epstein-forensic-finance

Methodology

⚠️ This analysis does not constitute an audit, examination, or review performed in accordance with GAAS, GAGAS, or AICPA SSFS No. 1. All dollar figures are (Unverified) automated extractions. Verification tiers are analyst assessments, not audit opinions. See COMPLIANCE.md for full professional standards discussion.

Overview

This document describes the extraction pipeline I built to identify and quantify financial transactions from 1,476,377 files across 19 DOJ EFTA datasets. The pipeline produced a publication ledger of 6,310 unique transactions totaling $2,146,000,000 (Unverified) across 10 payment types. The auditable subtotal (Tiers 1–3) reaches $1,960,600,000 — 104.4% of the $1.878 billion FinCEN SAR benchmark.

I wrote all extraction code, designed the database schema, and performed the forensic analysis as a solo effort, with AI assistance (Claude, Anthropic) for development acceleration and quality assurance. The full database architecture is documented in SCHEMA.md.

Data Sources

DOJ EFTA Corpus

Metric Value
Total files indexed 1,476,377 + 503,154 media
Datasets 19 (DS1-12 + DS98-104)
Extracted text records 1.48M+
Entity extraction (spaCy NLP) 11,438,106 entities
Unique persons identified 734,122
Database size 8GB SQLite
Relational tables 36

Structured Database Tables Used in Wire Extraction

Table Rows Description Quality
verified_wires 185 Court-exhibit authenticated wire transfers (Alfano exhibits, DB-SDNY) Gold standard — dates, bates numbers, exhibits
fund_flows_audited 7,355 5-tier classified financial flows (PROVEN/STRONG/MODERATE/WEAK/VERY_WEAK) Audited — entity-pair matching, context scoring
fund_flows 16,677 Raw financial flows before classification Unfiltered — contains balances, noise
financial_hits 35,375 Keyword-flagged financial references Mixed — wire_transfer category filtered
trust_transfers Variable Trust-to-trust transfer records Structured — trust administration records

FinCEN SAR Benchmark

Bank SAR Amount Source
JPMorgan Chase $1,100,000,000 U.S. Senate Permanent Subcommittee on Investigations
Deutsche Bank $400,000,000 FinCEN SAR filings
BNY Mellon $378,000,000 FinCEN SAR filings
Combined $1,878,000,000  

Sources: NYDFS Consent Order (2020); JPMorgan USVI Settlement (2023)

5-Axis Forensic Scoring

Every financial record extracted from the EFTA corpus is independently scored across five axes before entering the wire extraction pipeline:

Axis Weight What It Measures
Context Language ×3 Transaction vocabulary (wire, routing, SWIFT) vs. noise (lawsuit, net worth)
Amount Specificity ×1 $2,473,891.55 scores high; $10,000,000.00 exactly scores low
Date Presence ×1 Full date > year only > no date
Entity Quality ×2 28 known banks, 64 financial actors, 71+ garbage entity exclusions
Source Document Type ×1 Financial/spreadsheet > email > general document

Classification Tiers:

Validation: v6.2 spot-check achieved 93% accuracy on top-30 PROVEN transactions (28/30), with 0% balance contamination (down from 47% in v5).

Extraction Pipeline

Phase-by-Phase Log

All amounts are (Unverified) automated extractions.

Phase Description Amount Running Total % SAR Quality Gate
v2 Core OCR Extraction +$1,204,000,000 $1,204,000,000 64.1% Amount-unique dedup
BF adj Butterfly Trust Correction -$63,000,000 $1,141,000,000 60.8% Manual review
v3.2 7-Layer Wire Expansion +$237,452,186 $1,378,452,186 73.4% 7-layer proximity filter
14.5 Known Entity Fund Flows +$90,936,712 $1,469,388,898 78.2% Entity name match
14.5B Balance Contamination Fix -$53,766,217 $1,415,622,681 75.4% BUG FIX
15E Fund Flows Real Wires +$14,000,000 $1,429,622,681 76.1% Wire keyword required
15F Redaction Recovery +$368,170 $1,429,990,851 76.1% Proximity scan
16.1 Transaction-Line Parser +$78,547,827 $1,508,538,678 80.3% Format validation
16.2 Round-Wire Extractor +$4,975,350 $1,513,514,028 80.6% Round-amount + context
17 Trust Transfers + DS8 +$12,720,752 $1,526,234,780 81.3% Table structure
18 Full Category Sweep +$3,854,313 $1,530,089,093 81.5% Category filter
19 Audited PROVEN Fix +$59,524,629 $1,589,613,722 84.6% BUG FIX
20A Verified Wires (amount-new) +$53,093,926 $1,642,707,648 87.5% Court-exhibit verified
21A/B STRONG/MODERATE New Amounts +$9,641,465 $1,652,349,113 88.0% Wire indicator + tier
23 Date-Aware Wire Census +$191,304,691 $1,843,653,804 98.2% Date dedup
24 Above-Cap Verified Wires +$120,575,938 $1,964,229,742 104.6% Court-exhibit verified
25 Date Recovery from Context +75 dates, $0 Δ $1,964,229,742 104.6% Source context parsing, 0 collisions
5I Entity Resolution & Bank Expansion 481 wires, 228 entities, 14 banks $973,392,414 (entity-resolved) — Entity classification + custodian audit
5J Multi-Bank Statement Parser +1,202 transactions from 13 banks +$430K verified statements — Statement-level verification
5K Payment Type Expansion CHIPS, SWIFT, checks, bank statements 10 payment types — Beyond wire transfers
5L Publication Ledger Assembly 6,310 unique, four-tier GAGAS $2,146,000,000 104.4% T1–T3 = $1,960,600,000

Deduplication Strategy

Three-Stage Dedup Evolution

Stage 1 (Phases v2-20): Amount-Only Dedup Each unique dollar amount was counted once, regardless of entity pairs or dates. Maximally conservative but destroyed legitimate repeat wires.

Stage 2 (Phase 23): Date-Aware Census I added date as a dedup dimension: (amount, entity_from, entity_to, date). This recovered 95 same-amount different-date wires worth $189M.

Stage 3 (Phase 24): Verified-Tier Cap Removal I removed the $10M safety cap for court-exhibit verified wires. All 8 above-cap entries had exhibit numbers, bates stamps, dates, and named counterparties.

Stage 4 (Phase 25): Date Recovery from Source Context I queried source database tables (fund_flows_audited.date_ref, fund_flows_audited.context_snippet, verified_wires.date, fund_flows.context) to recover dates for 75 previously undated wires — improving date coverage from 31.9% to 51.6%. Zero collisions with existing dated entries confirmed that all undated wires were genuinely unique, validating the earlier dedup methodology. Credit: u/miraculum_one (Reddit) identified that dates were present in context fields.

Entity Classification

Type Description Examples
EPSTEIN ENTITY Controlled trusts, shells, personal accounts Southern Trust, Jeepers Inc., Plan D LLC
EXTERNAL PARTY Investors, beneficiaries, counterparties Leon Black, Sotheby’s, Tudor Futures
BANK/CUSTODIAN Financial institutions intermediating transfers Deutsche Bank, JPMorgan, HSBC
UNKNOWN Unclassifiable short strings or OCR artifacts —

Entity normalization handled 178+ OCR variants (e.g., “JJEFFREY EPSTEIN” → “Jeffrey Epstein”, “GHISLA1NE MAXWELL” → “Ghislaine Maxwell”).

Contamination Bugs Found & Fixed

Nine data quality issues were identified and corrected during the pipeline:

# Phase Bug Impact What Went Wrong How Fixed
1 14.5B Balance Contamination -$53.8M Running statement balances extracted as wire amounts is_balance flag; excluded from subsequent phases
2 16.1 Brokerage Noise -$155M Securities language mixed with wire data BROKERAGE_NOISE regex filter
3 19 Self-Dedup Bug +$59.5M Table checking new entries against itself Fixed dedup reference to prior-phase JSON only
4 20D Amount-Only Dedup +$115M Same amount, different entity pairs collapsed Entity-pair aware dedup (created Bug 5)
5 22 Chain-Hop Inflation -$311M $10M through 4 entities counted as $40M INTERNAL entity taxonomy; excluded internal hops
6 22 Cross-Table Name Duplication Risk flagged Same wire as “Leon & Debra Black” and “LEON_BLACK” Three-tier confidence framework
7 22 Statement Noise as Entities -$59M “Balance Transfers”, “Gift Cards” as wire entities STATEMENT_NOISE filter; 188 entries removed
8 23 Date-Blind Dedup +$189M Four $10M wires on different dates counted as one Date-aware master ledger composite key
9 24 Arbitrary Cap + Custodian +$120.6M / -$113.4M $10M cap excluded verified wires; “(DBAGNY)” misclassified Cap removed for verified tier; custodian audit

Limitations

  1. Not a professional audit: This is automated text extraction, not a review under GAAS, GAGAS, or AICPA SSFS No. 1.
  2. Solo practitioner: No segregation of duties or independent partner review. AI-assisted QA provides partial mitigation.
  3. OCR quality varies: Some amounts may be misread from scan artifacts (e.g., $1M vs $10M).
  4. Entity normalization is imperfect: Some entities may be misidentified or incorrectly merged across OCR variants.
  5. Sealed documents are inaccessible: Court-sealed records cannot be extracted. The dollar value of sealed financial activity is unknown.
  6. SAR vs. completed transactions: The SAR benchmark counts attempted suspicious transactions; I extract completed wire transfers.
  7. Destroyed records: Pre-retention period records may have been destroyed. The dollar value is unquantifiable from the EFTA corpus.
  8. Cross-table name overlap: Same wire may appear with different entity formatting across database tables.
  9. Chain-hop filtering trade-offs: Some legitimate multi-step transactions may have been excluded by the internal entity filter.
  10. Date coverage: 350 of 481 master ledger entries have dates (72.8%) after Phase 5I entity resolution. The remaining 131 undated entries carry higher duplication risk, though zero-collision validation confirmed all undated wires were genuinely unique.

Tools & Infrastructure