Precision in Practice: Elangoraj Thiruppandiaaj on AI’s Role in the Future of Finance & Insurance
As we count down to the Data & AI Conference Europe 2025 (13–17 October, London), we’re catching up with some of the inspiring minds who will be taking to the stage. One of them is Elangoraj Thiruppandiaaj, Data Scientist at Just Group, whose work sits at the cutting edge of AI in highly regulated industries. In his session, Shaping the Future of Finance and Insurance: AI for Accuracy, Elangoraj will share how he’s embedding AI into business-critical areas — from retirement services to operational risk — to deliver both innovation and compliance. In this exclusive conversation, he opens up about aligning AI with strategic goals, building trust in data-driven decision-making, and fostering a culture of innovation where precision truly matters.
1. AI for Accuracy in Regulated Industries
In finance and insurance, accuracy isn’t optional — it’s the foundation of trust. Yet as products become more complex and regulations more demanding, embedding AI requires balancing innovation with compliance. Data quality is a recurring challenge, with fragmented, multi-format information often impacting reliability. Finally, cultural adoption is just as important as the technology.
2. Aligning AI with Strategy
We start with the business problem, not the technology. Every AI initiative is mapped to a strategic priority, with clear success metrics agreed upfront. This means engaging stakeholders early, validating feasibility, and ensuring compliance is built into the design. By aligning outcomes with organisational goals, we make AI a driver of measurable business value rather than just an experimental tool.
3. Multimodal AI in Action
We work with data in many formats — text, images, and even handwritten notes — often within the same document. By combining generative AI, AI agents, and statistical models, we can extract, summarise, and structure this information. This approach improves accuracy, speeds up decision-making, and significantly reduces manual effort in handling complex information.
4. Balancing Innovation and Trust
Generative AI or large language models can be powerful, but they don’t solve every AI challenge. In many cases, traditional and explainable models — like those built by actuaries — are better suited for accuracy and transparency. Combining these approaches, and being clear about their strengths and limitations, helps maintain both innovation and trust.
5. Human-in-the-Loop Decision Making
In high-stakes areas, automation is never absolute. We define clear thresholds for when a model’s recommendation can be actioned directly versus when it needs expert review. Human oversight is essential for edge cases, complex judgments, or when ethical considerations arise. This balance ensures efficiency without compromising accuracy or accountability.
6. Measuring AI Impact
We measure impact on two fronts — business value and accuracy. Business metrics include efficiency gains, cost savings, and improved decision turnaround times. On the technical side, we track model performance, stability, and error rates over time. Combining these measures ensures AI delivers tangible value while maintaining reliability.
7. Ethics and Governance in AI
We start with strong governance frameworks that define clear policies for data usage, model development, and risk assessment. Bias detection and mitigation are built into the process from the outset. Data protection is treated as non-negotiable, with strict controls to prevent leakage and ensure regulatory compliance. Regular reviews and transparent reporting keep both ethics and accountability front and centre.
8. Looking Ahead
One of the most exciting trends is AI’s ability to process vast amounts of data and provide meaningful insights with minimal context. This capability opens the door to creating rich “what-if” scenarios, enabling leaders to explore different outcomes before making key business decisions. In finance and insurance, this means faster, more informed choices that can balance risk, opportunity, and compliance.
Elangoraj’s insights highlight that in regulated industries, AI success isn’t just about algorithms — it’s about strategy, trust, and impact. From multimodal AI solutions to robust governance and ethical safeguards, his approach offers a practical blueprint for organisations ready to innovate responsibly. Don’t miss his session on Wednesday, 15 October 2025 at the Data & AI Conference Europe 2025 — and see how accuracy-driven AI can transform finance and insurance while keeping compliance and ethics at the forefront.
