M&A due diligence and decision framework
Built a reusable M&A framework that turns valuation, quality-of-earnings and financial-risk analysis into consistent, board-ready decision support across live transactions. One engagement has reached an acquisition recommendation and integration plan; no completed transaction outcome is disclosed.
Engagement record
Counterparties withheld by design
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Valuation
Board-ready valuation packs · live transactions
2026
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Quality of earnings
Normalised-earnings analysis · live transactions
2026
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Financial-risk matrix
Deal risk assessment · live transactions
2026
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VDR Q&A
Data-room-driven follow-up analysis · live transactions
2026
Evidence inputs
- Live transactions within the group; IFRS reporting basis.
- IFRS IAS 21 · Quality-of-earnings analysis · AI-assisted workflows
Transformation
- Codified IAS 21 currency translation, quality-of-earnings analysis and financial-risk matrices.
- Structured outputs for consistent review and board-ready decisions across deals.
- Used model assistance for first-pass reading and extraction while keeping every figure traceable and re-checkable.
Controls & assurance
- Standardised the method while preserving transaction-specific judgement.
- Kept numbers and conclusions under deterministic checks and reviewer authority.
Output
- Deployed a consistent analytical structure across live transaction work.
Business value
- Improves the speed, comparability and auditability of transaction decisions.
- Problem
Each transaction needed faster analysis without sacrificing consistency, traceability or reviewer judgement.
- Approach
Codified IAS 21 currency translation, quality-of-earnings analysis and financial-risk matrices.
- Outcome
Deployed a consistent analytical structure across live transaction work.
Improves the speed, comparability and auditability of transaction decisions.
Transformation route
- 01
Codified IAS 21 currency translation, quality-of-earnings analysis and financial-risk matrices.
- 02
Structured outputs for consistent review and board-ready decisions across deals.
- 03
Used model assistance for first-pass reading and extraction while keeping every figure traceable and re-checkable.
Decision log
- Standardised the method while preserving transaction-specific judgement.
- Kept numbers and conclusions under deterministic checks and reviewer authority.
This case study proves
- AI-assisted financial analysis AI & Automation Strong
- IFRS / UK GAAP Finance Strong
- M&A due diligence Finance Strong
What I learned
- Standardising the method improves speed without predetermining the answer.
Future improvements
- Build a redacted precedent library of worked examples and standard adjustments.