UC14: Actuarial Data Synthesiser

The Problem

Actuaries need comprehensive claims data summaries to perform pricing analysis, reserve estimation, and trend projections. Manually compiling data from disparate sources (claims warehouse, enrollment system, provider contracts) is time-consuming; actuaries spend days preparing exhibits and validating data quality. Standardized actuarial exhibits (by age, gender, state, provider, diagnosis) must be calculated correctly to support pricing credibility and regulatory submissions.

What the Agent Does

Multi-Year Claims Aggregation: Retrieves claims data across 3 to 5 years, aggregates by relevant dimensions (age, gender, state, product, provider, diagnosis), and produces standardized claim counts and costs.
Exhibit Generation: Produces standard actuarial exhibits (medical loss ratio by product, cost per member per month (PMPM) by age/gender, top diagnoses by cost, trend factors year-over-year).
Anomaly Detection & Flagging: Identifies unusual trends (sudden spike in specific diagnosis, geographic outlier, seasonal variation) and flags for actuarial review.
Enrollment Normalization: Calculates enrolled member months (denominator for PMPM calculations), handling mid-year joiners/leavers and coverage suspensions.
Data Quality Assessment: Reports on data completeness (e.g., "Claims for 95% of enrolled members", "Diagnosis code coverage 88%"), missing values, and data gaps.
Comparable Benchmarking: If industry benchmarks are available, compares payer's metrics against benchmarks to identify areas of over/under performance.
Report Formatting & Export: Delivers exhibits in standard formats (Excel, PDF) suitable for regulatory submission or board presentation.

Data Requirements

Data Sources:

Historical claims data (3 to 5 years) , medical and pharmacy claims with full detail (member ID, service date, diagnosis, procedure, cost, provider)
Member enrollment file (historical snapshots) , enrollment counts by month, demographics (age, gender, state, product), coverage status
Provider contracts (if internal cost data available) , contract rates, network status
Industry benchmarks (e.g., AHRQ, Milliman, Truven data) , comparative metrics (optional but valuable)
Actuarial assumptions , mortality tables, trend factors, elasticity assumptions

Data Classification:

Public: Industry benchmarks (purchased or public sources)
Internal Structured: Claims data warehouse, enrollment snapshots, contract data
Internal Unstructured: Actuarial workpapers, prior trend analysis, pricing assumptions
External Third-Party: Industry benchmarks
Sensitive/Regulated: Claims data is PHI; actuarial analysis is sensitive and typically not disclosed publicly (proprietary)

Data Quality Requirements:

Timeliness: Claims data lag of 30 to 60 days acceptable for annual pricing; more frequent analysis (monthly) requires more recent data cuts.
Completeness: Claims warehouse should be 98%+ complete for enrolled members; enrollment data should have 99%+ accuracy.
Accuracy: Diagnosis/procedure codes must be valid; amounts must be accurate; member ID consistency must be high.

Integration Complexity: Low-Medium , Most payers have claims data warehouse in place; actuarial analysis is standard data manipulation. Complexity is mainly in correctly implementing PMPM calculations, enrollment normalization, and trend factor estimation.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	3	0.45
Error Reduction	10%	3	0.30
Cost Avoidance	10%	2	0.20
Strategic Leverage	5%	3	0.15
Data Availability	15%	5	0.75
Process Clarity	15%	4	0.60
Ease of Implementation	10%	5	0.50
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.85

Why It Scores Well

Time savings are direct: automating data compilation saves actuaries 10 to 20% of time on pricing cycle (valuable during annual rate-setting cycle). Error reduction from consistent calculation and audit trail. Data is readily available in claims warehouse. Process is well-defined. Strategic leverage is moderate (good actuarial analysis is important but not a competitive advantage; poor analysis is a liability).

Regulatory Alignment

CMS Rate Certification: MA plans must submit certified rates to CMS; actuarial data and assumptions must be documented.
State Insurance Rate Filings: Payers filing rates with state DOI must provide actuarial justification; standardized exhibits support credibility.

Sprint Factory Fit

Sprint 0 (2 weeks) + 2 build sprints (4 weeks)

Actuarial data synthesis is a straightforward data aggregation and reporting task. Implementation is quick (2 sprints). Can be implemented as add-on to existing claims warehouse infrastructure. Recommended for all payers performing rate-setting. Fallback is manual analysis using SQL or Excel.

Comparable Implementations

Milliman Actuarial Software , Industry standard for actuarial analysis and reporting
Health Care Cost Institute (HCCI) Data Analysis , Benchmarking and trend analysis
Standard actuarial exhibits , Published standards for exhibit formats

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