Course Introduction
The Insurance Data Analytics and Claims Intelligence Training Course provides a practical framework for transforming insurance data into actionable insights for claims management, underwriting support, fraud detection, portfolio monitoring, customer service, operational improvement, and risk-informed decision-making. The program connects insurance domain knowledge with modern analytics techniques to help participants understand how policy, claims, customer, payment, reserve, exposure, and operational data can be structured and analyzed to improve visibility across the insurance value chain.
Participants will learn how to work with diverse insurance datasets, including policy records, claim notifications, claim transactions, loss amounts, reserves, payments, claimant information, insured assets, coverage details, premiums, deductibles, adjuster activities, service-provider information, and historical outcomes. The course emphasizes data quality, consistent business definitions, claims lifecycle structures, temporal analysis, segmentation, reconciliation, and analytical modeling so that insurance organizations can develop reliable datasets for operational and management reporting.
A major focus of the course is claims performance analytics. Participants will examine claim frequency, severity, incurred and paid losses, outstanding reserves, settlement duration, reopening rates, claim aging, claims by cause, claims by product, geographic loss patterns, and other indicators used to understand claims performance. They will learn how to identify emerging trends and operational exceptions by combining historical claims information with policy, exposure, customer, location, and financial data, enabling more comprehensive analysis than isolated claims reporting.
The program also introduces analytical approaches for identifying unusual claims behavior and potential fraud indicators. Participants will learn how to use segmentation, anomaly detection concepts, rules-based indicators, historical patterns, network relationships, and predictive modeling concepts to support claims investigation. The emphasis is on analytical prioritization and evidence-based investigation rather than treating an algorithmic signal as proof of fraudulent behavior, with appropriate attention to data quality, governance, explainability, privacy, and human review.
Modern business intelligence tools can provide an effective foundation for insurance claims reporting and intelligence. Power BI can connect to numerous data sources, use Power Query for repeatable data preparation, create relationships and DAX calculations within semantic models, and produce interactive reports and dashboards. Its service capabilities also include scheduled refresh, alerts, sharing, security roles, and other features that can support governed insurance reporting environments.
By the end of the five-day program, participants will be able to build an integrated claims intelligence framework covering data preparation, claims KPIs, loss analytics, operational performance, fraud indicators, reserve and payment analysis, customer and provider intelligence, predictive analytics, and executive reporting. Through practical exercises and a capstone project, learners will analyze realistic insurance datasets, develop claims-performance measures, investigate patterns and exceptions, construct an interactive dashboard, and present data-driven findings that can support claims managers, insurers, brokers, actuaries, risk teams, and senior decision-makers.
Duration
5 days
Who Should Attend
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Claims managers responsible for monitoring claims performance, settlement processes, service quality, and operational efficiency.
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Claims analysts conducting loss analysis, claims segmentation, trend analysis, and performance reporting.
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Insurance data analysts working with policy, claims, customer, financial, and operational datasets.
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Underwriting professionals seeking deeper insight into claims experience, loss trends, and portfolio characteristics.
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Insurance fraud investigators interested in analytical techniques for identifying unusual claims behavior and investigation priorities.
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Actuarial and risk professionals analyzing claims frequency, severity, loss development, reserves, and portfolio performance.
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Insurance operations managers responsible for claims workflows, service-level performance, and process improvement.
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Insurtech professionals developing data-driven insurance products, claims solutions, analytics platforms, or intelligence tools.
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Financial analysts supporting insurance loss reporting, claims payments, reserves, profitability, and management reporting.
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Internal audit and compliance professionals reviewing claims controls, exceptions, operational patterns, and data quality.
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Customer experience and service professionals analyzing claims journeys, settlement times, complaints, and customer outcomes.
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Insurance executives and managers seeking practical capabilities for data-driven claims and portfolio decision-making.
Course Objectives
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Develop a comprehensive understanding of insurance data analytics and apply analytical methods to claims, policy, customer, financial, operational, and exposure information.
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Build reliable insurance datasets by integrating claims transactions, policy records, payment data, reserves, coverage information, customer attributes, provider records, and loss classifications.
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Calculate and interpret claims KPIs including claim frequency, severity, incurred losses, paid losses, outstanding amounts, settlement duration, reopen rates, and claims aging.
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Analyze claims portfolios across products, geographies, customer segments, causes of loss, channels, claim types, policy characteristics, and time periods to identify meaningful performance patterns.
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Apply analytical techniques for identifying unusual claims behavior, prioritizing investigation cases, detecting anomalies, and supporting fraud-management processes while maintaining appropriate human review.
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Analyze claims financial performance by connecting premiums, exposures, incurred losses, payments, reserves, expenses, and claim outcomes to understand portfolio-level loss characteristics.
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Develop predictive analytics concepts for claims severity, claim duration, escalation, settlement outcomes, reserve requirements, and other operational or financial insurance indicators.
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Design interactive claims intelligence dashboards that communicate operational performance, loss trends, fraud indicators, geographic patterns, financial exposure, and management-level exceptions.
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Apply data-quality, governance, privacy, security, and analytical-model validation principles to ensure that insurance analytics outputs are reliable, explainable, appropriately controlled, and fit for business use.
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Complete an integrated claims intelligence project involving data preparation, KPI development, claims analysis, anomaly investigation, predictive modeling concepts, dashboard development, and executive recommendations.
Comprehensive Course Outline
Module 1: Insurance Data Analytics and Claims Intelligence Foundations
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Understanding the insurance data ecosystem and the relationships between policies, insured exposures, premiums, claims, payments, reserves, customers, providers, underwriting, and financial performance.
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Identifying key insurance data sources including policy administration systems, claims systems, payment platforms, customer records, underwriting databases, external data, service-provider records, and operational systems.
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Designing insurance analytical structures using policy identifiers, claim identifiers, coverage codes, product classifications, loss causes, customer segments, geographic hierarchies, and reporting periods.
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Establishing insurance data-quality principles covering completeness, accuracy, uniqueness, consistency, timeliness, reconciliation, lineage, business definitions, and controlled reference data.
Module 2: Claims Data Preparation and Analytical Data Modeling
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Preparing claims datasets by standardizing dates, claim statuses, transaction types, payment categories, loss classifications, reserve fields, policy attributes, and claimant information.
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Integrating claims, policy, customer, premium, payment, reserve, provider, geographic, and operational datasets into a unified analytical environment.
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Designing analytical data models that connect claim events, policy periods, transactions, customers, coverage, loss causes, financial measures, adjusters, service providers, and calendar dimensions.
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Using Power Query to connect to, reshape, clean, and combine insurance data sources through repeatable transformation processes that can be refreshed as source data changes.
Module 3: Claims Performance and Loss Analytics
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Measuring claim frequency, claim severity, incurred losses, paid losses, outstanding claims, average claim cost, settlement duration, and other core claims-performance indicators.
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Analyzing claims by product, coverage, geography, cause of loss, customer segment, policy type, channel, claim handler, service provider, and reporting period.
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Investigating claims trends and changes in loss experience by comparing historical performance, current reporting periods, emerging patterns, and portfolio segments.
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Developing claims-performance dashboards that combine operational, financial, frequency, severity, aging, settlement, and exception indicators into an integrated management view.
Module 4: Claims Financial Analytics, Reserves and Payments
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Structuring claims financial data around incurred amounts, paid amounts, outstanding amounts, reserves, recoveries, expenses, deductibles, settlements, and claim transaction histories.
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Performing paid-versus-incurred analysis to understand claims cash flow, outstanding exposure, settlement patterns, and financial changes across reporting periods.
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Analyzing reserve movements and claims development patterns to identify unusual changes, aging concentrations, large-loss exposure, and emerging financial pressures.
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Building financial claims dashboards that connect claim-level transactions with policy, product, geography, customer, and operational information for more comprehensive loss analysis.
Module 5: Claims Operations, Service and Process Analytics
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Measuring claims operational performance using notification volumes, assignment times, response times, processing durations, settlement times, reopened claims, pending claims, and service-level indicators.
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Analyzing claims workflows to identify bottlenecks, aging concentrations, workload imbalances, process exceptions, repeated activities, and operational performance differences.
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Evaluating adjuster, team, branch, channel, and service-provider performance using consistent productivity, quality, workload, and settlement measures.
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Developing process-improvement analytics that connect operational indicators with customer outcomes, claim costs, settlement duration, complaints, and claims experience.
Module 6: Fraud Analytics, Anomaly Detection and Investigation Intelligence
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Understanding analytical approaches to insurance fraud detection, including business rules, anomaly detection, segmentation, historical comparisons, link analysis, and predictive risk scoring.
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Identifying unusual claims patterns using claim frequency, timing, amounts, repeated parties, unusual combinations of attributes, rapid claim activity, and other investigation indicators.
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Building analytical prioritization models that help investigators focus attention on claims requiring additional review while distinguishing indicators from confirmed investigation outcomes.
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Designing fraud analytics dashboards that show investigation volumes, analytical alerts, claim characteristics, geographic patterns, provider relationships, outcomes, and trends while supporting appropriate access controls.
Module 7: Customer, Provider and Geographic Claims Intelligence
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Segmenting customers and policyholders using claims experience, product relationships, loss patterns, service interactions, tenure, exposure characteristics, and other relevant attributes.
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Analyzing repairers, medical providers, assessors, brokers, agents, contractors, and other claims-service participants using volume, cost, turnaround time, exception, and outcome indicators.
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Applying geographic analytics to identify concentrations of claims, loss events, severity patterns, catastrophe exposure, service demand, and regional operational differences.
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Combining customer, provider, geographic, and claims information to identify relationships and patterns that can support service improvement, operational planning, and investigation workflows.
Module 8: Predictive Claims Analytics and Insurance Forecasting
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Developing analytical approaches for predicting claim frequency, claim severity, settlement duration, escalation likelihood, reopening risk, and other claims-related outcomes.
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Preparing historical insurance data for predictive modeling by defining target variables, selecting features, handling missing information, separating training and validation data, and evaluating model performance.
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Exploring classification, regression, segmentation, anomaly detection, and time-series concepts applicable to claims intelligence and insurance operational forecasting.
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Interpreting predictive-model results using appropriate validation, explainability, performance metrics, monitoring, and business controls rather than relying solely on automated predictions.
Module 9: Claims Intelligence Dashboards, KPIs and Reporting
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Designing insurance KPI frameworks covering claims frequency, severity, incurred losses, paid losses, reserves, settlement time, aging, operational productivity, fraud indicators, and customer outcomes.
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Building Power BI semantic models, measures, relationships, and analytical calculations that support repeatable claims reporting and interactive management analysis.
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Creating executive and operational reports with filters, drill-through analysis, trend visuals, segmentation, exception views, geographic analysis, and claim-level investigation pathways.
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Developing governed claims dashboards and reporting processes using refresh, sharing, alerts, security roles, and appropriate access controls for different insurance stakeholder groups.
Module 10: Capstone Insurance Data Analytics and Claims Intelligence Project
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Preparing and validating a realistic insurance dataset containing policies, claims, payments, reserves, customers, providers, geographic attributes, loss causes, and operational information.
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Developing an integrated claims intelligence dashboard presenting frequency, severity, incurred and paid losses, outstanding exposure, settlement performance, aging, anomalies, and operational KPIs.
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Conducting a detailed claims investigation using segmentation, trend analysis, anomaly indicators, financial analysis, geographic patterns, and predictive analytics concepts to identify important findings.
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Presenting a final insurance analytics report that combines data-quality findings, claims intelligence, financial insights, operational recommendations, dashboard outputs, and an actionable management improvement roadmap.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
