P2P

37 I L T A N E T . O R G payments industry lies in its capacity to detect and prevent service violations, particularly in combating fraud and money laundering. As officials from regulatory bodies such as the Financial Conduct Authority and the U.S. Securities and Exchange Commission have publicly acknowledged, Gen AI technologies can drive operational efficiency, particularly in customer due diligence, screening, and transaction monitoring controls. Industry leaders like Stripe and Mastercard have already begun leveraging Gen AI to enhance their operations. Stripe utilizes ML algorithms to bolster fraud prevention measures while optimizing product offerings across its business spectrum. Similarly, Mastercard harnesses Gen AI models to ensure the security of over 125 billion transactions annually, extending its applications to customer experience enhancement, treasury management, and product testing. Gen AI's Impact on Customer Due Diligence and Identity Verification Gen AI is increasingly applied to customer due diligence and screening controls, leveraging natural language processing and text mining techniques to enhance risk assessment processes. Integrating Gen AI with human activity drives innovation in AML practices, potentially leading to a more comprehensive approach to know-your- customer (KYC) procedures. The integration of Gen AI has significant implications for KYC processes, especially in the non-traditional space of fintech. AI-driven KYC solutions can enhance regulatory compliance, improve risk management practices, and elevate the overall customer experience by harnessing advanced biometrics and, potentially, social profiles. A popular approach to KYC verification incorporates challenge questions during user login to strengthen the verification process. This innovative method uses user data to monitor and compare login activities with new login attempts. Parameters such as failed login attempts, new user registrations, clients with limited identity details, or alterations in transaction patterns are analyzed within the challenge question technique, implemented in many login types. Risk factors are then calculated based on these parameters, enabling the system to flag and log out users exhibiting suspicious behavior. A step up from the traditional challenge questions, Hewlett Packard Enterprises (HPE) unveiled, in March 2024, the enhancement of its AIOps network management features by integrating multiple Gen into a cloud-native network management solution, diverging from conventional Gen AI methodologies that rely on API calls to public LLMs. This network incorporates a self- contained set of LLMs with pre-processing techniques and safeguards to enhance user experience and operational efficiency, prioritizing search response times, accuracy, "Industry leaders like Stripe and Mastercard have already begun leveraging Gen AI to enhance their operations."

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