DAIM: Inside The Algorithm | Divya Kesavan on AI Transformation in Banking: Beyond RPA and Automation

👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com For years, digital transformation and AI transformation have been used almost interchangeably. Divya Kesavan, Head of Data Science for Business and Commercial Banking at Lloyds Banking Group, explains why that conflation breaks down in practice. In this episode, Divya walks Jeremy through the shift from rule-based RPA to reasoning-driven AI and why Lloyds' enterprise AI blueprint rests on three layers: reasoning, context and control. She unpacks what it actually takes to move an agentic system from a working proof of concept to a tool trusted with real banking decisions, including a striking example of an AI agent that went badly off script. The conversation also covers governance by design in a heavily regulated environment, confidence scoring as a practical risk tool, and why agile alone cannot carry the weight of AI-led change. A grounded, experience-based look at what transformation really requires. Follow Data & AI Mastery for more conversations with the people building AI inside major enterprises. If you enjoyed this conversation, check out this special episode of Data & AI Mastery where Raoul sits down with senior leaders from Lloyds to learn more about how the team is reimagining banking through AI-powered experiences:  Apple: https://podcasts.apple.com/gb/podcast/reimagining-lloyds-banking-group-ai-transformation/id1779783413?i=1000701688423 Spotify: https://open.spotify.com/episode/06ZBLoyvemQEVftCuWytVe?si=726cf7a2d20b4b22 YouTube: https://www.youtube.com/watch?v=U2krteWX89Q Glossary Terms RPA (Robotic Process Automation): software that mimics human actions on a screen to automate repetitive, rules-based tasks.  RAG (Retrieval-Augmented Generation): a method that grounds an AI model's responses in real, relevant data retrieved at the time of the request, rather than relying solely on what the model learned during training. Human-in-the-loop: a safeguard where a person reviews or approves an AI system's output before it's acted on, typically when confidence is low or the decision carries risk. Confidence scoring: a technique used to measure how certain an AI system is in its output. Outputs above a set threshold proceed automatically, while lower-confidence results are flagged for human review. Chapter Markers: (00:00) - Cold open: the case for AI governance (00:39) - Introduction: Divya Kesavan, Lloyds Banking Group (02:23) - Where automation stops and AI begins (03:39) - Why traditional RPA breaks under ambiguity (05:04) - From RPA blueprint to AI-first processes (06:47) - Building agentic reasoning: the real technical challenge (09:29) - The rogue agent story and why permissions matter (12:03) - Lloyds' enterprise AI blueprint: reasoning, context, control (14:45) - Reliability as the hardest problem to solve (16:34) - Designing and testing AI in a regulated environment (19:25) - Embedding responsible AI thinking across the team (22:54) - Confidence scoring and managing risk in practice (24:34) - Common misconceptions about AI versus automation (26:18) - Balancing probabilistic AI with deterministic workflows (27:39) - Advice for data science leaders starting out (30:04) - The one idea to take away Useful Links: Connect with Divya Kesavan on LinkedIn: https://uk.linkedin.com/in/divya-kesavan-54800b3 For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com