Quantitative Due Diligence on a Private Markets Benchmarking Platform
Independent technical DD that de-risked a strategic platform investment decision.
An institutional investor was weighing a strategic investment in a third-party private markets benchmarking and analytics platform — and needed an independent, technically credible view of what it was actually buying. We ran a comprehensive quantitative due diligence engagement and delivered a findings-and-risk report that put the decision on solid ground.
The Challenge
The platform in question offered research-grade benchmarks and analytics for private markets — private infrastructure equity and debt, and private equity — built on transaction data and academic pricing models. Its outputs spanned thousands of segment indices, factor exposures, credit-spread and yield metrics, alpha attribution, and regulator-recognised benchmarks used for compliance and reporting. Before committing capital, the investor needed to understand the technical reality behind it, not the marketing narrative. Fact sheets and headline coverage figures rarely reveal how the underlying data is sourced and cleaned, how the valuation and index-construction models are designed, where coverage is genuinely deep versus modelled, or how heavily the methodology leans on particular assumptions and academic dependencies.
The investor had the commercial and financial picture but lacked the in-house quantitative and private-markets expertise to interrogate the methodology itself. Getting that assessment wrong — in either direction — carried real cost.
The Solution
We ran a comprehensive quantitative due diligence engagement, examining the platform across the dimensions that actually drive its value and its risk.
1. Data & methodology
We evaluated how the platform sourced, cleaned, and validated its underlying transaction and asset data across markets and vintages, and scrutinised the methodology behind its index construction, valuation, and pricing outputs to understand what was empirically grounded and where modelling assumptions were doing heavy lifting.
2. Model design & coverage
We reviewed the design of the platform's valuation, factor, and attribution models — including their treatment of illiquidity, comparable-based pricing, and alpha-versus-beta decomposition — and assessed coverage across sectors, geographies, and asset classes: where it rested on real observations, where it was thin or extrapolated, and how well it would hold up against the investor's intended use, including regulatory reporting.
3. Dependencies & architecture
We mapped the platform's data, methodological, and delivery dependencies — from its academic research base and index registrations to its API, Excel, and Python/R integration surface — to surface concentration risks, key-person and IP exposure, and the engineering realities behind the product.
Scope of assessment
Data sourcing and cleaning, index-construction and valuation methodology, factor and alpha-attribution model design, coverage and limitations across sectors and geographies, regulatory benchmark suitability, and data, methodological, and delivery dependencies — assessed independently and reported with a clear-eyed view of strengths, weaknesses, and risks.
The Results
We delivered an independent due diligence report with detailed findings, a structured risk assessment, and strategic recommendations. It gave the investor a clear, technically grounded basis for its decision — replacing the vendor's narrative with an objective view of the platform's methodology, data, and delivery strengths, weaknesses, and dependencies, and de-risking a significant strategic commitment.