*We customize the course outline and content to your specific needs and relevant use cases.
Module 1: Data analytics in the insurance business
- Role of analytics in insurance decision making and operational steering
- Common data sources policy, claims, customer, distribution, and finance
- Turning business questions into metrics, dimensions, and dashboard logic
- Practical analytics use cases for claims, lapse, retention, and service monitoring
Module 2: Power BI foundations and data preparation
- Power BI workspace, report structure, and data flow overview
- Importing data from spreadsheets, files, and business systems
- Cleaning and transforming data with Power Query
- Structuring tables so downstream analysis stays consistent and readable
Module 3: Data modeling for insurance reporting
- Fact and dimension thinking for insurance datasets
- Building relationships between claims, customer, policy, and product data
- Handling dates, hierarchies, and segment fields for useful filtering
- Avoiding common modeling mistakes that distort business results
Module 4: Core insurance measures and KPIs
- Building measures for claim counts, claim severity, and settlement trends
- Defining lapse, retention, and renewal related indicators clearly
- Calculating loss ratios and related profitability views
- Creating measure logic that is understandable for business users and management
Module 5: Claims analytics with Power BI
- Claims intake, processing, reserve, closure, and payment analysis
- Segmenting claims by line, cause, region, channel, and handler
- Tracking development patterns, aging, and operational bottlenecks
- Presenting claim trends and case mix in a business friendly way
Module 6: Lapse and retention analysis
- Identifying churn and retention patterns across products and customer groups
- Comparing customer cohorts, renewal periods, and policy durations
- Visualizing drop off points and renewal performance
- Supporting action planning for retention improvement
Module 7: Loss ratios and portfolio performance
- Structuring dashboards for premium, claims cost, and ratio analysis
- Comparing performance across products, regions, intermediaries, and time periods
- Distinguishing trend, volatility, and outlier behavior
- Building views that support management conversations on portfolio quality
Module 8: Fraud indicators and early warning views
- Designing dashboards around suspicious patterns and anomaly signals
- Highlighting repeat behavior, unusual timing, payment patterns, or claim clusters
- Combining business rules and indicator logic in a visual reporting context
- Presenting fraud related views carefully to support investigation rather than overclaiming
Module 9: Customer and service dashboards
- Building customer dashboards around service requests, complaints, and contact patterns
- Linking customer metrics to retention and claims related outcomes
- Segmenting dashboards by channel, product, and customer profile
- Supporting service improvement with clear customer focused visuals
Module 10: Interactive reporting and dashboard design
- Using slicers, drill through, bookmarks, and navigation for business usability
- Designing dashboards for executives, team leads, and operational users
- Avoiding clutter and choosing visuals that match the business question
- Balancing detail and clarity in insurance reporting environments
Module 11: Insight communication and decision support
- Turning dashboard outputs into business narratives and action points
- Explaining trends, exceptions, and uncertainties in plain language
- Comparing static reports with interactive decision support views
- Creating dashboard structures that support meetings, reviews, and follow up actions
Module 12: Governance, refresh, and long term reporting practice
- Data quality checks and refresh discipline for trusted reporting
- Managing KPI definitions, ownership, and version consistency
- Sharing dashboards responsibly across teams and management levels
- Building a practical roadmap for stronger insurance analytics and reporting maturity
