During the Temenos Community Forum in Vienna earlier this year, Finextra had the opportunity to interview Adam Gable, product director of financial crime, treasury, and risk at Temenos, along with Hani Hagras, chief science officer at Temenos. They discussed the role of instant payments as a catalyst for enhanced speed and innovation in mitigating financial crime.
Gable emphasized that instant payments inherently require real-time processing, meaning that security checks must be both rapid and precise. Inefficient processes can disrupt customer experiences, erode goodwill, and negatively affect brand reputation. Issues such as checks being held for manual review or inaccurate assessments can result in customer fraud, underscoring the necessity for banks to maintain robust security protocols.
“As checks are conducted, a certain percentage will inevitably require further investigation, which takes time and can hinder customer satisfaction,” Gable explained. “Furthermore, banks need to proactively consider the systems and processes they have in place as they transition to instant payments.”
Hagras highlighted the potential of AI in enhancing crime mitigation for instant transactions, suggesting that automation could streamline the user experience. This would be supported by a comprehensive audit trail, enabling banks to scale effectively. AI has the capability to combat financial crime, facilitating sanctions screening and anti-money laundering processes by automatically identifying fraudulent activities, while efficiently handling false positives to avoid disrupting customers.
“Fraud is an ongoing struggle,” Hagras noted.
He further elaborated that AI must be adept at generating models that adhere to diverse regulatory requirements, instilling trust in banks and financial institutions. A pivotal aspect of this is the use of explainable AI, which ensures that the generated models can be readily understood, analyzed, and effectively audited by business users and relevant authorities.
Many regulators reject opaque box models due to their lack of transparency concerning the decision-making processes behind outputs. Hagras concluded that the focus on explainable AI is critical for Temenos, highlighting the importance of complying with both current and future regulations pertaining to AI ‘explainability’.