Modernizing Investment Analytics with Foundry

How a Global Reinsurer Unified Portfolio Data and Improved Decision Confidence

Executive Summary

A global reinsurance provider faced growing operational risk and reporting latency driven by fragmented investment data and complex transformation logic embedded in legacy platforms. Andersen Consulting worked with the client’s data and analytics organization to migrate and modernize core investment analytics on Palantir Foundry, establishing a governed, scalable data foundation for portfolio, position, and holdings analysis. Using Foundry as enabling infrastructure, the team delivered harmonized investment datasets in under four months, resolved critical data defects, and restored business confidence in analytics used by investment operations and leadership. The initiative demonstrated Andersen Consulting’s ability to execute hands-on, results-driven delivery while building a durable platform for future analytics and AI-enabled use cases.

Introduction: Context and Challenge

Global insurers and reinsurers operate in an environment defined by market volatility, heightened capital scrutiny, and increasing demand for transparency in investment performance. Legacy data platforms struggle to support timely, auditable analytics across complex portfolios managed by external investment managers.

The client relied on an Amazon Redshift-based investment analytics environment that had grown brittle over time, with fragmented transformation logic and inconsistent business rules applied across portfolios. Core datasets for securities, issuers, portfolios, positions, and holdings, required extensive manual reconciliation before they could be trusted for downstream analysis and reporting. Defects surfaced frequently as business users validated data, slowing decision-making and eroding confidence in reported results. At stake was the integrity of investment reporting used to inform capital allocation, risk oversight, and executive decision-making. The organization needed a unified, governed data foundation that could scale with expanding data volumes and evolving business requirements.

Solution: AI-Powered Investment Analytics Built on Palantir Foundry and AIP

Approach Overview

Andersen Consulting mobilized a cross-functional delivery team embedded directly within the client’s data and analytics function. The engagement followed an outcome-first execution model, prioritizing data accuracy, business trust, and operational continuity over theoretical architecture. Rather than redesigning everything upfront, the team focused on stabilizing critical pipelines, resolving high-impact defects, and incrementally extending functionality. This approach enabled investment teams to validate progress continuously while maintaining momentum toward a modern analytics platform.

Data Integration and Foundation

Palantir Foundry served as the system of record for unified investment analytics. Andersen Consulting migrated data sourced from external investment managers out of Redshift and into Foundry, where it was standardized into a consistent, enterprise-aligned data model. Foundry’s ontology and lineage capabilities provided clear traceability from raw inputs through transformed outputs, allowing business users to understand how figures were derived. Governed pipelines ensured consistent application of transformation logic across securities, issuers, portfolios, and holdings, reducing reconciliation effort and supporting audit and compliance requirements.

AI and Insight Generation

While the initial phase focused on data modernization rather than end-user AI applications, the solution applied analytics-driven logic to automate complex classification and adjustment processes. For example, mapping rules were implemented to derive standardized asset classes from heterogeneous security attributes provided by multiple external managers. These rules replaced manual interpretation and spreadsheet-based adjustments, producing consistent classifications across portfolios. All transformations remained deterministic, explainable, and fully auditable, establishing a foundation suitable for future machine learning and AIP-enabled use cases without introducing model risk prematurely.

User Experience and Workflow Integration

Investment operations and investment analytics teams interacted directly with Foundry datasets during validation and review. Analysts could trace discrepancies to specific transformation steps, accelerating root-cause analysis and defect resolution. Data refreshes occurred on predictable schedules, enabling teams to work from a single source of truth rather than reconciling multiple extracts. Adoption was immediate because the platform aligned with how teams already analyzed data, while significantly reducing manual effort and rework.

Rapid Implementation

The core migration and stabilization effort was delivered over approximately 12–16 weeks. The team consisted of Andersen Consulting data engineers working alongside a Palantir forward deployed engineer and client analytics staff. Development followed a structured DevOps lifecycle with clear promotion paths from development to UAT and production. Business stakeholders prioritized defects and enhancements in close coordination with delivery teams, allowing the solution to evolve quickly while maintaining production stability.

Results and Impact

The initiative transformed how the client manages and trusts investment analytics data.

• Improved Data Accuracy and Trust: Resolution of critical defects and standardized transformations materially reduced reconciliation issues identified by investment teams.
• Faster Analytics Turnaround: Analysts accessed harmonized portfolio and holdings data without manual preprocessing, shortening analysis cycles by days in some reporting workflows.
• Operational Efficiency: Automated asset classification and adjustment handling reduced reliance on spreadsheets and manual overrides, freeing capacity across Investment Operations.
• Stronger Governance and Auditability: End-to-end lineage and controlled promotion processes improved confidence in data used for financial and risk reporting.
• Scalable Foundation: The platform now supports onboarding additional investment data with significantly less incremental effort.

Collectively, these outcomes shifted investment analytics from a reactive, defect-driven process to a controlled and scalable operation.

A New Standard for Investment Data Integrity

The collaboration established a new operating baseline for investment analytics built on trusted data and disciplined execution. By combining deep hands-on delivery with close business alignment, Andersen Consulting helped elevate the role of data from a persistent pain point to a strategic asset.

“We now have confidence in the numbers we put in front of leadership,” said the client’s Head of Investment Analytics. “The platform has changed how quickly we can respond to questions and manage complexity.”

Looking Ahead

The client plans to expand the Foundry footprint by onboarding additional datasets and extending analytics to new investment strategies. With a governed data foundation in place, the organization is positioned to introduce advanced analytics and selective AI use cases as business needs mature. The long-term roadmap supports a fully AI-enabled investment operating model without compromising control or trust.