Applied AI · Forensic Accounting · Portfolio Project
ForensAI — AP Fraud Detection
An AI-powered accounts payable fraud detection tool built for independent forensic accountants — the CFEs who currently run Benford's Law in Excel, write investigation reports in Word, and spend three billable days on work that should take ninety minutes.
The market problem — why existing tools don't work
Too expensive
Enterprise platforms
KPMG internal tools, Relativity, Bloomberg Law. Priced at $2,000–$10,000+/month. Built for Big 4 teams with full support departments, not independent CFEs billing at $250/hour.
Wrong focus
Document extraction tools
CounselPro, FraudFindr, Ocrolus. Excellent at OCR and bank statement parsing. Not built for AP ledger pattern analysis — Benford's Law, threshold splitting, shell company detection.
Not court-safe
General AI (ChatGPT / Claude)
Can describe patterns conversationally. No audit trail. No traceable evidence chain. Hallucination risk is disqualifying in any legal proceeding. No purpose-built tool can be replaced by a chatbot here.
Why AP vendor fraud — the MVP case type
Five fraud types were evaluated against four criteria: standardized input data, mathematically provable detection patterns, value per case, and domain expertise match.
Evaluated · Skipped
Expense reimbursement fraud
Common, but input data is messy — receipts, PDFs, unstructured attachments. Too much preprocessing for an MVP. Not the right first case.
Evaluated · Skipped
Payroll fraud
Requires HR system access and employee record integration. Ghost employees and rate manipulation need different data models entirely. Strong v2 candidate.
Champion · MVP case type
Vendor / AP fraud
Standardized CSV input from any accounting software. Mathematically provable patterns.$100k–$500k average exposure. Direct match to forensic accounting expertise. The only case type that wins on all four criteria.
Evaluated · Skipped
Financial statement fraud
Requires full auditor access across multiple ledgers and periods. Complexity and access requirements push this to v3 at the earliest.
Evaluated · Skipped
Embezzlement
Highly variable — cash skimming, check tampering, and asset misappropriation each require different detection logic. Broad but shallow for a first build.
Decision criteria
Four decisive factors
AP fraud wins on: standardized input (AP ledger CSV), defensible math (chi-square, exact match), high case value (tool pays for itself on first finding), and domain expertise alignment.
Positioning reframe
Sharpening from a broad AI pitch to a defensible niche — the move that changes who you sell to, how you reach them, and what they pay.
Old positioning
"AI-powered forensic accounting assistant for fraud investigators"
→
New positioning
"The AP vendor fraud tool for independent forensic accountants who are done doing this in Excel"
Old promise
"Detect fraud with AI"
→
New promise
"Court-ready AP fraud findings in 90 minutes, not 3 days"
Old customer
"Fraud investigators" — too broad to reach, too broad to close
→
New customer
Solo CFEs and small forensic firms (2–8 people), billing $200–$400/hr, 4–12 AP fraud cases per year
What ForensAI analyzes
Four mathematically provable detection methods. Every finding links to a specific transaction ID — making output court-ready, not just conversational.
01
Benford's Law
In natural financial data, leading digit 1 appears ~30% of the time. Fabricated amounts distort this. ForensAI runs a chi-square test (df=8) per vendor and flags where p<0.05.
02
Duplicate invoice detection
Groups by vendor + exact amount. Flags any combination appearing more than once. Catches intentional resubmission with suffix modification (APX-7741 → APX-7741R).
03
Threshold splitting
Detects invoices clustering within 5% below approval limits ($5k, $10k, $25k, $50k). Three or more invoices in this band from one vendor triggers a high-severity flag.
04
Shell company scoring
Scores vendors 0–100 on: PO Box address (+40), missing EIN (+30), new vendor receiving >$10k in first 90 days (+25), large total exposure (+5). Scores ≥50 are flagged.
Ideal customer profile
Independent CFEs and small forensic firms currently doing AP fraud analysis in Microsoft Excel and writing court reports in Word.
CFE
Certified Fraud Examiner credential
1–8
People in the firm
$250/hr
Average billing rate
Excel
Current AP analysis tool
4–12
AP fraud cases per year
Word
Current report writing tool
"I spend two days building the same Benford's formula in Excel for every new client. Then another day writing a report that's 80% identical to the last one. That's three billable days I'm writing off because I can't charge a client $750/hour for Excel work."
Defensible moat
What competitors cannot copy — and why the position holds over time.
Founder insight
You are the customer
The forensic accounting and ML background gives domain credibility no generalist AI company can replicate. You know which columns matter, what a real Benford deviation looks like, how a court report needs to read. Building for yourself first is the strongest possible product-market fit signal.
Technical moat
Evidence chain by default
Every finding links to a specific transaction ID from the source file. This makes ForensAI output usable in a legal proceeding — and impossible to produce from a general AI chatbot. That's the feature that converts a skeptical CFE who has been burned by hallucinations before.
Scope moat
AP-only, done right
"We do AP vendor fraud and we do it better than anyone" beats "we do everything" when a CFE is about to stake their professional reputation on a court submission. Breadth signals fragility. Depth signals trust. Being narrow is the moat.
Pricing moat
Right-sized for independents
$149–$249/month sits below FraudFindr's $449 floor. For a CFE billing $250/hour, ForensAI pays for itself if it saves one hour per case — and it saves ten. That's a one-line sales pitch, not a feature list.