Used by forensic accountants, litigation support teams & fraud examiners
4.8/5Trusted by 10,000+ finance teams
Forensic accounting software built on extraction, normalization, and tracing
Extract, reconcile, and trace every transaction across bank statements — with transfer detection, flow-of-funds analysis, and check-to-statement matching. One normalized dataset with page-level provenance.
4.8/5Trusted by 10,000+ finance teamsMatter evidence
Operating · Q1
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Checks & deposit slips
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Brokerage statement
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Overall progress0%
0 of 3 complete
Evidence in → structured ledger → transfer & flow analysis → case-ready export
Built for forensic work — not adapted from a bookkeeping tool. Financial investigations overview
Drag & Drop Financial Documents Here
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Built for investigation-grade work
< 1 day
data prep on most engagements
6+ banks
normalized into a single dataset
Every row
traced back to its source PDF page
The first week of a forensic engagement shouldn't be spent building a spreadsheet
What eats your time before analysis even starts
- •Six months of statements from four banks — different formats, some scanned, some native PDF.
- •Hundreds of check images with no direct link to the statement lines they represent.
- •Funds moving through three LLCs and two personal accounts, tracked across manual tabs.
- •Two analysts categorize the same transactions differently — now the numbers don't reconcile.
- •A gap in October's records surfaces on the day the report is due.
What changes when you use DocuClipper
- All statements extracted and normalized into one consistent dataset within hours of upload.
- Checks and deposit slips matched to statement lines automatically — handwritten included.
- Flow-of-funds views built from your data, not assembled by hand in Excel.
- Categorization rules saved and applied consistently across every analyst and matter.
- Missing periods flagged on intake — before they become a problem at the finish line.
From raw documents to investigation-ready data
Three steps. Same day.
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Drop in your documents
Bank statements, card statements, check images, deposit slips — any mix of formats, any number of institutions. Upload a single matter or a high-volume batch.
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DocuClipper does the data work
Transactions are extracted, normalized to a consistent schema, and matched across accounts. Checks are linked to statement lines. Missing periods are flagged. You get clean rows, not raw PDFs.
Analyze, trace, and export
Categorize activity, follow the flow of funds across entities, review flagged items, and export an Excel-ready dataset where every row points back to its source page.
Technical capabilities for forensic bank records
How the product behaves under the hood — inventory, classification, transfer logic, funds views, and documentary matching — beyond generic speed-and-accuracy marketing copy.
File inventory
Treat the upload set like evidence custody: know which institution–account–period combinations are represented, which months never arrived, and where filenames or date ranges disagree with the transaction data inside.
- Roll up coverage by bank, account mask, and statement period so missing months surface before review hours are burned.
- Compare opening/closing balances and statement date spans across PDFs to catch overlapping files, duplicate months, or wrong-account uploads.
- Pair with reconciliation signals so statements that fail balance tie-outs are visible in the same inventory view as missing documents.
What you are indexing
institution, product (checking / card / LOC) account_last4, currency, statement_period_end file_hash, ingest_timestamp, page_count balance_begin, balance_end, reconcile_status
Categorization
Apply a repeatable taxonomy across the normalized ledger: keyword and pattern rules, amount thresholds, and counterparty-driven tags — so two analysts do not silently diverge on how payroll, transfers, or cash deposits are labeled.
- Layer rules (description contains, regex, MCC text, amount bands) and re-run them when new statements land.
- Keep category, rule name, and match confidence on the row for workpaper defensibility.
- Export category columns alongside raw descriptions so Excel models and expert reports stay aligned with the extraction.
Typical rule inputs
description_raw, normalized_payee amount, debit_credit, transaction_type category, rule_id, applied_at
Transfer detection
Identify internal movements across accounts you already have: paired credits and debits within configurable date and amount tolerances, including ACH references, wires, and card payment patterns — so internal noise is separated from third-party spend.
- Score candidate pairs using amount match, date proximity, memo tokens, and direction (out from account A, in to account B).
- Highlight one-sided outflows — funds that leave a known account with no inbound counterpart in the dataset — for follow-up discovery on undisclosed institutions.
- Let reviewers accept, split, or reject pairs so the final transfer table matches your professional judgment.
Pairing heuristics (examples)
|amount_A + amount_B| ≤ tolerance |date_A − date_B| ≤ N business days memo_token_overlap OR reference_id_match flag: unmatched_outflow / possible_external_account
Flow of funds
Move from flat transaction grids to structured views of how cash moved among entities and accounts: aggregate by period, counterparty cluster, or transfer chain so you can explain the story without rebuilding pivot logic for every engagement.
- Slice inflows, outflows, and net by month, account, or entity label once the schema is normalized across banks.
- Relate categorized spend and transfer pairs to high-level summaries attorneys can follow in conference or on the stand.
- Drill from any summary back to underlying rows that still carry PDF page pointers for traceability.
Views built on normalized data
monthly_net_by_account top_counterparties_by_category transfer_edges(A→B, amount, date) source_page_index per transaction row
Matching checks and deposit slips to bank transactions
Ingest front/back check images and deposit tickets through OCR, extract check number, date, amount, payee/payer fields, and link each image record to the bank-reported line when amounts, dates, and reference data align.
- Handle scanned and smartphone photos, including many handwritten memo lines, without manual keying of every field.
- Propose matches to statement lines using amount equality (within tolerance), date windows, and check-number text when present.
- Keep the image artifact associated with the matched transaction so exports and exhibits preserve the documentary trail.
Linked artifact metadata
check_image_id, check_number, amount matched_transaction_id, match_score bank_line_date, bank_line_description
Reconciliation, exports & matter-scale processing
Controls that sit alongside the core engine — so the dataset you analyze is balanced, traceable, and consistent across staff.
Statement-level reconciliation
Roll extracted transaction totals back to opening and closing balances from the PDF so you know which files reconciled cleanly before you rely on them in a report.
Undisclosed-account signal from one-sided flows
Surface outflows that never pair to an inflow inside your workspace — a short list of transfers to investigate or to support additional document requests.
Investigation-oriented summaries
Cash-flow trends, counterparty rollups, and timeline-oriented cuts that sit on top of the same normalized schema as the technical modules above.
Page-anchored exports
Every exported row retains a pointer to the source PDF page so workpapers and exhibits answer “where did this number come from” without ad-hoc screenshots.
Multi-bank schema normalization
Different column layouts and date formats land in one consistent transaction model so filters, rules, and transfer logic run once across the whole matter.
Batch throughput for large productions
Upload high page counts across many accounts; processing is built for litigation-scale volumes rather than one-off conversions.
Works across every type of financial investigation
Civil, criminal, regulatory, or internal — if banking records are part of the evidence, DocuClipper shortens the path to answers.
- Forensic investigations
- Financial crime & fraud tracing
- Bankruptcy & restructuring
- Expert witness & litigation support
- Family law & lifestyle analysis
- Transaction advisory & M&A diligence
- Business valuation support
- Embezzlement & misappropriation
- Insurance and claims support
- Regulatory & program integrity reviews
DocuClipper vs. doing it manually
The difference shows up on day one of every engagement.
| Feature | DocuClipper | Manual / generic tools |
|---|---|---|
| Data intake time per engagement | Under a day for most matters | 3–5 days of manual entry |
| Statement formats supported | PDF, scanned images, native bank exports | Whatever the analyst can copy-paste |
| Cross-account, multi-entity tracing | Normalized schema + flow-of-funds views | Fragile cross-tab spreadsheets |
| Missing period / gap detection | Automatic flags before analysis starts | Often discovered late — or not at all |
| Categorization consistency | Saved rules applied across all matters | Each analyst re-labels from scratch |
| Deliverable traceability | Each row linked to source PDF page | Screenshots, notes, and hope |
| Check & deposit slip linkage | OCR + proposed match to statement line | Side-by-side PDFs and manual pairing |
Go deeper on the capabilities that matter most
Dedicated pages for each subsystem — inventory and analysis through tracing, categorization, checks, and flow of funds.
Bank statement analysis
Coverage, reconciliation, and review-oriented cuts.
Transaction categorization
Rules, tags, and consistent labels across matters.
Transaction tracing
Pairing logic and line-level trails.
Flow of funds
Aggregated movement across accounts and entities.
Check extraction
OCR from check and slip images into structured fields.
"Data preparation is usually handled in less than a day, which lets me put my energy into the actual analysis."
Forensic CPA · Independent litigation support practice
"DocuClipper lets us do a rapid assessment of scope on day one — before we've committed to a billing estimate."
Managing Director · Regional forensic accounting firm
Common questions from forensic teams
Straight answers on data intake, multi-bank normalization, checks, and exports.
Most matters are ready for analysis the same day. Upload your PDFs — bank statements, card statements, check images — and DocuClipper returns structured, normalized transactions. The data prep that used to take days now takes hours or less.
Yes. DocuClipper normalizes transactions from different institutions into a consistent schema — same columns, same date format, same amount fields — so you can analyze everything in one place instead of juggling six different spreadsheets.
Upload check and deposit-slip images alongside your statements. DocuClipper extracts the key fields and links each check to its corresponding statement line — including handwritten checks — so you can reconcile without manual matching.
Yes. Transactions where funds leave an account with no visible receiving side are automatically flagged as possible transfers to undisclosed accounts. That gives you a prioritized list to present when requesting additional documents.
Export to Excel or CSV with clean fields. Every transaction includes a reference back to the source PDF page, so your workpapers and exhibits are fully traceable. Plug the output into your firm’s existing report templates.
No. DocuClipper works as well for a straightforward embezzlement case with two bank accounts as it does for a multi-entity receivership with dozens. The speed benefit shows up immediately regardless of matter size.
The platform is built for the same document-heavy workflows you see across financial crime investigations, bankruptcy and restructuring, expert witness preparation, family law financial analysis, transaction advisory, and valuation work—any engagement where you need structured transactions, transfer tracing, and exhibit-ready exports from bank and card statements.