraudulent transactions and identify potential red flags overlooked by traditional data sources.