Insurance Risk and Claims Operations Dashboard Using Tableau | RUSA Analytics
Introduction
Insurance companies manage thousands of claims involving different claim amounts, processing timelines, risk indicators, and investigation requirements. Without a structured reporting system, identifying urgent claims and monitoring operational performance can become difficult.
RUSA Analytics developed an Insurance Risk and Claims Operations Dashboard using Tableau to demonstrate how insurance data can be transformed into an interactive analytical solution. The dashboard brings important risk and operational indicators into one view, helping users identify claims that may require additional attention.
Instead of reviewing separate spreadsheets and static reports, insurance teams can explore claim risk, monitor service-level agreement (SLA) breaches, review investigation referrals, track reopened claims, and examine estimated claims leakage through visual analytics.
This dashboard is part of the RUSA Analytics Insurance Claims and Underwriting Intelligence Tableau proof of concept, built with synthetic data for demonstration and portfolio purposes.
What Is an Insurance Risk and Claims Operations Dashboard?
An Insurance Risk and Claims Operations Dashboard is an interactive reporting solution that combines claims information, risk indicators, processing timelines, investigation referrals, and potential financial exposure.
Using Tableau, insurance analysts can filter the information, compare claim characteristics, and investigate patterns across the reporting period, product, geography, policy type, and other available dimensions.
The primary objective is to help teams answer questions such as:
Which claims have high risk scores and may need further review?
How many claims have exceeded their defined SLA thresholds?
Which claims have been referred for Special Investigation Unit (SIU) review?
How many claims have been reopened?
Which risk indicators occur most frequently?
Where should claims teams prioritize their next review?
The dashboard supports operational monitoring and prioritization. It does not automatically establish that a claim is fraudulent or that a flagged claim contains an actual error.
Why Tableau Is Useful for Insurance Risk Analytics
Tableau makes it possible to explore complex insurance datasets through interactive charts, KPI cards, filters, and detailed claim tables.
For RUSA Analytics, Tableau provides a practical way to demonstrate how claims operations data can support more informed review and prioritization.
Key benefits include:
Centralized monitoring: Bring operational risk indicators together in one dashboard.
Faster identification of priorities: Highlight claims with high risk scores, SLA breaches, or investigation referrals.
Interactive analysis: Filter the dashboard to investigate patterns across available business dimensions.
Operational visibility: Compare risk indicators and understand where additional review may be required.
Data-driven decision support: Use consistent measures and visual summaries to guide further investigation.
Understanding the RUSA Analytics Tableau Dashboard
The Risk and Claims Operations Dashboard contains five headline indicators and three major analytical visualizations, followed by a detailed investigation priority queue.
1. High-Risk Claims
The High-Risk Claims KPI displays the number of distinct claims with a risk score meeting the dashboard's configured threshold.
In this proof of concept, the threshold is a risk score of 80 or above.
This indicator helps analysts identify claims that may warrant closer review. A high score is a synthetic triage indicator, not a validated fraud probability or proof of suspicious activity.
2. SLA Breach Rate
The SLA Breach Rate shows the proportion of claims flagged as exceeding their defined service-level agreement threshold.
Monitoring this measure helps teams identify possible processing delays and evaluate whether operational workflows require attention.
A high breach rate should prompt further investigation into claim complexity, workload, documentation requirements, and processing practices rather than an automatic conclusion that staff performance is poor.
3. Reopened Claims
The Reopened Claims KPI measures the number of distinct claims marked as reopened.
Reopened claims may require additional analysis because they can involve new information, unresolved documentation, revised assessments, or other follow-up activity.
Reviewing this measure alongside claim status and claim age can help teams understand recurring operational issues.
4. SIU Referrals
The SIU Referrals KPI tracks claims flagged for referral to a Special Investigation Unit.
These referrals indicate that a claim has been selected for additional investigation or review. A referral is not proof of fraud, and the dashboard should not be used to make that assumption.
5. Estimated Claims Leakage
Estimated Leakage summarizes the estimated financial amount associated with the dataset's leakage field.
This measure can help analysts identify claims that may require financial or procedural review. However, an estimate is not necessarily a confirmed loss, recoverable amount, or proven overpayment. Its meaning depends on the business rules used to generate the source field.
Key Visualizations in the Tableau Dashboard
Claim Risk Prioritization — Scatter Plot
The claim risk prioritization scatter plot compares claim amount with risk-related information. The distribution helps analysts explore how claims differ in financial size and risk characteristics.
Analysts can look for claims that combine relatively high claim amounts with elevated risk scores or other review indicators.
This visual helps answer questions such as:
Are high-value claims concentrated in particular risk groups?
Are there unusual patterns that require further investigation?
Which claims should be reviewed first based on the organization's established rules?
A scatter plot highlights patterns; it does not independently determine whether a claim is fraudulent.
Investigation Donut Chart
The investigation donut chart summarizes the distribution of claims across the investigation-related categories used in the dashboard.
It provides a quick overview of the volume associated with different risk indicators or investigation outcomes, depending on the field assigned to the chart.
This visualization helps analysts compare categories and identify where review activity is concentrated.
Primary Risk Indicators
The Primary Risk Indicators chart ranks the available risk indicators by their frequency in the dataset.
The dashboard includes indicators such as:
Documentation review
High severity
Late reporting
Repeated reopening
Reserve volatility
These indicators provide useful context for claims review. For example, frequent documentation-related flags may justify examining documentation workflows, while repeated reopening may indicate a need to review claim resolution processes.
These categories are analytical flags, not definitive evidence of misconduct.
Investigation Priority Queue
The investigation priority queue provides claim-level detail for further review. It includes fields such as Claim ID, Claim Amount, Days Open, Risk Score, and SIU Referral.
This table connects high-level dashboard summaries with individual synthetic claim records, allowing an analyst to identify the records behind a pattern.
In a production environment, access to claim-level information should follow the organization's privacy, security, and role-based access requirements.
How the Dashboard Supports Insurance Operations
The dashboard demonstrates several practical use cases for insurance teams.
Risk-based claim review: Combine risk scores, claim amounts, and other indicators to prioritize manual assessment.
SLA monitoring: Identify claims that have crossed their defined processing thresholds and investigate the reasons for delays.
Investigation management: Review SIU referrals and risk-indicator distributions to understand where additional investigation may be needed.
Reopened claim analysis: Monitor repeated claim activity and explore opportunities to improve documentation or resolution processes.
Potential leakage review: Examine claims with estimated leakage indicators and direct them for appropriate financial validation.
The dashboard supports better visibility and prioritization, while final decisions remain with qualified claims, investigation, and operational personnel.
How RUSA Analytics Built the Dashboard in Tableau
The dashboard follows a structured analytics workflow.
Step 1: Prepare the Data
The project uses synthetic insurance data representing claim details, policy information, dates, products, geography, and claim transactions.
The source fields are reviewed for appropriate data types, missing values, key uniqueness, and consistency before analysis.
Step 2: Build the Data Model
The Tableau model uses logical relationships between the relevant data tables rather than indiscriminately joining fact tables with different levels of detail.
This approach helps reduce the risk of duplicated claim amounts and incorrect aggregations when combining information from different tables.
Step 3: Create Calculated Measures
Tableau calculated fields are used to define measures such as high-risk claim counts, SLA breach rate, reopened claims, SIU referrals, and estimated leakage.
Each measure should be checked against the source data and the agreed business definition before being used in a dashboard.
Step 4: Create Worksheets
Separate Tableau worksheets are created for KPI cards, claim risk prioritization, investigation distribution, primary risk indicators, and the detailed priority queue.
Each worksheet has a specific analytical purpose and is formatted to support clear interpretation.
Step 5: Assemble the Dashboard
The worksheets are arranged into a single dashboard with consistent headings, filters, readable labels, and a clear visual hierarchy.
The layout places the most important operational indicators near the top, analytical charts in the middle, and claim-level details toward the bottom.
Step 6: Validate the Results
The KPI values and chart totals are checked against the source data. Filters are tested to ensure that the displayed values respond as intended, and the team checks for duplicated measures, incorrect aggregations, and inconsistent definitions.
This validation step is essential because a visually appealing dashboard is only useful when its calculations are trustworthy.
Flowchart 1: Tableau Insurance Risk Analytics Workflow
Synthetic Data → Data Quality Checks → Data Modeling → Tableau Calculations → Risk and Operations Worksheets → Interactive Dashboard → Validation →
Business Review
This workflow demonstrates how RUSA Analytics transforms source data into an analytical dashboard. Data preparation and validation are important at every stage, not just at the end.
Flowchart 2: Claims Review and Prioritization Process
Claim Data Available → Review Risk Score and Operational Indicators → Check Claim Amount and Days Open → Identify SLA Breaches or SIU Referrals → Prioritize for Manual Review → Document Findings → Take Appropriate Action
The workflow is intended to support a human-led review process. A risk flag should lead to appropriate assessment, not an automatic fraud conclusion or adverse decision.
Important Considerations Before Using Risk Dashboards in Production
A risk and claims operations dashboard should be treated as a decision-support tool rather than a replacement for professional judgment.
Before using a similar solution in a live insurance environment, organizations should:
Define each KPI and risk indicator clearly.
Validate calculated measures against trusted source records.
Review risk thresholds for relevance and potential bias.
Protect sensitive claim and customer information.
Monitor data quality and refresh reliability.
Distinguish estimated leakage from confirmed financial loss.
Ensure that claim referrals and adverse decisions receive appropriate human review.
The RUSA Analytics proof of concept uses synthetic data. Its values and patterns are
illustrative and should not be interpreted as actual insurance company performance.
Conclusion
The RUSA Analytics Insurance Risk and Claims Operations Dashboard demonstrates how Tableau can make insurance claims information easier to explore, monitor, and prioritize.
By bringing together high-risk claim counts, SLA breach monitoring, reopened claims, SIU referrals, estimated leakage, risk indicators, and claim-level details, the dashboard provides a practical example of operational insurance analytics.
The wider lesson is that effective dashboards require more than charts. They need a sound data model, clearly defined measures, appropriate visual design, reliable validation, and responsible interpretation of risk indicators.
Through this Tableau proof of concept, RUSA Analytics showcases an approach to making insurance risk and claims operations data more accessible for analysis and review.
About RUSA Analytics
RUSA Analytics explores data analytics and business intelligence solutions that help turn complex datasets into meaningful insights. Through projects using Tableau and Power BI, RUSA Analytics demonstrates how well-designed data models, validated metrics, and interactive dashboards can support better business understanding.
Explore more at the RUSA Analytics website: https://www.rusaanalytics.com/
Related Resources
Explore RUSA Analytics: https://www.rusaanalytics.com/
Learn more about Tableau: https://www.tableau.com/
Explore Microsoft Power BI: https://powerbi.microsoft.com/
Explore the Next Dashboard in the Insurance Analytics Series
Discover how the Insurance Claims & Executive Portfolio Analytics Dashboard in Tableau provides an executive-level portfolio overview and how the Underwriting and Portfolio Analytics Dashboard examines premium performance, quote-to-bind conversion, renewals, and risk tiers. Continue with the Insurance Risk and Claims Operations Dashboard Using Tableau to explore operational risk monitoring and claims prioritization.
Comments