DD Tech Lab
Technical methods for audit, fraud-examination, and due-diligence work. Stochastic processes, knowledge graphs, platform-based DD, and the workpaper-defensible analytics behind them.
What DD Tech Lab Is
DD Tech Lab is the technical-methods publication of Sheepdog Prosperity Partners.
It is written for audit, fraud-examination, and due-diligence practitioners working on engagements where descriptive statistics, ratio analysis, and first-order Benford tests no longer carry enough signal. We focus on the methods that produce defensible findings in restatement work, related-party network discovery, period-end transaction-timing diagnostics, and long-memory fraud detection.
Every article walks the technique end-to-end: the math, the audit standards it speaks to, runnable code on synthetic data, the workpaper outputs a PCAOB inspector would expect, and full attribution to the published prosecutions where the pattern first surfaced.
Companion code for every article lives in the public repository Sheepdog-Prosperity-Partners-LLC/dd-tech-lab-companions on GitHub. Each script is deterministic under seed=42 and reproduces the worked example in the article exactly.
Who It Is For
- Audit seniors, managers, and directors
- Forensic-accounting practitioners (private and government)
- Financial DD officers and operational-DD analysts
- Internal audit at financial institutions
- Risk officers at banks, asset managers, and regulated entities
- Counsel and transaction stakeholders working alongside the above
What We Cover
Stochastic processes and Markov chains
Excel-based fraud detection
Knowledge graphs and Neo4j
Palantir Foundry for enterprise DD
Published prosecutions and pattern recognition
Workpaper-defensible methodology under PCAOB AS 2401 / AS 1215
Latest Articles
Schema Design for Sanctions Screening: Modeling the OFAC SDN List as a Knowledge Graph for Real-Time DD Lookups
The U.S. Treasury’s Office of Foreign Assets Control publishes the Specially Designated Nationals and Blocked Persons (SDN) list as XML and CSV files containing tens of thousands of entries: individuals, entities, vessels, and aircraft subject to sanctions. The naïve screening
Linear Regression for Outlier Detection in Excel: Building the Standardized-Residual Workpaper for Expense-Account Analytics
When utility costs scale with square footage, sales commissions track revenue, or freight charges follow tonnage shipped, the auditor needs more than ratio analysis to spot anomalies. PCAOB AS…
Cypher Patterns for Transaction-Graph Anomaly Detection: Round-Tripping, Layering, and the Cycle-Structure Diagnostic
Article 001 used a graph to model ownership — relatively static data, where the diagnostic question is path traversal under a regulatory threshold. Transaction graphs are different in three operational respects. They are high-velocity (hundreds of thousands of edges per quarter
From Beneficial-Ownership Lists to Cypher: A Practical Knowledge-Graph Setup for Due Diligence
An ownership chain that nests beyond three levels is the point at which the DD analyst’s tooling has to change. This article walks the schema, loading, and Cypher queries for graph-based beneficial-ownership analysis.
Modeling Journal-Entry Sequences in Production: Encoding the Chart of Accounts, Selecting a Baseline, and Handling the Close-Cycle Shift
The first-order Markov apparatus from Article 001 worked cleanly on a 5-state synthetic dataset with 1,000 transitions and a known baseline. Production audit work breaks each of those assumptions.…
Same-Same-Different in Excel: Detecting Identical-Value-Different-Date Patterns Across Vendor Files
Duplicate disbursements erode 0.8–1.1% of organizational spending annually (ACFE, *Report to the Nations*, 2024 ed., p. 47). Excel’s COUNTIFS function detects these control failures through systematic pairwise comparison: records matching on vendor and invoice identifiers but
First-Order Markov Modeling for Transaction-Stream Analysis in Audit
Financial-statement auditors and DD analysts work with sequences: journal-entry posting streams, period-over-period account-reconciliation states, customer-vendor transaction chains, adjusting-entry…
Benford’s Law in Excel: Setting Up the First-Digit Test for Mass-Transaction Screening
The first-digit distribution of naturally occurring numerical populations follows a logarithmic pattern. Auditors leverage this pattern to screen transaction datasets for anomalies. The…
Frameworks & Tools
Companion code on GitHub
github.com/Sheepdog-Prosperity-Partners-LLC/dd-tech-lab-companions
Workpaper Templates
Coming soon
If you face a forensic-accounting question, a complex DD scope, or a methodological challenge a single-period analytical procedure cannot answer, start with a Discovery call.