MBA (GM & FS) · Trimester II · 3 Credits
Insurtech & Digital Risk
Solutions
Master the intersection of insurance, data analytics, and artificial intelligence. From risk pooling fundamentals to AI-driven underwriting, fraud detection, and parametric insurance — become industry-ready for the digital insurance era.
About This Course
The insurance industry globally and in India is undergoing a fundamental transformation driven by technology, data analytics, and artificial intelligence. This course equips MBA students with a dual lens — deep understanding of insurance and risk management principles combined with hands-on proficiency in analytics tools and technologies reshaping the industry. Every concept session is reinforced by a practical lab using Python, Excel, Power BI, ChatGPT, KNIME, and Looker Studio.
Module 1: Insurance Fundamentals & Digital Transformation
Sessions 1–4 · 6 hrs · CILO-1Introduction to Insurance Industry
Insurance ecosystem, risk transfer, pooling, law of large numbers, life/health/general insurance, IRDAI mandate, and the insurance value chain.
Risk Management Fundamentals
Pure vs. speculative risk, risk management process (5 steps), risk identification techniques, likelihood-impact matrix, 5 Ts of mitigation, and building a risk register in Excel.
Insurance Business Model
Underwriting profit vs. investment income, premium calculation, combined ratio, claims lifecycle, key metrics (loss ratio, IBNR, solvency), and building an insurance P&L in Excel.
Digital Transformation in Insurance
Traditional vs. digital models, API/cloud/AI/IoT drivers, Indian InsurTech ecosystem map, digital customer journey, Bima Sugam & India Stack, using ChatGPT for insurance research.
Module 2: Insurance Data & Analytics
Sessions 5–7 · 5 hrs · CILO-2Insurance Data Sources & Cleaning
Insurance data landscape, the 6-table integrated dataset, handling missing values, type conversion, deduplication, merging tables in Pandas, and data quality reporting.
Data Visualization for Insurance
Insurance KPIs for visualization, Matplotlib foundations, Seaborn for statistical plots, claims trend analysis, portfolio composition charts, correlation heatmaps, and publication-ready exports.
Power BI for Insurance Analytics
Power BI vs. Python, DAX measures for insurance KPIs, building 3-page dashboard: Claims Overview, Policy Analytics, Customer Segmentation. Dashboard design principles.
Module 3: InsurTech Innovations
Sessions 8–10 · 4 hrs · CILO-1, CILO-2Introduction to InsurTech
Three waves of InsurTech, global landscape (Lemonade, Wefox, ZhongAn), Indian startup ecosystem, business model types, unit economics, and IRDAI regulatory sandbox.
Digital Customer Acquisition
Digital acquisition funnel, CAC by channel, LTV calculation, LTV/CAC ratio, funnel analytics in Python, channel profitability ranking, and retention-driven acquisition strategy.
Embedded & Usage-Based Insurance
Embedded insurance economics, Indian examples (Ola, Flipkart, MakeMyTrip), PAYD vs. PHYD, telematics data analysis in Python, UBI leaders (Root, Tesla), and privacy considerations.
Module 4: Artificial Intelligence in Insurance
Sessions 11–14 · 6 hrs · CILO-2, CILO-3AI & Machine Learning Fundamentals
ML in insurance overview, supervised vs. unsupervised, the Scikit-learn workflow, regression (claim amount), classification (claim probability), clustering (policyholder segments), overfitting.
AI for Underwriting
Feature engineering for risk, Random Forest risk scoring models (0–1000), risk tier classification, premium recommendation engine, SHAP/LIME explainability, and STP underwriting.
AI for Claims Processing
Claims triage classification, severity prediction, fast-track vs. investigate decisions, XGBoost for claims models, straight-through processing, and human-in-the-loop architecture.
Generative AI in Insurance
LLM landscape, prompt engineering patterns, insurance chatbot design, policy document Q&A and summarization, claims document processing, and GenAI regulatory guardrails.
Module 5: Fraud Analytics & Digital Risk
Sessions 15–17 · 5 hrs · CILO-2, CILO-3Insurance Fraud Analytics
Fraud typology (application, claims, provider), 25+ red flags, fraud analytics framework, building fraud indicator dashboards in Power BI, and economics of fraud management.
Fraud Detection Using ML
Class imbalance problem, SMOTE, Isolation Forest for anomaly detection, XGBoost with class weights, precision-recall evaluation, and KNIME workflow for fraud analytics.
Cyber Risk & Cyber Insurance
Threat landscape (ransomware, breaches, BEC), first-party vs. third-party coverage, NIST-based risk assessment, cyber risk matrix in Excel, the war exclusion debate.
Module 6: Climate Risk & Parametric Insurance
Sessions 18–20 · 4 hrs · CILO-1, CILO-2, CILO-3Climate Risk in Insurance
Physical vs. transition risks, India's climate vulnerability, portfolio exposure analysis in Python, climate-claims correlation, Looker Studio risk dashboard, and the protection gap.
Parametric Insurance Design
Parametric vs. indemnity, product structure (index/trigger/exit/payout), global examples (CCRIF, ARC), Python modeling, basis risk analysis, and cyclone trigger design.
Catastrophe Modeling & Risk Assessment
CAT model modules (hazard/vulnerability/financial), Monte Carlo simulation in Python, AAL/PML/TVaR metrics, EP curves, reinsurance decision-making, and model limitations.
Module 7: Advanced Analytics & Business Intelligence
Sessions 21–23 · 5 hrs · CILO-2, CILO-3Advanced Python for Insurance Analytics
Time series decomposition, ARIMA forecasting for claims, stationarity testing, premium prediction with multiple regression, Ridge/Lasso regularization, and forecast evaluation.
Customer Retention & Churn Analytics
Economics of churn, churn feature engineering, XGBoost churn classifier, retention strategy design, ROI of retention, uplift modeling, and KNIME churn automation workflow.
Looker Studio for Executive Reporting
Executive vs. operational dashboards, C-suite KPIs, 2-page executive report (Performance Overview + Portfolio & Market), data storytelling, and management review cadence.
Module 8: Governance, Compliance & Capstone
Sessions 24–26 · 4 hrs · CILO-1, CILO-3Insurance Regulations & Digital Governance
IRDAI framework, solvency & capital adequacy, DPDP Act 2023 impact on insurance data, regulatory reporting, compliance dashboard in Power BI, and regulatory sandbox.
Ethical AI & Responsible Insurance
Ethical AI principles, algorithmic bias types, proxy discrimination, bias detection & mitigation, AI governance framework design using ChatGPT, and EU AI Act implications.
Capstone Project Presentations
8 project options, evaluation rubric, report & presentation guidelines, 10+5 min defense format, peer feedback, course wrap-up, and insurance analytics career pathways.
Assessment Structure
Tools & Setup
You will gain hands-on proficiency with six industry-standard analytics tools throughout this course.
Learning Outcomes
CILO-1: Domain Knowledge
Analyze insurance fundamentals, risk management principles, InsurTech innovations, and digital transformation trends in the Indian insurance context.
CILO-2: Analytics Tools
Apply Excel, Power BI, Looker Studio, Python, KNIME, and AI technologies to insurance data for underwriting, claims, customer analytics, fraud detection, and risk assessment.
CILO-3: Solution Design
Design and evaluate data-driven insurance solutions including risk scoring, parametric products, fraud detection, compliance dashboards, and ethical AI frameworks.
Integrated Dataset
A single integrated synthetic insurance dataset (~45,000 records across 6 tables) is used consistently throughout the course across all tools.