~Ashish Lahoti, Chief Transformation Officer, ServiceNow
AI’s impact to the Banking and Financial Services Industry (BFSI) will be profound. The industry’s entire business and operating model will undergo transformative changes. Everything is up for reimagination— from the level of personalized financial advice, to how fraud and credit risk are assessed, to how customer service is automated. The impetus may come from inside, but external forces of fierce peers and nimble niche newbies will present novel challenges. Regulatory frameworks will evolve, attempting to keep pace with changing surface areas of risk domains.How should CIOs respond? Is there a Goldilocks spot on the continuum of “act fast” to “tread cautiously” that is the answer, or is it something more fundamental? In fact, Darwin’s “survival of the fittest” quote will ring true like never before as companies that are the most agile in driving AI transformation take the stage as the new BFSI leaders.
“If something is moving a million times faster every 10 years, what should you do?” Jensen Huang, NVIDIA CEO, asked, citing rapid advancements in AI capabilities. “The first thing you should do is instead of looking at the train, from the side is … get on the train, because on the train, it’s not moving that fast.”
Here’s how BFSI executives can move forward:
Embrace Rapid AI Advancements and Address AI Challenges
The “AI train” is accelerating rapidly, with advancements addressing issues like hallucination and recency bias while enhancing capabilities through stepwise problem-solving and specialized AI agents. To stay competitive, BFSI executives must not only adopt these advancements but also actively address the associated challenges. Leveraging the evolving richness of GenAI applications will be crucial for maintaining a leading edge in the industry.
Avoid Cognitive Bias
Avoiding cognitive biases such as the “availability heuristic” is essential. This bias often leads to focusing solely on high-stakes scenarios while overlooking less critical applications where AI can deliver substantial value with lower risk. By considering a broader range of AI applications, organizations can uncover opportunities for significant improvements and efficiencies.
Align AI Use Cases with Strategic Priorities
Identify and implement AI use cases that directly support your organization’s strategic goals. Consider the board’s priorities—such as increasing customer lifetime value (CLTV), rural expansion, or enhancing risk management. Choose AI applications that drive these outcomes. For example, integrating GenAI with predictive AI can advance robo-investing platforms with enhanced insights and interactive dashboards. Similarly, AI-driven digital workflows can streamline customer service, reducing costs and improving experiences. Ensure these applications align with both current and future AI advancements for maximum impact.
Use a Platform Orientation to Accelerate Deployment and Avoid Customization Pitfalls
Adopting a platform-based approach offers significant benefits over bespoke solutions. A unified AI platform provides built-in data privacy features, streamlined data curation, guardrails, and domain specific large language models (LLMs), eliminating the need for scarce skills in data engineering, model training, and AI operations. This approach avoids the brittleness and complexity of fragmented systems, which require extensive integration, maintenance, and risk management. By leveraging an enterprise ready platform, organizations accelerate time to market for AI solutions, as the platform’s pre-trained models and integrated workflows are already optimized for immediate use. This not only reduces technology debt and avoids costly, backlogged infrastructure, but also ensures scalable, accurate AI applications, leading to more efficient and reliable deployment.
Take an Iterative Learning Approach
An iterative approach is essential. Begin with pilots to test and refine AI applications, using initial results to guide further. For example, in complaints management, start with AI solutions focused on sampling based controls, such as reviewing a small percentage of complaints. Gradually enhance the system by integrating GenAI and predictive analytics to expand coverage to 100% of complaints. This process involves initial testing, refining and expanding the scope based on feedback, and integrating the solution across the entire lifecycle of complaints—from detection and escalation to resolution and compliance reporting. Continuously gather data to fine-tune the AI system, ensuring it adapts and scales effectively, drives ongoing process improvements, and strengthens risk management.
By integrating these strategies, CIOs can effectively navigate the rapidly evolving AI landscape and leverage its capabilities to drive strategic objectives and operational improvements within their organizations.
(Disclaimer: The above content is non-editorial, and TIL hereby disclaims any any and all warranties,
expressed or implied, relating to it. This is a Brand Connect Initiative)
