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Five Banking Trends Driving Artificial Intelligence Forward

With the rapid evolution of artificial intelligence (AI) in the financial services in recent years, banks continuously strive to get ahead in hyper-personalizing customer experience to empower customers to “live more” and “bank less”, as one of Southeast Asia’s top banks very aptly put it.

There is no doubt AI has brought benefits to consumers, businesses, and the wider economy. However, AI has also amplified risks and created new challenges and will continue to do so as AI models get more sophisticated.

With this background, we have identified 5 key trends that banks will be focusing their efforts and investments in the area of AI.


Governance of AI has been found to be more effective if it embraces diversity of skills and perspectives and covers the full spectrum of functions. This cross-functional mitigates the AI complexity and related data challenges. 

Managing Data

Owing to the nature of the XAI algorithm and the fact that outcomes and drivers of XAI are fully explainable, the uncovering and understanding of latent data quality issues can happen during XAI model developments, which supplements up-stream data management efforts creating a virtuous circle.

Managing Model Risk

AI models can create risks that arise from complex inputs, inter-variables relationships, the algorithm itself, and even the ensuing outputs. Hence, explaining AI model output is critical and more so in the financial industry as regulators keep a keen watch to proactively prevent the pervasive use of AI from causing a systemic risk to the banking system.

Barriers to Adoption

While AI can be deployed in many business scenarios, and that has been the case, the barriers to AI adoptions, especially for smaller banks, can still be significant. High on the list are the barriers involving data, documentation, explainability and governance. AI can magnify existing risks and introduce new challenges resulting in more controls and structures.

Optimized Explainability

“Explanation” depends on context, a good explanation for a customer is very different from one to data scientist or bank executive. This explainability gap require consistency to facilitate understanding among the different stakeholders and there is an already increasing calls for this level of explainability.

About the author

CK Loy

CK Loy is a Principal Solutions Consultant at Temenos. Based in Singapore, he is also Temenos’ Global Domain Lead for Analytics and XAI. Earlier in his career, he was a banker where he led analytics teams in areas of portfolio management, analytics and modeling. He then joined the fintech industry in 2014 to continue his efforts on empowering banks to leverage data, analytics and AI to enhance financial inclusions and business successes.

View the Webcast

Our expert speakers, Hani Hagras and CK Loy, discussed the latest trends and technological advancements in AI, with a particular focus on how it can help financial institutions mitigate risk, increase efficiency, and modernize their Sanctions Screening processes to fight financial crime.