Data & AI Mastery

👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this special compilation episode of Inside the Algorithm, host Jeremy, Chief AI Scientist at Cambridge Spark, brings together the best moments from a season of conversations with leading academics, researchers and practitioners. Guests from aviation forecasting, manufacturing, NHS health modelling and neuro-symbolic AI research take on the same core questions. Hearing them side by side shows how experts from very different fields approach the challenge of taking AI from theory to practice. The episode covers why so many AI projects stall at the prototype stage and where models break when they meet something they've never seen. It also covers how trust is built in systems that can feel like a black box, why the strongest results come from hybrid systems, and what experience still gives you as AI tools take on more of the work. Key Takeaways Production readiness means handling edge cases, unforeseen scenarios and adversarial attacks. It also means securing architecture sign-off and making sure outputs reach the people who need them. Projects succeed when teams understand how a solution will be used and what "done" looks like, and when they have champions inside the client's business. LLMs learn correlations, not the world. Their reliance on text patterns explains both their surprising capability and their strangest hallucinations. Historical data has limits. Events like the pandemic can break the relationships forecasting models depend on almost overnight. Stress testing, what-if scenario planning and human domain expertise are essential safeguards. Trust is built through involvement. Participative modelling earned deep trust with NHS clients in the Midlands, and transferring that trust to new regions proved harder than rebuilding the model itself. Hybrid systems deliver the strongest results. Pairing language models with solvers, simulations and small, fine-tuned specialist models lets each part do the job it's best at. Simplicity and production-first thinking win. A working model in production beats a more accurate one that takes months to build. Explainable approaches often serve clients better. Juniors need the chance to learn the trade. Guests warn about "rubber-stamping" AI-generated code and encourage newcomers to look beyond the hype to the wider history of AI. Useful Links & Resources Inside the Algorithm on the Data and AI Mastery podcast feed Inside the Algorithm episodes on the Cambridge Spark YouTube channel Cambridge Spark: cambridgespark.com Connect With the Show Cambridge Spark LinkedIn: https://www.linkedin.com/school/cambridge-spark/ Cambridge Spark Instagram: https://www.instagram.com/cambridgespark/ Cambridge Spark X: https://x.com/CambridgeSpark Host Jeremy Bradley on LinkedIn: https://www.linkedin.com/in/jeremy-bradley/ Which of these clips landed hardest for you: the prototype trap, the pandemic story, or the case for pairing LLMs with solvers? Tell us in the comments, and let us know which guest you'd like to hear a full episode with again. Visit cambridgespark.com to learn how Cambridge Spark can upskill your workforce in data and AI. #InsideTheAlgorithm #EnterpriseAI #DataEngineering #MachineLearning #AIDeployment

👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In financial services, getting AI wrong doesn't just mean a bad headline. It means real customers losing real money at the moments that matter most. If you're trying to balance AI innovation with regulatory scrutiny, you already know the tension: move too fast and risk a compliance failure, move too slow and watch competitors who don't hesitate pull ahead. Edmund Towers leads Advanced Analytics & Data Science at the Financial Conduct Authority, shaping the regulator's approach to AI, financial crime detection and consumer protection. Before joining the FCA, he delivered financial services transformation programmes as a manager in Accenture's Financial Services Change Practice. In this compilation episode, he's joined by Jessica Rusu, the FCA's Chief Data, Information and Intelligence Officer, alongside senior data leaders from Santander UK, Aviva and TalkTalk. You'll hear how the FCA thinks about AI governance without resorting to tick-box regulation, and how banks and insurers turn that clarity into production systems. Sarah Self at Aviva explains how a summarisation tool cut claims hold times by more than 50%. Kevin Cassar walks through building an AI triage system in health insurance that improved both customer outcomes and operational efficiency. This episode covers the FCA's AI Live Testing programme, the Consumer Duty as the policy anchor for responsible AI, and how Santander UK's Luke Pearce secured C-suite sponsorship to move faster on generative AI. It's built for data and AI leaders in regulated industries who need to make the business case for AI while keeping compliance onside, and for anyone who assumes regulation and innovation have to be at odds. Key Takeaways Edmund Towers explains why the FCA's AI Live Testing lets firms trial live products under supervision, and why it's built on guidance rather than a tick-box checklist. The Consumer Duty already covers responsible AI, according to Towers, and blaming a "black box" model won't protect a firm from accountability under the Senior Managers Regime. Sarah Self's team at Aviva cut claims-handler hold times by more than 50% with a summarisation tool, and built a medical underwriting system running at 99% accuracy. Kevin Cassar reveals the co-sponsorship model, COO paired with CDO, that got a health insurance triage system built with risk and business teams involved from day one. Chapter Markers 00:00 Why regulated industries need a different AI conversation 02:07 The FCA's role in AI adoption across financial services 04:10 Inside the FCA's AI Lab and Live Testing programme 07:49 Why Consumer Duty rules already cover responsible AI 10:37 Santander's shift from risk-averse to AI-enabled 13:05 Aviva's claim summarisation tool cuts hold times in half 16:07 Building an AI triage system in health insurance 18:39 Getting COO and CDO co-sponsorship for AI projects 20:03 The FCA's fight against fraud, scams and money laundering 23:41 Closing thoughts: compliance and innovation as one agenda Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Visit the Cambridge Spark Website: https://cambridgespark.com/

👉 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

👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this episode of Data & AI Mastery, host Dr Raoul-Gabriel Urma is joined by Simona Paravani-Mellinghoff, Chief Investment Officer at BlackRock, to explore why data quality and accessibility sit at the heart of every serious AI strategy. Simona explains what good data infrastructure looks like when overseeing hundreds of billions in mandates and why relevant data, not just volume, drives better investment insights. She shares her Ferrari and Fiat Punto analogy for deciding when to use large generative AI models versus smaller, specialised ones and outlines a three-layer skill pyramid, prompting, critical thinking and creative thinking, that leaders and their teams will need to thrive in an AI-driven world. The conversation also covers AI's role in expanding financial inclusion, why Simona funds scholarships and teaches at Cambridge, and her advice for anyone starting a career in data or finance today. If you enjoyed this episode, follow Data & AI Mastery and share it with a colleague thinking through their own data and AI strategy. Chapter Markers (00:00) - Introduction: no data, no AI party (01:16) - Simona's journey from Italy to global CIO (03:40) - What academia and industry learn from each other (05:21) - Building good data infrastructure at scale (08:56) - Frontier models vs specialised models: the Ferrari and Fiat Punto analogy (11:42) - Why education and scholarships matter to Simona (15:03) - The three-layer skill pyramid for the AI era (17:19) - Will AI threaten jobs or create new ones (21:04) - AI's role in advancing financial inclusion (23:22) - Advice for the next generation (24:06) - Quickfire round (27:22) - Closing thoughts and key takeaways Useful Links Connect with Simona Paravani-Mellinghoff on LinkedIn: https://uk.linkedin.com/in/simona-paravani-cfa-5129524 Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Visit the Cambridge Spark Website: https://cambridgespark.com/

👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com What does it actually take to ship machine learning inside one of the UK's largest insurers? Jeremy Bradley sits down with Alberto Romero, director of AI engineering at Aviva, to trace his path from InsurTech founder to enterprise AI leader. Alberto explains why prototypes are so often mistaken for finished products and what production readiness really demands once edge cases, drift and adversarial behaviour enter the picture. The conversation covers how to get genuine explainability out of large language models rather than plausible-sounding justification, when fine-tuning earns its place in a regulated stack, and why Aviva built its own internal platform to govern AI use cases at scale. Alberto also shares his take on fraud detection as an adversarial ML problem and the one failure mode he sees engineering teams repeat most often. Follow Data & AI Mastery so you never miss an episode, and share it with a colleague working through similar production challenges. If you enjoyed this conversation, you might also like this episode featuring Sarah Self. She joined us on Data and AI Mastery to explore what most organisations get wrong when deploying AI. Apple: https://podcasts.apple.com/gb/podcast/from-cybersecurity-to-ai-director-sarah-self-on-leading/id1779783413?i=1000764247007 Spotify: https://open.spotify.com/episode/0BpSq5X1ZP8ctIYTxWVAJT?si=1264586e79f3446f YouTube: https://www.youtube.com/watch?v=2jgM095SYG0 Glossary Terms RAG: Retrieval-Augmented Generation is an AI methodology that enhances Large Language Models by pulling factual context from external knowledge bases. GAN: Generative Adversarial Network is a deep learning architecture in which two neural networks compete against each other to create highly realistic synthetic data from a training dataset Non-deterministic: describes a process, algorithm, or system whose outcome is inherently unpredictable and cannot be guaranteed to repeat exactly, even when it starts from the exact same initial conditions ReAct (Reasoning + Acting) approach: a prompting technique that enables AI models to solve complex problems by alternating between thinking and taking action Chapter Markers (00:00) - Cold open: why prototypes get mistaken for production (02:53) - Avoiding common AI adoption pitfalls in regulated sectors (05:44) - Real explainability versus post-hoc justification in LLMs (09:18) - From startup founder to enterprise: the mindset shift (11:38) - Managing AI across 70+ use cases at Aviva (13:31) - Standards first, technology second (17:19) - Where fine-tuning earns its place (20:33) - Building Aviva's own governed AI platform (23:51) - Fraud detection as an adversarial ML problem (28:34) - Quick fire: the most common AI failure mode (29:37) - What deserves more attention as AI scales Useful Links Connect with Alberto Romero on LinkedIn: https://uk.linkedin.com/in/albertoromero-uk 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