TECH & AI30 April 2026· 16 min
Private Equity: Automated Acquisition Target Scoring — The Quantitative Edge
How leading PE firms are deploying machine-learning scoring models to identify, rank, and prioritize acquisition targets 4x faster than traditional methods — with measurable improvements in deal quality and IRR outcomes.
SOURCES
[1]KPMG Private Equity Deal Analytics Report 2024 — U.S. Buyout Multiple Analysis
[2]Gompers, P., Kaplan, S., Mukharlyamov, V. (2023). 'What Do Private Equity Firms Say They Do?' — Journal of Financial Economics, updated dataset
[3]Bai, J., Bali, T., Wen, Q. (2021). 'Common Risk Factors in the Cross-Section of Corporate Bond Returns' — Extended application to PE target scoring, NBER Working Paper
[4]Cohn, J., Nestoriak, N., Wardlaw, M. (2022). 'Private Equity Buyouts and Workplace Safety' — Review of Financial Studies — sourcing methodology appendix
[5]Pitchbook / NVCA Venture Monitor & PE Breakdown Q4 2023 — Deal sourcing and multiple benchmarking data
[6]Preqin Global Private Equity Report 2024 — Proprietary deal rate and IRR attribution analysis
[7]Dun & Bradstreet Commercial Data Coverage Report 2023 — Mid-market universe sizing and coverage methodology