Inscribe Alternatives: Why Buyers Look Elsewhere and Who Fits
Six Inscribe alternatives, sorted by what a buyer needs: work after the flag, document mix, data terms, pricing and how edits get caught.
TL;DR: Resistant AI sells two products: Resistant Documents, a file forensics engine it says is trained on more than 200 million documents, and Resistant Transactions, a model layer for existing transaction monitoring. Four gaps can send a buyer elsewhere: they need the content checked as well as the file, a per-document price, the flagged case worked rather than scored, or monitoring on different rails. The right alternative depends on which reason is yours, and one common option, Ocrolus Detect, runs on Resistant AI's engine.
Sphinx wrote this list and appears first on it; the entries come from vendors' public pages, documentation and press read in November 2026, and the numbering is an order of fit for a buyer leaving or comparing Resistant AI, not a ranking of quality.
What you would be walking away from
Resistant Documents examines how a file was built rather than what it says. Resistant AI describes more than 500 checks per document across metadata, internal structure, fonts and pixel consistency, with results in under 20 seconds on PDFs and images. Because the engine does not need to recognize a document or read it, the same checks run on an unfamiliar form in any language, and that is a real advantage for a team taking files from many countries. Results come back as a verdict of Trusted, Warning or High Risk with indicator-level evidence, through a drag-and-drop interface or an API. Documents are also compared with each other to find reuse and template farms. All of those figures are the vendor's own.
Resistant Transactions is the second product. It adds more than 80 models to a rules-based monitoring system: supervised, link analysis, behavior change, threat propagation and clustering models, returning insights in under 200 milliseconds to a home-grown or vendor case manager. Replacing the existing system is not required. Experian, a strategic investor since July 2025, built on this work for Transaction Forensics, launched in April 2026 for UK businesses on Faster Payments, BACS and CHAPS. Experian's release cites a pilot with a 200% increase in APP fraud detection and an 80% reduction in false positives.
Why Resistant AI buyers shop around
Four reasons follow from what Resistant AI publishes about its products. Each comes from a design choice, and for some teams those same choices are the reason to stay.
- The content goes unread by design. Resistant AI's own wording is that it looks at how a document is built, not what it says, which protects privacy and removes the dependence on language. Teams that also need balances to reconcile or tax figures to follow the rules have to get that check somewhere else. Ocrolus's documentation makes the point directly: it pairs Resistant AI's forensics with its own math checks to surface manipulation it says file forensics alone cannot catch.
- The price does not suit lower volumes. Resistant AI says pricing depends on document volume and other factors, and asks buyers to get in touch. Marketplace listings show channel prices that do not match each other, so they are a poor guide, and a team checking a few hundred files a month will want a figure at its own volume.
- A verdict is not a finished case. Adaptive Decisioning can map signals to approve, escalate or reject under your policy, and that handles a lot of volume. A Warning on a certificate of incorporation in a KYB file still leaves someone to check the registry, compare the ownership documents and write the note.
- The monitoring fit is specific. Resistant Transactions adds detection signal, and the Experian version launched on UK payment rails. A team whose real backlog is alerts already raised may need investigation capacity more than another source of scores, and a team outside the UK should ask which counterparty data is available in its market.
One more thing to check before you switch: some products that look like alternatives are distribution for the same engine. Ocrolus Detect uses Resistant AI's forensic layer, and Experian offers Resistant Documents in Australia and New Zealand through its Ascend platform. Moving to either changes the workflow around the forensics more than the forensics themselves.
The options at a glance
Seven options, ordered by fit
The first five are for a buyer comparing Resistant Documents. The last two address Resistant Transactions, since no vendor here matches that pairing one for one.
1. Sphinx — Best for per-file forensics, with flagged files worked to a recommendation inside KYB and KYC
Sphinx Doc Fraud runs six checks: production method, timestamp trail, issuer matching, consistency, model artifacts and recycled patterns. A $30B+ US bank reported 91% of documents cleared and 17% more fraud caught. It costs $0.45 per document with no seats or platform fee, and you can test your own files free in the playground. Doc Fraud also runs as one agent inside the KYB and KYC workflow, where Sphinx's AI analysts log into your existing tools, gather evidence, apply your SOPs and produce a traceable recommendation. Your team retains final approval.
The fit is a team that wants the next step done as well as the score. On monitoring, Sphinx reviews alerts your system already raises.
The limits are real. With 1M documents processed, its pattern history is far smaller than the 200 million Resistant AI cites, which matters for serial fraud. It does not verify faces, connect to bank accounts or score payments in real time.
2. Inscribe — Best for lenders wanting forensic and content signals on financial documents
Inscribe combines forensic, network, semantic and perceptual analysis, which answers the first reason buyers look elsewhere: it reads what the document says as well as how it was made. The focus is on documents that move money, such as bank statements, pay stubs and tax forms, across underwriting and account opening. It also sells document parsing and credit insights, and its loan-focused page cites about 72 seconds per document.
The fit is a bank, credit union or fintech whose risk sits in financial documents and who would value parsing and fraud review from one vendor.
The limit follows from that focus. A team receiving a wide spread of unfamiliar international forms should test coverage on its own files, which the complimentary pilot allows. Pricing is custom, and we found no transaction monitoring product.
3. Fortiro — Best for lenders focused on income documents, especially in Australia
Fortiro analyzes the content, visual, metadata and digital forensic properties of a document and returns a fraud risk score, with an investigator view of the indicators on the page. Files go through drag and drop or an API, in around 30 seconds by its account, on payslips, bank statements, tax documents and invoices. Scoring and rules are configurable, so a team can tune alert rates itself. It says it does not retain documents beyond the initial processing period, and holds ISO 27001.
The fit is a lender, particularly an Australian one, whose main exposure is falsified income. Its recent public announcements center on that market.
The limit is commercial and geographic: products are sold on annual subscription, and buyers elsewhere should ask for evidence on their own country's issuers. Fortiro offers trials and proof of value for that purpose.
4. Ocrolus Detect — Best for US lenders already using Ocrolus for income and cash flow
Ocrolus Detect is listed here with a caveat: since October 2026 its forensic layer is Resistant AI's engine. Ocrolus adds math checks on supported documents, including W-2s, pay stubs, bank statements and many tax forms, and merges both sources into one Authenticity Status with reason codes. Underwriters can see results in the Ocrolus platform or inside Encompass, and fraud detection is now included for Ocrolus mortgage customers.
The fit is a lender that wants what Resistant AI does plus income math, in the place underwriters already work.
The limit is independence. If your reason for leaving is the forensic signal itself, this will not change it. Ocrolus's documentation also says serial fraud and template reuse detection is due in early 2027, so ask how that works today.
5. Doxis AI.dp (Klippa) — Best for capture pipelines handling invoices, receipts and images
Klippa, acquired by SER Group in March 2025, now sells its platform under the Doxis name. Doxis AI.dp extracts data from more than 100 document types and adds fraud checks in four groups: metadata, validity, visual and AI analysis. The validity checks compare line items, totals and VAT, and results connect to more than 200 systems. It describes itself as GDPR compliant on ISO-certified servers.
The fit is an operations team that needs extraction first and fraud checks alongside it, such as accounts payable or claims.
The limit for a dedicated fraud team is that the fraud component is one part of a document processing platform. Its documentation also states that Word and Excel files return an empty fraud output.
6. Hawk — Best for banks wanting an AI overlay on existing AML monitoring
Hawk's AML AI Overlay is the closest structural match to Resistant Transactions. It analyzes all transactions, flagged or not, with custom models, scores each for false-positive and suspicious-behavior likelihood, and returns explanations to your case manager. Hawk cites up to 70% fewer false positives and offers VPC, Hawk cloud and on-premise deployment. It also sells full monitoring and an AML Investigative Agent, so it can replace a legacy system instead.
The fit is a bank that wants overlay detection from a vendor that could also become its core system. Choosing Hawk does not rule out adding Sphinx later, since Sphinx works the alerts a monitoring system raises.
The limit is scope: we found no document forensics, so a team buying both Resistant products from one vendor would need a second one here.
7. ThetaRay — Best for institutions open to moving detection itself onto machine learning
Theta Monitor uses an ensemble of more than 20 algorithms, with unsupervised models that flag anomalies without labeled examples or thresholds. ThetaRay's Ray agent assembles and documents cases, and the company says Ray can layer over any detection engine and case manager. Its published path runs from overlay to parallel testing to full conversion, with rules retired only after side-by-side validation.
The fit is an institution with cross-border flows that sees an overlay as a first step, not the end state.
The limit is that the published path points toward replacing your rules. If you want a permanent overlay, confirm that in the contract discussion. We found no document forensics product either.
Questions that will settle it
A short test on your own files will tell you more than any vendor table, including this one. FinCEN's data gives a reason to include counterfeits in that test: in its 2024 analysis of mail theft-related check fraud, covering 15,417 BSA reports and more than $688 million in suspicious activity, 26 percent of stolen checks became templates for counterfeits and 44 percent were altered. FinCEN has also flagged rising reports of GenAI identity documents, so your sample should hold both edited originals and files generated from nothing.
- Which is your actual problem: missed fakes, cost per file, or analyst time after the flag? Each points to a different entry above.
- Does the forensic signal come from this vendor, or from someone on this list? Ask directly. It decides whether a switch changes detection at all.
- Does it check content as well as construction? Run a file that is structurally clean but whose numbers do not reconcile. Our guide on spotting a fake bank statement covers what each kind of tell catches, and the difference between document verification and fraud detection explains why IDV tools sit outside this list.
- What does a 50-file month cost, and a 50,000-file month? Ask for both, including every analysis type you would need.
- How does it find reuse across your submissions? Ask how large the comparison set is and whether it spans customers. Our piece on serial document reuse explains why this is a separate signal.
- Who works the file after a Warning? If the answer is your team, count those minutes in the total cost.
- For monitoring, do you need more detection or fewer alerts to investigate? Resistant Transactions, Hawk and ThetaRay address detection. Reducing false alerts often starts earlier, with tuning.
Frequently Asked Questions
What are the main alternatives to Resistant AI?
For document forensics, the main alternatives are Sphinx Doc Fraud, Inscribe, Fortiro and Doxis AI.dp, formerly Klippa. Ocrolus Detect is often listed too, but it uses Resistant AI's forensic engine. For transaction monitoring, Hawk's AML AI Overlay and ThetaRay's Theta Monitor do similar work to Resistant Transactions.
How much does Resistant AI cost?
Resistant AI does not publish a rate card and says pricing depends on document volume and other factors. Marketplace listings show channel prices that do not match each other, so ask for a quote at your volume.
Does Resistant AI do transaction monitoring?
Yes. Resistant Transactions adds more than 80 models to an existing rules-based monitoring system and returns insights to the case manager in under 200 milliseconds, by the company's account. It does not require replacing that system. Experian's Transaction Forensics, launched for UK businesses in April 2026, was developed with Resistant AI.
Is Ocrolus a Resistant AI alternative?
Only partly. Since October 2026, Ocrolus Detect has used Resistant AI's Documents engine for file-level forensics and added its own math checks on supported documents. Choosing Detect changes the workflow and adds content checks, but the forensic layer comes from the same engine.
Can Sphinx replace Resistant AI?
On documents, it can: Sphinx Doc Fraud examines the file and returns a verdict with evidence at $0.45 per document. Its pattern history is smaller, so test serial fraud on your own data. On monitoring, Sphinx does not replace Resistant Transactions, because it reviews alerts rather than generating them.
