Insurance Data Analytics for Claims and Fraud Detection Training Course

Course overview

Claims teams hold rich operational data: first notification of loss records, adjuster notes, repair estimates, payment histories, policy changes, claimant details and provider invoices. Yet many insurers still rely on manual queue reviews, static exception lists and fragmented spreadsheets to identify leakage, prioritise complex claims and escalate suspected fraud. This course equips participants to turn claims data into evidence-led decisions that improve triage, shorten investigation time and make fraud referrals more defensible.

Participants learn to define claims and fraud analytics use cases, assess data quality, create analysis-ready datasets and select meaningful indicators across frequency, severity, cycle time, reserve movement, payment patterns and recovery outcomes. They use SQL, Excel, Power BI and introductory Python techniques to profile claims portfolios, detect duplicate and anomalous records, segment claimants and providers, build rule-based fraud indicators, and communicate findings through dashboards and investigation-ready case summaries. The course also addresses false positives, model performance measures, governance, privacy and the distinction between a risk signal and proof of fraud.

Teaching is structured around a realistic insurance claims dataset covering motor, property and liability claims. Instructor demonstrations are followed by guided data labs, team-based case reviews and decision workshops involving suspicious provider networks, inflated repair costs and unusual claim timing. By the end of the week, each participant produces a claims fraud analytics pack: a documented data-quality assessment, prioritised fraud-risk rules, a Power BI dashboard specification, investigation queue logic and a 90-day implementation plan for their own claims environment.

The course is suited to claims professionals moving into analytics, fraud and financial-crime teams, insurance data analysts, operational managers and technology staff supporting claims platforms. It is designed for professionals who need practical analysis methods rather than a purely statistical or machine-learning curriculum.

Course objectives

By the end of this course, participants will be able to:

• Profile claims datasets using completeness, validity, uniqueness and timeliness data-quality checks
• Write SQL queries to join policy, claim, payment, claimant and provider records for investigation analysis
• Calculate claims frequency, severity, leakage, reserve-development and cycle-time indicators
• Detect duplicate payments, abnormal claim values and suspicious timing patterns using exception-analysis methods
• Construct rule-based fraud-risk scores using weighted indicators and documented alert thresholds
• Segment claimants, repairers, medical providers and claims handlers to identify unusual behavioural patterns
• Build a Power BI claims dashboard with drill-through views for triage, investigation and management reporting
• Produce an investigation-ready analytics pack containing findings, evidence references, risk rules and implementation actions

Benefits of attending

Target competencies

Who should attend

• Claims Analysts — who need to turn operational claims records into triage and leakage insights
• Fraud Investigators — who require stronger data-led referral criteria and case prioritisation evidence
• Claims Managers — who oversee claim quality, settlement speed and fraud-control performance
• Insurance Data Analysts — who support claims reporting, portfolio analysis and operational dashboards
• Special Investigations Unit Staff — who assess suspicious claims, claimant behaviour and provider activity
• Business Intelligence Developers — who build reporting solutions for claims, underwriting and fraud teams

Requirements and prerequisites

Participants should be comfortable working with tabular data in Microsoft Excel, including filters, sorting, basic formulas and pivot tables. Familiarity with insurance claims terminology such as policy, peril, reserve, indemnity payment, claimant, recovery and first notification of loss is useful, but it will be reinforced during the course. No previous SQL, Python, Power BI, predictive modelling or fraud-investigation qualification is required. Complete beginners should expect guided exercises in each tool and should be prepared to work with sample claims data, interpret basic charts and perform simple calculations.

Training methodology

The course combines short instructor-led explanations with daily hands-on analysis of an insurance claims dataset. Participants work in Excel and SQL to clean and query records, then use Power BI and guided Python notebooks to visualise trends and test anomaly indicators. Case studies require teams to assess suspicious claims, provider relationships and payment patterns while documenting the evidence needed for escalation. Each day closes with a practical output, and the final workshop converts these outputs into a prioritised fraud analytics implementation plan for the participant's own function.

Course outline

Day 1: Claims data foundations and analytics use cases

• Claims lifecycle data from first notification of loss to closure and recovery
• Core insurance claims entities: policy, claimant, loss, reserve, payment and provider
• Claims leakage, fraud, waste and abuse analytical use cases
• Data dictionaries, field lineage and claims-data ownership
• Data-quality dimensions for operational claims extracts
• Excel profiling with pivot tables, filters and conditional formatting
• Defining measurable claims and fraud analytics questions

Workshop: Participants profile a motor claims extract and produce a data-quality scorecard with priority remediation issues.

Day 2: Querying and preparing claims data

• Relational data structures for policy, claim, payment and provider tables
• SQL SELECT, WHERE, ORDER BY and aggregate functions for claims analysis
• INNER JOIN and LEFT JOIN techniques for linking claims records
• Date calculations for reporting delay, settlement duration and claim timing
• Identifying missing values, duplicate records and inconsistent reference keys
• Creating analysis-ready fields for severity, frequency and payment behaviour
• Documenting transformation logic for auditability and reuse

Workshop: Participants write SQL queries that join claim, payment and provider tables and create a cleaned investigation dataset.

Day 3: Claims performance and anomaly analysis

• Frequency, severity, average cost and loss-ratio supporting indicators
• Reserve movement and settlement variance analysis
• Cycle-time analysis across notification, assessment, payment and closure stages
• Outlier detection using percentiles, interquartile range and z-scores
• Duplicate-payment and repeated-invoice matching techniques
• Trend analysis by peril, geography, claimant segment and repairer
• Interpreting anomalies without treating statistical signals as fraud proof

Workshop: Participants investigate anomalous repair invoices and produce an exception list ranked by financial exposure and review rationale.

Day 4: Fraud indicators, scoring and visual investigation

• Common claims fraud typologies in motor, property and liability insurance
• Rule-based indicators for claimant, policy, loss-event and payment behaviour
• Provider and repairer risk indicators including concentration and repeat usage
• Weighted fraud-risk scoring and alert-threshold design
• Precision, recall, false positives and investigation-capacity trade-offs
• Power BI data modelling, measures and drill-through investigation views
• Privacy, fairness, case confidentiality and fraud analytics governance

Workshop: Participants design a weighted fraud-risk score and build a Power BI triage view for suspicious claims and providers.

Day 5: Operationalising claims fraud analytics

• Investigation queue design and risk-based case prioritisation
• Evidence packs, referral narratives and escalation decision criteria
• Control testing for claims payments and fraud-rule effectiveness
• Dashboard KPIs for leakage prevention, referrals and investigation outcomes
• Monitoring rule drift, alert volumes and false-positive rates
• Roles of claims operations, SIU, compliance, data governance and IT
• Ninety-day roadmap for deploying a claims analytics use case

Workshop: Participants present a claims fraud analytics pack containing triage rules, dashboard measures, governance controls and a 90-day action plan.

Tools & standards covered

Microsoft Excel, Microsoft SQL Server, Microsoft Power BI, Python

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions