Fellow Actuary. IIT Kanpur. 18+ years turning complex data into commercial decisions across Banking, Insurance, and GenAI — from $50B balance sheets to production ML systems.
I'm Yashvant Jain — a Fellow Actuary from IIT Kanpur with 18+ years of experience building analytics systems that drive real commercial outcomes. My work sits at the intersection of rigorous statistical thinking, machine learning, and strategic business impact.
Over my career I've scaled enterprise analytics functions from the ground up, led credit risk and regulatory modelling for a $50B+ CCAR-regulated bank, and embedded GenAI solutions into production environments — including analytics assistants, NLP complaint engines, and automated decision pipelines.
I've worked across Accenture, AIG, TCS, AXA, CRISIL, Ernst & Young, and HSBC — spanning Banking, Insurance, Energy, and Retail. Whether it's a churn prediction model, GLM-based insurance pricing rater, or an agentic analytics workflow, I bring both the technical depth and the leadership experience to take it from idea to production.
Based in Bengaluru, India. Available for consulting engagements globally.
From model development to full analytics transformation — I work with banks, insurers, and fintechs to build decision intelligence that scales.
Mortgage PD, Credit Card EAD and LGD models under Basel frameworks. Behavioral scorecards, utilization forecasting, underwritten risk profile dashboards, and CCAR-compliant balance sheet planning for retail and wholesale portfolios.
GLM-based pricing models, telematics driver scoring, frequency-severity modelling, delinquency tracking, claims analytics, and A/B testing for Travel and SME insurance conversion and profitability uplift.
Engagement-based segmentation, churn prediction, CLV, next-best-product and credit limit assignment, campaign ROI, and market size & volume share models to predict customer preference and drive acquisition decisions.
Production GenAI solutions — AI4BI analytics assistants (saved 1.5 FTE on sales reporting), automated performance reviews, portfolio exception flagging, and agentic BI pipelines for decision intelligence workflows.
End-to-end NLP complaint classification engines — automatically route to KX knowledge bases or assign to the appropriate L1, L2, and L3 support teams. Text mining for triaging, capacity planning, and operational efficiency.
Enterprise portfolio monitoring covering delinquency tracking, roll rates, limit and attachment monitoring, annual profit studies, and real-time KPI dashboards for underwritten risk profiles across banking and insurance books.
Forecasting-driven capacity planning for helpdesk and contact centre operations — POS spike detection, demand forecasting, workload modelling, and L1/L2/L3 team sizing to reduce cost-to-serve while maintaining SLA.
Tariff competitiveness, FX channel usage and deflection strategies, partner offer optimization, and channel-level pricing models for loyalty and energy retail to improve margins and acquisition funnel conversion.
Scale analytics functions from POC to production and from 10 to 60+ people — operating model design, cloud data pipeline modernisation on Snowflake and GCP, and capability building across Data Science, BI, and Engineering.
A selection of anonymised engagements across Banking, Insurance, Energy, and Retail — all delivered end-to-end.
Built wholesale loan forecasting models (prepayment, utilization, balance growth, origination spread) for CCAR-compliant balance sheet planning. Delivered dashboards for underwritten risk profiles, enabling commercial leadership to monitor portfolio health in real time.
Developed PD models for mortgage portfolios and EAD/LGD models for credit card books under Basel frameworks. Built behavioral scorecards for lifecycle risk management and credit limit assignment, including next-best-product targeting to support cross-sell strategy.
Built telematics-based driver risk scoring and GLM Auto Insurance pricing models integrated into live pricing raters and frontline underwriting systems. A/B testing and UI/UX experimentation delivered measurable SME and Travel Insurance profitability improvement.
Built revenue and channel optimization analytics including engagement-based customer segmentation, consumption personas, market size and volume share preference models, FX channel deflection strategy, and next-best-action engine across a loyalty ecosystem with $800M in annualised sales.
Designed and deployed an in-house GenAI-powered analytics assistant handling ad-hoc sales figure queries and automating recurring performance reviews. Freed 1.5 FTE from manual reporting work and enabled analyst teams to focus on higher-value exception analysis.
Built an NLP-based complaint classification engine that automatically routes incoming complaints to the KX knowledge base or assigns them to the appropriate L1, L2, or L3 support teams. Reduced manual triaging effort significantly and improved capacity planning accuracy for helpdesk operations.
Led a forecasting-driven capacity planning project for helpdesk operations, including detection of POS transaction spike events that caused demand surges. Built demand forecasting models to right-size L1/L2/L3 team capacity and reduce cost-to-serve while maintaining SLA performance.
Have a question about your analytics strategy, credit risk modelling approach, or whether GenAI is right for your use case? Ask the AI version of me — trained on my 18 years of expertise.
Useful for: scoping a project, understanding methodologies, comparing approaches, or deciding if we're a good fit before reaching out.
Suggested questions
Yash — Analytics Advisor
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Whether you need a specific model built, an analytics function transformed, or a GenAI strategy validated — let's talk. I typically respond within 24 hours.