Scienaptic AI is a US-based credit decisioning platform that emerged from the AI-for-credit wave and now serves lenders globally with a model-led decisioning stack. Floowed is a global loan decisioning platform, headquartered in Singapore, built as two products on one platform: a Document Intelligence engine that reads and analyses any loan document at any quality, and a Decisioning Engine that runs your credit policy on that data, every application, every time.
Both run policy and models. Only one reads and analyses the documents that arrive as phone photos and handwritten payslips, then runs your policy with the rules behind every call, with a real number from one short call instead of a months-long sales cycle.
Who Scienaptic is built for
Scienaptic AI markets itself as an AI-powered credit decisioning platform. The wedge is models: in-platform model build, deployment, and monitoring, with a credit-officer-facing UI layered on top. The reference customer base skews to US credit unions, US community banks, auto finance, and a growing set of international lenders.
The buyer Scienaptic is built for is the lender with a defined modelling problem: thin-file or near-prime applicants where a better score lifts approval rates without lifting loss rates, with a risk team that wants the modelling lifecycle, deployment, and challenger-champion testing handled in one platform. Pricing is custom enterprise, sales-led, with implementation done by Scienaptic and partners.
Lenders pick Scienaptic when the bottleneck is scoring, not document intake, and when there is appetite for a vendor-led AI engagement that produces a tuned model and a deployment surface for it.
Who Floowed is built for
Floowed was built for the credit and risk teams who need to make decisions on the applications they actually receive: handwritten payslips, photographed bank statements, scanned identity documents, partially completed forms with corrections in the margin. The credit officer is the day-to-day operator at the case level, with risk teams authoring policy alongside. The buyer is the head of credit at a bank, fintech, NBFC, multifinance company, BNPL, rural bank, cooperative, microfinance lender, or any other lender, anywhere in the world.
Floowed reads and analyses these documents, it does not just OCR them. Income is normalized, bank statements are analysed for cash flow (ADB, DSCR), fraud and tampering signals are surfaced, and claims are cross-checked across documents. Where the topic touches identity, KYC, or secured lending, Floowed also cross-checks what a document claims against the evidence in the image: an ID against a selfie, a utility bill against a meter photo, a vehicle title against the chassis photo. A fraud surface pure extraction tools miss.
Floowed is score-agnostic. Bring any score, from CredoLab, Zest, Trusting Social, FICO, Experian, CRIF, your internal model, or a combination, and Floowed absorbs it unchanged. Floowed orchestrates them as inputs to the credit policy on the Decisioning Engine. We do not build proprietary scores and we do not compete with scoring vendors.
Capability comparison
| Capability | Scienaptic AI | Floowed |
|---|---|---|
| Document intelligence on bad-quality input (handwritten, scanned, photographed) | partial (integration model, not native headline product) | ✓ native, headline product, reads and analyses any-quality docs, best-in-class globally |
| Plain-English policy builder for credit and risk teams (Decisioning Engine) | partial (rule UI exists, model-engineer-leaning) | ✓ plain-English engine, credit officer operates, risk teams author |
| Evidence cross-check (claim vs image: ID vs selfie, title vs chassis photo) | ✗ extraction only | ✓ cross-checks document claims against the evidence in the image |
| Time to first decision | weeks to months, vendor-led | ✓ same week, self-serve trial, start free |
| Time to a real price quote | ✗ custom enterprise, multi-month sales cycle | ✓ consumption-based on credits, sized to your operation on one short call |
| Activation timeline (no professional services dependency) | ✗ vendor-led implementation typical | ✓ same-week activation, no professional services required |
| Integrations breadth (LMS, bureaus, KYC, banking) | partial (bureau and data partners, customer-specific integrations) | ✓ 40+ pre-built integrations |
| Score-agnostic orchestration (bring any score) | partial (Scienaptic also ships its own models) | ✓ bring any score, absorbed unchanged, Floowed orchestrates, never competes |
| Audit trail per decision | ✓ standard for enterprise decisioning | ✓ every decision logged with policy version, inputs, outputs, reasoning |
What Scienaptic pitches hardest
Scienaptic leads with the modelling story. The platform is built around the modelling lifecycle: build, validate, deploy, monitor, retrain. That is a real capability and for a buyer whose stated bottleneck is model quality, it is the headline pitch.
The second pitch is the US reference base. Named credit-union and community-bank customers are public, the US auto-finance presence is real, and the company has documented lift in approval rates and loss reductions at multiple lenders. If your buying committee weights US references heavily, Scienaptic shows that signal.
The third pitch is the vendor-led modelling engagement: Scienaptic offers to do the modelling work as part of the contract. For a buyer who wants to outsource model build, that is the proposition.
The honest pushback on each. Modelling is the right pitch when document intake is already solved. For most lenders globally, including most US community lenders we have spoken with, it is not. BIS work on supervisory technology repeatedly flags upstream data quality, not model sophistication, as the binding constraint in real-world lending. The applications arrive as photos, scans, and handwritten forms before any score gets pulled. Reading and analysing that input is the bottleneck before the model. Floowed reads and analyses the paperwork other IDPs choke on: it is best-in-class globally on handwritten, scanned, and photographed loan documents, ahead of US-built IDPs (Ocrolus, Rossum, Hyperscience) that optimised for pristine US enterprise inputs. Solve that layer first and the modelling layer becomes a configuration choice, not a vendor lock-in. Bring any score, absorbed unchanged: FICO, Experian, CRIF, CredoLab, Zest, Trusting Social, an internal model, or Scienaptic's models for that matter. (For context on how regulators view alternative data in credit underwriting, see the CFPB's commentary on using alternative data in credit decisioning.) Floowed orchestrates, it does not compete with the scoring vendor. That structural neutrality is the recommendation: take the document layer from the platform that reads and analyses messy real-world input, take the score from whichever vendor wins your own bake-off, and own the policy layer yourself on a Decisioning Engine the credit and risk teams can actually operate.
Where Floowed wins
Three structural choices separate Floowed from the AI-decisioning category.
Document intelligence reads and analyses, it does not just extract. Floowed reads the documents lenders actually receive: handwritten passbooks, photographed and scanned bank statements, skewed phone photos of a payslip held against a window. Same accuracy whether the input is a clean PDF or a real-world photo. But it does not stop at OCR. It analyses: income normalization, cash-flow and bank-statement analysis (ADB, DSCR), fraud and tampering signals, and cross-document validation. It cross-checks what a document claims against the evidence in the image, an ID against a selfie, a vehicle title against the chassis photo, a fraud surface pure extraction tools miss. This is the headline product, not a partnership, and it reads and analyses the paperwork other IDPs (Ocrolus, Rossum, Hyperscience) choke on. For any lender whose intake is not already a clean digital pipeline, document quality is the bottleneck before the model ever runs. Scienaptic and most AI-decisioning vendors treat this as someone else's problem.
The Decisioning Engine runs your policy, every time, audit-grade. The Decisioning Engine runs your credit policy on that clean, analysed data, every application, every time, with the rules behind each call. The credit officer writes and edits policy in plain English; risk teams author the policy that governs it. Same policy, every application, no exceptions, every decision logged with the reasoning behind it. Bring any score and the engine absorbs it unchanged as an input.
Pricing is fast and consumption-based. Floowed pricing is consumption-based on credits, sized to your operation. A quick call determines the ideal package and cost, fast, not a long or complicated sales cycle. No credit card to start a trial. No sales call to see the platform. If your buyer needs a real number before starting a procurement cycle, you get it from one short call instead of three sales meetings and a multi-month cycle.
Same-week activation, no professional services dependency. The Decisioning Engine is the implementation. The credit officer writes the first policy directly, in plain English, the same week the trial starts. The 40+ integrations with LMS, bureaus, KYC, and banking are pre-built. The first decision happens in weeks, not quarters. Scienaptic, like most enterprise decisioning vendors, sells with implementation services. That model fits a buyer with a multi-quarter timeline. It does not fit a lender who needs to be live this quarter.
One more thing. Floowed is score-agnostic on purpose. We do not build proprietary scores, so we never end up in the awkward position of pushing a customer onto our model when their own model or a partner's model is the right answer. Scienaptic ships its own models alongside the platform. For some buyers that is a feature. For buyers who already have a score they trust, it is a conflict of interest baked into the product.
In production at Alon Capital, founder Rene de Jesus put it simply: "Floowed reads the documents, runs our credit policy, and surfaces a decision in minutes."
What does Scienaptic actually cost?
Scienaptic publishes no pricing, and the review platforms list none either. The model is custom enterprise, sales-led, and shaped by volume, products, and the scope of the modelling engagement, so buyers learn real numbers only inside the sales cycle. Industry reputation puts this class of contract in the high five figures to low six figures annually, with a services component on top for implementation and model build.
The model build is the part to watch. Scienaptic's value proposition is the modelling engagement, which means the true cost includes data-science services and a calibration period before the platform earns its keep. That is a reasonable spend if the model is genuinely your bottleneck. It is a long way around if your bottleneck is the documents.
Floowed prices on consumption-based credits, sized to your operation on one short call, at a fraction of typical enterprise platform cost. One short call gets you a real quote, not three sales calls and a multi-month cycle. Start free or see the live pricing page to start.
How to evaluate
Five questions credit and risk teams can use to compare any decisioning platform against the applications you actually receive.
- Run a real application end to end. Take a recent declined or escalated loan file, with the original document set, including any phone photos or scans, and put it through the platform's intake. Does it read and analyse them into structured data the policy can act on, without manual cleanup? Does it flag tampering or a claim that does not match the image?
- Edit a credit policy in front of the vendor. Ask the credit officer, not the vendor's analyst, to change a debt service ratio threshold or add a new exception rule, then deploy it. How long does it take? Who has to be in the room?
- Ask for the implementation timeline in writing. First policy live, first decision through, full production. Compare against your business need.
- Ask how fast you can get a real price. One short call sized to your operation, or a custom proposal after multiple sales meetings? If it depends on a months-long cycle, factor that into your timeline.
- Confirm the score posture. Can you bring any model, absorbed unchanged? Does the vendor have its own model they will prefer? Is the orchestration layer neutral?
FAQ
Is Scienaptic a competitor to Floowed?
Where the bottleneck is model build and document intake is already solved, Scienaptic and Floowed pitch to different buyers. Where document quality, fast time to a real price, and time to first decision matter more, Floowed is the answer.
Does Floowed build proprietary credit scores?
No. Floowed is score-agnostic by design. We orchestrate any score the lender already trusts, from CredoLab, Zest, Trusting Social, FICO, Experian, CRIF, internal models, or a combination, absorbed unchanged. We do not compete with scoring vendors, we use them as inputs.
What if our bottleneck really is the model, not the documents?
Most lenders we speak to assume that and discover otherwise once they look at their real intake. The applications arrive as photos, scans, and handwritten forms before any score runs. Floowed reads and analyses that first. Bring whichever model wins your own bake-off as the score input.
Can credit and risk teams operate Floowed without engineering support?
Yes, that is the design. The Decisioning Engine is built for the credit officer to write and edit policy rules in plain English, with risk teams authoring policy. No SQL, no DSL, no Python. Versioning, rollback, and per-decision audit are automatic. See the Decisioning Engine walkthrough for details, or read what is loan decisioning.
How much does Scienaptic cost?
Scienaptic does not publish pricing. Industry reputation puts this class of custom enterprise contract in the high five figures to low six figures annually, with implementation and model-build services on top, and real numbers surface only inside the sales cycle. Floowed prices on consumption-based credits, sized to your operation on one short call, at a fraction of typical enterprise platform cost.
How does Floowed compare to other decisioning vendors?
We have published side-by-side comparisons of Floowed vs Zest AI, Floowed vs Provenir, and Floowed vs GDS Link. The pattern is consistent: where document quality and time to first decision dominate, Floowed wins.
Book a demo
If you are evaluating loan decisioning platforms, the fastest way to decide is a demo on your own loan flow with your own documents. We will show you the Document Intelligence engine reading and analysing real applications, the Decisioning Engine running a live policy edit, and the audit trail behind each decision. Book a demo, or start free, and decide from there.