TLDR: A Finextra Research study, in collaboration with Appian, highlights the critical need for banking-specific AI solutions, questioning the efficacy of generic generative AI in addressing the sector’s unique challenges. The report underscores declining bank productivity in the US despite high tech spending, emphasizing that embedding AI within governed, automated processes is key to unlocking its true enterprise value and driving future technological transformation.
Finextra Research, in association with Appian, has released a new impact study titled ‘Is Generative Too Generic? Why You Need to Consider Banking Specific AI.’ The study delves into the pressing question of whether generalized generative AI models are sufficient to meet the complex and highly regulated demands of the banking industry, or if a more tailored, banking-specific approach is essential.
The research points to a significant challenge within the United States banking sector, where productivity is reportedly declining despite substantial investments in technology. This trend underscores an urgent requirement for more effective implementation of artificial intelligence. The study posits that the fundamental key to unlocking the full enterprise value of AI lies in its seamless integration within orchestrated and automated processes. This strategic embedding is crucial for ensuring AI solutions possess the necessary governance, auditability, and the flexibility to adapt in real-time to evolving market and regulatory landscapes.
Indeed, the report suggests that financial institutions that successfully embed AI within such governed and orchestrated processes are poised to lead the next wave of technological transformation. Conversely, those that fail to do so risk falling further behind. The findings emerge at a time when AI is a paramount concern across many banking circles, with a leading intelligence platform’s annual banking industry AI Index Roundtable indicating a widening performance gap between institutions prioritizing AI in their internal and external operations and those that have not.
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While the potential of AI is widely recognized, many banks are reportedly hindered by legacy systems. This creates a significant impediment in an environment where speed, resilience, and reliability are not merely advantageous but critical for survival and growth. The Finextra study, therefore, serves as a crucial analysis of the existing gap between AI’s immense potential and its current, often underwhelming, impact within the banking sector.
