Transforming Actuarial Capability for a Growing International Health MGA

Client Wins

> Created a single vision for pricing, reserving and reporting across the organisation.

> Improved transparency and governance for reinsurers, carriers and investors.

> Identified operational risks and control weaknesses before they could affect future growth.

> Established the foundations for automation, reducing reliance on manual spreadsheet processes.

> Positioned the business to support future advanced analytics, machine learning and AI-enabled decision making.

Client overview

Our client is a rapidly growing international health insurer MGA operating across multiple markets, working closely with carriers and reinsurers to deliver specialist health products.

As the business scaled, its actuarial and data processes—built around Excel-based tools and evolving data architecture—began to face increasing strain. The organisation needed a more robust, scalable and transparent actuarial framework capable of supporting growth, strengthening reinsurer confidence and meeting regulatory expectations.

The challenge

The client’s actuarial, data and reporting capabilities had evolved organically. While functional, this created several structural challenges:

  • Fragmented data architecture
  • Limited reserving sophistication and monitoring
  • Manual and inefficient reporting
  • Weak integration between pricing and reserving
  • Limited governance and auditability

These challenges created operational risk and limited the ability to scale efficiently or present a strong, defensible actuarial narrative to stakeholders.

Our approach

Broadstone conducted a structured Phase 1 diagnostic across five core pillars:

  • Methodology (pricing and reserving)
  • Data architecture
  • Data quality and lineage
  • Reporting and monitoring
  • Governance and controls 

The work involved:

  • Deep review of pricing models, reserving approaches and data flows
  • Mapping of the end-to-end actuarial ecosystem (data → models → reporting)
  • Identification of structural gaps and inconsistencies
  • Benchmarking against best practice actuarial and data frameworks

This diagnostic was designed not only to identify issues, but to define a pragmatic and deliverable roadmap for transformation.

Broadstone combines deep actuarial expertise with data and modelling capability to help insurers modernise their analytical infrastructure.

For this client, it meant:

  • Bridging actuarial insight and data engineering
  • Designing practical, implementable solutions (not just theory)
  • Aligning technical delivery with commercial and regulatory objectives

Client overview

Our client is a rapidly growing international health insurer MGA operating across multiple markets, working closely with carriers and reinsurers to deliver specialist health products.

As the business scaled, its actuarial and data processes—built around Excel-based tools and evolving data architecture—began to face increasing strain. The organisation needed a more robust, scalable and transparent actuarial framework capable of supporting growth, strengthening reinsurer confidence and meeting regulatory expectations.

STEP 1

End-to-end actuarial
architecture design

Delivering:

  • A unified analytical dataset
  • A structured data architecture that improved consistency, quality control and traceability from source data through to reporting.
  • A single source of truth for actuarial analysis and reporting 
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STEP 2

Reserving
framework specification

We designed a scalable reserving framework including:

  • Ultimate loss ratio (ULR) and triangular methods
  • Actual versus Expected monitoring and analysis of change
  • Clear separation of data, parameters and modelling logic
  • Defined outputs for reserving, reporting and governance
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STEP 3

Pricing model review
and redesign principles

We established improvements to pricing capability, including:

  • Transparent decomposition of risk premium (incidence × utilisation × cost)
  • Centralised and governed parameter structures
  • Improved linkage between pricing and reserving outputs

STEP 4

End-to-end actuarial
architecture design

We introduced the concept of a:

  • Parameter Master with ownership, versioning and approvals
  • Formalised change control and auditability
  • Strengthened documentation and reproducibility standards
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STEP 5

Reporting
and stakeholder outputs

We designed a pathway to:

  • Automated reinsurer reporting
  • Consistent, governed management information
  • Improved transparency for carriers and investors
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STEP 6

Deliver an
implementation roadmap

Including:

  • Python-based actuarial engines
  • Automated data pipelines and reporting
  • Embedded governance and testing frameworks
  • Scalable architecture aligned to future analytics and AI integration

Impact

The engagement provided the client with a clear roadmap for transforming its actuarial and data capabilities, enabling it to scale with confidence.

The client subsequently commissioned Phase 2 implementation support to begin delivering the target-state architecture and actuarial operating model.

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