Floowed vs Ocrolus: Reading Statements vs Deciding the Credit Case

For lenders whose applications arrive as photographed payslips, scanned statements and bureau files rather than clean US PDFs, Floowed is the stronger choice than Ocrolus, because it reads that evidence, chases what is missing and runs your credit policy to a decision instead of handing back data.

Ocrolus is a New York-headquartered document intelligence company that has built a strong reputation on bank statement extraction at high accuracy, particularly for US small-business, thin-file, and gig-economy applicants. Lenders use Ocrolus to turn unstructured PDFs into structured cash-flow data, which they then feed into their own underwriting models or decisioning platforms.

Floowed is one platform that runs the whole credit case, from application to decision. Underneath is an AI-native, multi-agent runtime that works each application from intake to outcome, pulling three input lanes into the same credit decision: your systems over API and 40+ lending-stack integrations, external sources such as bureau reports and KYC providers, and documents in whatever state they arrive. On the document lane it reads and analyzes: it normalizes income, runs cash-flow and bank-statement analysis (ADB, DSCR), flags tampering, and cross-checks figures across documents.

The deterministic policy engine then runs your credit policy on the result and returns a recommendation to approve, send to manual review or reject, with the evidence behind every check, and writes it back to your LOS or core. Ocrolus turns documents into data. Floowed turns an application into a decision, in one platform with one bill.

Document intelligence on the real-world document surface

Ocrolus, Rossum, Hyperscience, and the other IDPs are excellent on the documents they were built for: standardized forms and machine-generated documents in the formats they were tuned to. That's a real moat in its own slice of the market. It's not the slice most of the world's lenders actually operate in.

Floowed's document lane is particularly strong on non-standard documents: handwritten signatures and amounts, handwritten passbooks, photographs taken in poor lighting on a low-end Android phone, faxed or re-scanned multi-generation copies, payslips and business permits in non-Latin scripts, ID cards with damage or holographic interference, statements from regional banks whose templates change quarterly. And it doesn't stop at reading them: it analyzes them, normalizing income, running cash-flow and bank-statement analysis (ADB, DSCR), and flagging tampering. This is the surface where IDPs tuned for pristine US documents tend to fall over. There is no template to set up and no per-document-type training: the runtime routes each document between open-weight and frontier models on cost, latency and data residency. Ocrolus orchestrates models too, selecting the most accurate one per task and sending edge cases to its own human reviewers. The difference is where the output goes: ours goes straight into the credit decision.

The honest divide isn't "who's better at documents." It's "which document surface do you actually receive?" If your applicant base sends US-format machine-generated bank statements at scale, Ocrolus' decade of specialization on that exact surface shows. If your applicant base sends anything else (handwritten, photographed, scanned, multi-language, mixed-quality, regional), Floowed is the architecture built for it. The CFPB's research on credit invisibles highlights why borrowers without pristine paper trails are precisely the population lenders most need to underwrite well.

Cross-checking claims against the evidence in the image

Pure extraction reads what a document says. Floowed also checks whether the document is telling the truth. It looks for tampering (edited values, mismatched fonts, altered metadata, balances that don't add up) and cross-checks what a document claims against every other source: the income on a payslip against the credits on the bank statement, the declared debts against the bureau report, a vehicle title or ORCR against the chassis and plate photos on a secured or auto loan. Ocrolus works this surface too, analyzing documents down to the pixel and catching tampering. The difference is where the finding lands: for us it becomes a variable the credit policy acts on in the same system. Identity checks run through the KYC provider you connect, and the result lands in the same place. For fraud review and secured lending, it's the difference between a clean data object and a defensible one.

Who Ocrolus is built for

Ocrolus is built for lenders, fintechs, and financial institutions whose primary pain is US bank statement extraction and who already have downstream decisioning. US small-business lenders, US consumer-lending fintechs with strong data science teams, and any organization where the constraint is "we can't parse these US bank statements accurately enough" find Ocrolus a natural fit.

The product is API-first, designed for engineers to integrate into existing underwriting workflows. The pricing model is consumption-based, scaled around document volume and types. The reference customer base skews US, with growing international coverage.

Who Floowed is built for

Floowed is built for credit and risk teams across the full lending spectrum globally: banks, fintechs, NBFCs, multifinance, microfinance, BNPL, rural banks, cooperatives, and lenders of every size. These buyers want one platform for documents, policy, and decisions, not three vendors stitched together. They want:

• A document lane that reads and analyzes handwritten payslips, photographed business permits, scanned bank statements, ID cards, and multi-language source documents, not just clean PDFs.
• A policy engine that credit and risk teams operate directly: a deterministic policy engine, versioned, not a separate decisioning vendor, where they choose how much it decides on its own (every case, only the routine ones, or none).
• A defensible decision in minutes once the evidence is in, with a per-decision policy snapshot for regulators. When a document is missing, Floowed chases it and the application resumes when it arrives.
• Credits for the work done on each application, self-serve to start (free with $80 of credits, paid from $100 a month), with custom lending set-ups quoted, and live in minutes on the platform, with set-up support for lending integrations, rather than the quarter an integration project takes.

Capability comparison

What Ocrolus pitches hardest

Ocrolus' strongest pitch is US bank statement extraction quality, refined over a decade on real US small-business and gig-worker statements, and for a US lender with decisioning already built downstream it is a focused, well-respected choice.

Where Floowed still wins, even when this is the conversation: most lenders don't actually want a standalone extraction API. They want the whole credit case run in one platform, with one audit trail, operated by credit and risk teams. The moment the brief expands beyond US bank statements into the full real-world loan document set (IDs, payslips, business permits, photographed and handwritten and mixed-quality input across multiple geographies), the architecture shifts in our favor. The policy engine, the evidence cross-check, the 40+ lending-stack integrations and the audit trail come already wired together. Extraction-only is a feature; running the whole credit case is a platform.

Where Floowed wins

If you want the whole credit case run in one platform, Floowed wins on architecture. You don't manage two vendors, two contracts, two audit trails, and two pricing models. Documents, bureau reports and your own systems' data flow into the same policy engine, which returns a defensible recommendation with a policy snapshot for regulators. Floowed returns the decision and writes it back to your LOS or core; it never moves money.

If your credit and risk teams (not your engineering team) need to own policy changes, Floowed is built for that operator, and they set the thresholds: which applications are recommended automatically, which go to manual review, which are raised as exceptions. If you want to start without a sales cycle (free credits, no card, no sales call) and be live in minutes on the platform, with set-up support for lending integrations, rather than the quarters an enterprise integration takes, that's our default.

This isn't theoretical. In production at Alon Capital, founder Rene de Jesus puts it plainly: "Floowed reads the documents, runs our credit policy, and surfaces a decision in minutes."

What does Ocrolus actually cost?

Ocrolus doesn't publish prices. The model is custom and consumption-based: the line item is "per document, per type", and it scales with volume and contract terms. Public reviews on G2 are strong on accuracy, but a recurring reviewer theme is wanting prices lowered, and a buyer note on Vendr says list rates rise when the annual commitment shrinks, which is how multi-year contracts get sold. For lenders running a few hundred to a few thousand applications a month, the per-document line is one of several vendor lines you'll need (extraction and decisioning, on top of your LMS), and the procurement complexity multiplies.

Floowed publishes its pricing. We charge credits for the work done on each application, not seats and not models, and a credit is a cent at every size. You can start free with 8,000 credits ($80), no card and no sales call. Paid plans start at $100 a month, and custom lending set-ups (bureau data, your scorecard, integrations) are quoted. One vendor, one bill, one audit trail. The detail is on pricing.

How to evaluate

If you genuinely just need extraction on pristine US bank statements, benchmark Ocrolus on that specific surface. If you need a system that reads and analyzes the messier real-world surface (handwritten, photographed, mixed-quality, non-US, multi-language), benchmark on the cases that actually stall today, not the clean ones.

If you need the whole credit case run, the evaluation is different. Score the full path: intake, read and analyze, chase what's missing, policy execution, decision, audit trail. Then score the operating model: who edits the policy when the regulator changes the rule? How long does it take? How many vendor contracts and integration teams are involved?

Talk to us or start free.

Compare also: Ocrolus alternatives and Floowed vs Nanonets. On the document side, read how to detect fake bank statements, or see our pricing. For the wider category, read what a credit decisioning platform is and bank statement analysis software.

FAQ

Does Floowed do bank statement extraction as well as Ocrolus?

On pristine US-format bank statements at high volume, Ocrolus has a decade of specialization that shows. On the broader real-world loan document surface (handwritten, photographed, mixed-quality, non-US), Floowed is built for it, and it analyzes as well as reads: income normalization, cash-flow and bank-statement analysis (ADB, DSCR), and tampering signals. Ours is the wider slice, and it ends in a credit decision rather than a data file.

How is Floowed built?

Floowed runs the whole credit case on an AI-native, multi-agent runtime. It is model-agnostic: each document is routed between open-weight and frontier models on cost, latency and data residency, with no per-type training or template to maintain. What the agents read is cross-checked against every other source, bureau reports and your own systems included. The credit decision itself runs on a deterministic policy engine, versioned, with a full audit trail, and anything still missing is chased as a long-running case that resumes when the answer arrives.

Could I use Ocrolus and Floowed together?

Yes. Floowed takes evidence over API as well as documents, so Ocrolus output can arrive as one input to the credit decision. The pairing rarely earns its keep, though: Floowed already reads and analyzes loan documents as one of its three input lanes, so a second reading layer means paying for document reading twice.

Is Ocrolus available outside the US?

Yes, with growing international coverage. The reference customer base and product roadmap remain strongest in the US, and its specialization is bank statements, pay stubs and tax forms. Lenders whose applicants send regional bank statements, handwritten passbooks or photographed documents should benchmark on those exact files before signing.

Does Floowed price per document?

No. Floowed charges credits for the work done on each application, not per document, per seat or per model, and a credit is a cent at every size. You can start free with 8,000 credits ($80), no card and no sales call. Paid plans start at $100 a month. Custom lending set-ups, built for how you decide with bureau data and your scorecard, are quoted.

What if I have a complex underwriting model in Python?

You can keep it. Bring any score. We orchestrate, we don't compete. Your Python model, CredoLab, Trusting Social and bureau scores all arrive as inputs, absorbed unchanged, and the policy engine combines them with the document evidence under your credit policy. The model keeps doing the scoring; the policy decides what happens to the application.

How fast is Floowed activation?

Live in minutes on the platform, with set-up support for lending integrations, not the quarters an enterprise implementation takes. You start from a template, connect the systems you already run over API or the 40+ lending-stack integrations, and adapt the credit policy in the Floowed Dashboard, back-testing it against your historical book before it goes live. No multi-quarter integration project; custom lending set-ups are quoted and include set-up with our team, and no multi-quarter integration project.

How much does Ocrolus cost?

Ocrolus doesn't publish pricing. It's a custom, consumption-based quote: per document, per type, scaling with volume and contract terms. Reviewers on G2 rate the accuracy highly and the price less so, and a buyer note on Vendr says list rates rise when annual commitments shrink. Floowed publishes its credits instead.

Architecture: data layer vs decisioning layer

Ocrolus and Floowed live at different layers of the loan technology stack. Ocrolus is a data layer: a specialist extraction service whose job is to turn an unstructured document into a structured object. Whatever you do with that object next (score it, underwrite it, decline it, archive it) happens in your downstream system. Floowed runs both layers as one platform: documents are one of three input lanes, next to your own systems and external sources such as bureau reports, and the policy engine evaluates the result against your credit policy and a decision comes out.

Architecturally, a data layer plus a decisioning layer can be a defensible stack. Some large lenders run exactly that way: Ocrolus or a peer for extraction, an in-house Python service or an enterprise decisioning suite for the policy, an LMS for the rest. The complexity is in the seams: maintaining two vendor contracts, two audit trails, two integration teams, and a clear handoff that survives an auditor's questions a year later. The bet Floowed makes is that for most lenders, collapsing those seams into one platform with one bill is the higher-leverage choice, and the decisioning layer is where the leverage compounds. Your LMS and core banking stay as the systems of record; they just stop being where the work happens.

The cost of stack-stitching nobody bills for

The honest cost of a multi-vendor document-to-decision stack isn't on any line item. It's in the integration engineering you'll do once and maintain forever, the audit trail you'll reconcile across two systems every time a regulator asks, the two-vendor pricing models that drift apart over the years, and the time your credit team spends explaining a decision that pulled signal from two sources rather than one. None of that shows up on the Ocrolus invoice; none of it shows up on your downstream decisioning invoice either. It shows up in the headcount you'd otherwise have spent on something more valuable. BIS supervisory technology research describes exactly this kind of multi-vendor seam cost as a hidden tax on financial institutions.

For the high-volume US small-business lenders with deep risk-engineering teams, the math can work. For everyone else, the overhead eats the leverage you bought the specialist for. Floowed's wager is that the right unit of buy for most lenders is one platform running the whole credit case, not two best-of-breed systems and an integration project.

Who owns the decision when documents change?

A useful test of any document-to-decision setup is what happens when the input changes. A new payslip format appears in your applicant base. A different bank starts producing statements with a layout your extraction model hasn't seen. A regional ID card design gets updated. In an extraction-only architecture, your vendor handles the extraction update, but your downstream policy still needs adjustment, often by a different team on a different timeline. In Floowed, the document lane reads and analyzes the new format with no template to build or model to retrain, and your credit and risk team sees the output inside the same policy engine where they edit the policy, so handling the new format is one change, not three. That tight loop matters more in markets where document quality and variety change faster than vendor release cycles, which is most markets outside the US.