M&A Capital Protection & High-Stakes Due Diligence
Protecting $127M in committed capital for a Diversified GCC Conglomerate
SAR 15M unrecorded exposure identified — $127M capital defended — Acquisition terms restructured prior to close
Names, identifiers, and proprietary facts are abstracted where required. The underlying operational patterns and outcomes remain faithful to the real engagement.
How the engagement unfolded.
A diversified GCC conglomerate was in the final stages of a major acquisition within the hospitality and F&B sector. The deal involved a committed capital of $127M, but the existing due diligence relied on standard seller disclosures and limited sampling.
As Group Director of Internal Audit, my mandate was to perform a final layer of independent operational and financial due diligence to validate the target's internal control environment and uncover any contingent liabilities.
I deployed a full-population forensic review across the target's supplier agreements, lease commitments, and litigation filings. Instead of relying on the seller's curated disclosure schedule, we indexed and cross-matched every active contract clause against historical claims and regulatory filings.
The audit uncovered SAR 15M in previously undisclosed litigation exposure and significant regulatory non-compliance issues. This discovery provided the executive team with critical leverage to renegotiate deal terms, protecting the entire $127M in capital from post-acquisition shocks.
Standard M&A due diligence relies on sampled vendor files and seller warranties, which routinely miss off-balance-sheet exposures. Running full-population contract forensics defends acquisition EBITDA before capital is committed.
1. Benford's AP Anomaly Engine (Python)
Audit Purpose: Scans accounts payable (AP) journal data to identify statistical deviation from Benford's distribution, signaling potential invoice splitting under delegation of authority (DoA) thresholds.
import pandas as pd
import numpy as np
def run_benford_audit(df_ap_transactions, amount_col='amount'):
"""
Analyzes transaction distribution against Benford's Law.
Flags vendors/employees exhibiting high chi-square deviation.
"""
# 1. Extract the first digit (ignoring zeros, negatives, and decimals)
df = df_ap_transactions[df_ap_transactions[amount_col] >= 1.0].copy()
df['first_digit'] = df[amount_col].apply(lambda x: int(str(abs(x)).replace('.', '').lstrip('0')[0]))
# 2. Calculate actual frequencies
digit_counts = df['first_digit'].value_counts().reindex(range(1, 10), fill_value=0)
total_count = len(df)
# 3. Calculate Benford expected frequencies
benford_probs = {d: np.log10(1 + 1/d) for d in range(1, 10)}
expected_counts = {d: benford_probs[d] * total_count for d in range(1, 10)}
# 4. Perform Chi-Square Goodness-of-Fit test
chi_square_stats = {}
for d in range(1, 10):
observed = digit_counts[d]
expected = expected_counts[d]
chi_sq_val = ((observed - expected) ** 2) / expected
chi_square_stats[d] = {
'observed': observed,
'expected': round(expected, 2),
'deviation': round(observed - expected, 2),
'chi_sq': round(chi_sq_val, 4)
}
total_chi_sq = sum(c['chi_sq'] for c in chi_square_stats.values())
critical_value_99 = 20.09 # df=8, p=0.01
return {
'total_chi_sq': round(total_chi_sq, 2),
'audit_verdict': 'SIGNIFICANT ANOMALY DETECTED' if total_chi_sq > critical_value_99 else 'NORMAL DISTRIBUTION',
'details': chi_square_stats
}By running this engine daily, we transition from examining a tiny 5% random audit sample to a complete 100% population scan, instantly flagging invoice splitting (e.g. breaking a single AED 100K transaction into three separate AED 33K invoices to bypass Director-level approval).
All client names, employer names, and specific figures have been anonymised in accordance with confidentiality obligations and professional standards.