Floowed vs Zest AI: Decisioning vs ML Scoring

Zest AI is a US-only machine learning credit-scoring engine that has extended into auto-decisioning, fraud, and Model Risk Management documentation. The score is the anchor. The customer base is US banks, credit unions, and auto lenders, and the compliance package (FCRA, SR 11-7) is US-specific. Floowed is a loan decisioning platform with no proprietary score: it ingests documents, runs your policy in a no-code Decisioning Canvas the credit officer operates directly, and orchestrates any score (FICO, Zest, CredoLab, Trusting Social, in-house) into a final decision. Different geography, different center of gravity, different buyer. If you are a US lender choosing a model factory, Zest is on your shortlist. If you are a lender anywhere else, Zest is not the conversation and Floowed is built for you.

The short answer

Pick Zest AI if you are a US bank, credit union, or auto lender that wants a custom ML underwriting model trained on FCRA-compliant bureau data, with the regulatory documentation US examiners expect. The named customers (Citibank, Discover, Truist, Freddie Mac, dozens of credit unions) are all in that bracket and Zest is built for them.

Pick Floowed if you are a lender outside the US, or if you operate in the US but want a decisioning layer that orchestrates any score rather than locking you into one model vendor. Floowed is global, HQ Singapore, score-agnostic by design, with native document intelligence on whatever applicants actually send and a credit officer as the operator. For the underlying category split, see the deeper write-up on credit decisioning vs credit scoring.

Quick comparison

Are they actually competitors?

Only partly, and only in one geography. Zest AI began life in 2009 as ZestFinance, a machine learning credit-scoring company. Sixteen years later the score is still the gravitational center of the product, even as the company has built out auto-decisioning, fraud detection (Zest Protect, August 2024), Model Risk Management documentation, and a Temenos-native embed (April 2025). Zest now markets itself as "AI-Automated Credit Underwriting" and claims to auto-decision 80 percent of applications. That is a real decisioning footprint inside the US.

Floowed went the other way. We built decisioning first and never shipped a score. The Decisioning Canvas is where credit officers compose policy: ingest a payslip, pull a bureau report, call a fraud signal, branch on debt-to-income, escalate edge cases for human review. Any score plugs in as an input, including Zest's. We are the orchestration layer, not the model factory.

Geography settles the rest. Zest AI is US-only. FCRA and Model Risk Management compliance, the bureau data they train on, and the credit union channel they sell into are all US-specific. In Southeast Asia, Latin America, Africa, and most of Europe, Zest is not on the shortlist. Floowed is. So inside the US, we overlap on the decisioning workflow story for buyers who want score-agnostic policy logic. Outside the US, we are not in the same room.

Where Zest AI is on the shortlist (for US lenders)

If you are a US bank, credit union, or specialty lender, Zest AI belongs on your shortlist for ML scoring. The reasons are factual.

Sixteen years of ML model development on FCRA-compliant bureau data. Auto-generated Model Risk Management documentation that maps to SR 11-7 and the related US regulatory framework, including the validation, fairness testing, and monitoring artifacts examiners expect. Named US customers including Citibank, Discover, Truist, Freddie Mac, First National Bank of Omaha, First Hawaiian Bank, and a deep credit union book through VyStar, Suncoast, Golden 1, Hawaii USA FCU, and Idaho Central. Native Temenos integration since April 2025, plus MeridianLink and Origence partnerships through credit union service organizations and LOS embeds. Zest Protect bundles fraud detection with scoring, distributed through MeridianLink since February 2025. For a US credit union running on Temenos or MeridianLink that wants one vendor for scoring, decisioning, and fraud, Zest is the platform that bracket of the market is built around.

None of those facts make Zest the right answer for a lender outside the US. They make Zest the right answer for a US lender whose bottleneck is approval rates on thin-file or protected-class applicants and who wants a custom ML model to move that curve. Different buyer, different problem, different geography from the rest of this comparison.

Zest AI outside the United States

This section is short because the answer is short. There is no Zest entity, sales motion, compliance package, or named customer in Southeast Asia, Latin America, Africa, or Europe. The company has not announced a non-US expansion and the product is structurally tied to US bureau data and US Model Risk Management documentation. A lender in the Philippines, Indonesia, Vietnam, Malaysia, or Singapore evaluating decisioning platforms is not evaluating Zest. They cannot, given how the platform is built.

That matters for the buyer this comparison is for. The single-largest competitive question for Floowed in the US is whether a Zest customer would be better served by a score-agnostic decisioning layer on top. The single-largest competitive question for Floowed outside the US is whether a lender should evaluate Zest at all, and the honest answer is no.

Where the Zest model breaks for the buyer outside the US (and for some inside it)

Zest's model-led architecture is the right shape for a US lender whose primary bottleneck is the score itself. For a different buyer profile, four things break.

The score is the anchor, but the score is not the bottleneck for most lenders. Zest's pitch is that a custom ML model on FCRA bureau data lifts approval rates by 30 percent on average. That is a real number for a US lender whose denial pool sits behind a generic FICO cut-off. It is not a relevant number for a SEA SME lender whose denial pool sits behind a credit officer manually retyping numbers from a JPEG of a payslip, or for a lender whose policy needs to change quarterly based on portfolio performance and macro conditions, or for a lender who already plugs in CredoLab, Trusting Social, or an internal model and wants the decisioning layer to orchestrate them. The score is one input. For most lenders, fixing the score is not the bottleneck. Fixing the policy and the document intake is.

Document intake is not a primary capability. Zest is built around structured bureau and application data. The platform assumes the inputs are clean: API-fed bureau scores, structured KYC payloads, digital application forms. For a US credit union onboarding through Temenos, that assumption holds. For a SEA mid-market lender whose applicants send photos of payslips, scans of bank statements, and handwritten income declarations, that assumption is the operational bottleneck. Zest does not solve it. Floowed treats it as a first-class part of the platform.

The Model Management System is the wrong operator profile for the buyer below tier-two. Zest's policy editing surface is the Model Management System, which is built for credit teams that think in models, cut-offs, and challenger experiments. That maps well to a US bank's risk-engineering team. It does not map to a credit officer at a fintech, NBFC, or multifinance lender who edits the policy by writing a rule the way they would explain it on a whiteboard. Plain English is closer to how the policy is actually written in those lenders, and that is the operator surface Floowed is built for.

One vendor for scoring, decisioning, and fraud is the wrong bundle for a buyer who already has those covered. The Zest pitch packages scoring, auto-decisioning, fraud detection, and MRM documentation under one roof. For a US credit union that wants one vendor across all of it, that bundle is the value. For a lender that already has a bureau relationship, an existing fraud vendor, and a separate KYC stack, paying for the bundle to get the decisioning layer is the wrong economics. Floowed sells one product (loan decisioning) and orchestrates whatever you already have.

Where Floowed is built to win

Floowed was built around three structural choices, each anchored to a buyer Zest does not sell to today.

Score-agnostic by design. Floowed is the orchestration layer, not the model factory. CredoLab, Trusting Social, Zest, FICO, Experian, your local bureau, your internal model, or a combination. Use whichever score is right for your portfolio in your market. The Decisioning Canvas combines whatever score you bring with bureau data, bank statement analysis, internal application history, KYC results, and the documentary evidence the applicant provides. The score is one input to the policy. We do not compete with scoring vendors and we do not compete with bureaus. We use them.

For a SEA lender, this is the only architecture that works. The market does not have a single dominant score the way the US has FICO. CredoLab covers some of the alternative-data layer, Trusting Social covers another, the local bureaus cover the traditional layer, internal models cover the rest. A platform that sells you its own score and then bundles decisioning around it is asking you to pick a horse before you have run any of them. A platform that orchestrates any score lets you pick whichever one wins on your portfolio.

Native document intelligence on whatever applicants actually send. Floowed reads handwritten payslips, photographed bank statements, scanned business registrations, partially completed application forms with handwritten corrections, utility bills, and identity documents from across Southeast Asia. Same accuracy across input quality. The output is structured data the decisioning layer acts on directly. This is the headline product, not a partner integration and not a side capability. If document quality is the operational bottleneck in your portfolio, this is the structural difference that separates Floowed from a model factory whose document strategy is "send us clean digital inputs."

The credit officer is the operator. The Decisioning Canvas is a no-code policy editor designed to be operated by the person who actually owns the credit policy. Rules are written in plain English. If the salary is below $1,500 and the requested amount is above $5,000 and the applicant has been employed less than six months, send to manual review. The credit officer ships that change directly. Versioning, rollback, and per-decision audit trail are automatic. There is no Floowed services team translating the policy into the platform's internal language because there is no internal language to translate into.

Published pricing and same-week activation. Core is $399 a month on annual or $499 a month on monthly. Scale is $799 a month on annual or $999 a month on monthly. Enterprise is custom. The first loan application is free. The first policy is live in days. No credit card to start a trial. No sales call to see the platform. A lender priced out of an enterprise SaaS contract is not priced out of Floowed's $399.

Document intelligence is the structural difference

Decisioning platforms can in principle look the same at the policy layer. Both run rules. Both have audit trails. Both can ingest a credit score and turn it into a yes, refer, or no. The difference shows up before the decision logic ever runs.

If your applicants send clean digital data through APIs and structured forms, the document intake question is uninteresting. The data is structured. You parse it, you decide. US credit unions running on Temenos or MeridianLink usually live in that world. Zest is built for that input pipeline.

SEA SME lending is not that world. The applications include scanned identity documents, handwritten income statements, photographed utility bills, partially completed application forms with handwritten corrections, business registrations from a dozen formats, and bank statements that range from a clean digital export to a phone photo of a printed statement with the corner folded over. That is the input pipeline.

Floowed reads all of that as a first-class product surface. Same accuracy across input quality. The output is structured data the decisioning layer acts on. No separate document-intelligence vendor procurement, no integration work, no second contract. If document quality is your operational bottleneck, this is not a feature you should expect a model factory to retrofit. It is a structural choice Floowed made on day one.

Which buyer should pick which

If you are a US bank, credit union, or specialty lender whose bottleneck is approval rates on thin-file or protected-class applicants, you have the regulatory appetite and budget for an enterprise SaaS contract, your LOS is Temenos, MeridianLink, or Origence, and you want one vendor for scoring, decisioning, and fraud, Zest AI is on your shortlist. We do not chase that segment.

If you are a lender outside the US, or a US lender who wants a decisioning layer that does not lock you into one model vendor, the rest of the platform fit lines up with Floowed. Score-agnostic decisioning that orchestrates whatever score is right for your portfolio. Native document intelligence on whatever applicants actually send. A credit officer who edits the policy directly in plain English without filing a ticket every time a cut-off needs to move. Same-week activation at a published price the budget can absorb. That is what Floowed is built for. Different category, different buyer, different geography from Zest.

Frequently asked questions

Is Zest AI available outside the United States?

No. Zest AI operates in the United States only. The compliance package (FCRA, Model Risk Management) and the bureau data partnerships are US-specific. There is no SEA, LATAM, or EU presence as of this writing. A lender outside the US is not a Zest customer.

Is Zest AI a scoring platform or a decisioning platform?

Both, with scoring as the anchor. Zest started as ZestFinance in 2009 building ML credit-scoring models. They have since extended into auto-decisioning, workflow automation, MRM documentation, and fraud detection. The product still leads with the model and bundles decisioning around it.

Can Floowed integrate with Zest AI scores as inputs?

In principle, yes. Floowed is score-agnostic by design. If a US lender uses a Zest model and wants Floowed as the decisioning and document intelligence layer on top, the Zest score becomes one input alongside bureau data, KYC signals, and document-extracted attributes. In practice this is rare today because Zest customers are typically already using Zest's own decisioning workflow.

How does Zest AI's auto-decisioning differ from Floowed's Decisioning Canvas?

Zest's auto-decisioning is model-led. The ML model produces a score and the workflow optimises cut-offs around it, with credit teams tuning policy through the Model Management System. Floowed's Decisioning Canvas is policy-led. Credit officers compose rules in plain English, branch on any attribute including external scores, and edit policy directly without retraining a model.

What is Model Risk Management and does Floowed support it?

Model Risk Management is the US regulatory framework (SR 11-7 and related guidance) requiring banks to validate, monitor, and document the models they use in lending decisions. Zest auto-generates this documentation for their ML models. Floowed does not produce a proprietary score, so MRM in the Zest sense does not apply. Floowed does provide full audit trails, version history, and decision logs for every policy change in the canvas, which supports the equivalent governance need for rules-based decisioning, and maps to BSP, OJK, MAS, and other Asian regulators in the same way.

Does Zest AI have native document intelligence?

Not as a primary capability. Zest is built around structured bureau and application data. Document intake on handwritten bank statements, scanned payslips, or photographed IDs is not the lane Zest sells into. Floowed treats it as a first-class part of the platform.

What about Floowed vs other decisioning platforms?

See Floowed vs Taktile for the agentic-decisioning comparison and Floowed vs Provenir for the SEA-incumbent comparison.

The bottom line

Credit scoring tells you the risk of a borrower. Credit decisioning tells you what to do about it. Zest AI is the strongest US pure-play on the scoring side, with a credible auto-decisioning extension layered on top of an ML model and a deep credit union and US bank book. For a US lender who wants a model factory with decisioning bundled around it, Zest is on the shortlist for real reasons.

Floowed solves a different problem for a different buyer. Score-agnostic decisioning that orchestrates whatever score is right for your market. Native document intelligence on whatever applicants actually send. A credit officer who edits the policy directly in plain English. Published pricing and same-week activation. If you are a lender in Southeast Asia, Latin America, Africa, or most of Europe, Zest is not the conversation. If you are a US lender who wants decisioning that is not anchored to one model vendor, Floowed is the alternative the bundled-vendor pitch does not offer.

Book a walkthrough

If you are evaluating loan decisioning platforms, the fastest way to decide is a 45-minute walkthrough on your own loan flow with your own documents. We will show you the Decisioning Canvas, a live policy edit, and document intake on real applications. Book a Floowed walkthrough.