Prism Data alternative: Cevrynt vs Prism Data: cash-flow scores vs underwriting review

Prism Data and Cevrynt do different jobs. Prism Data describes itself as "the cash flow underwriting solution": it turns bank transaction data into scores such as CashScore and thousands of cash-flow attributes, delivered by API and through partners. Cevrynt is evidence-linked underwriting preparation for U.S. MCA funders and alternative lenders: it assembles the borrower file, analysis, verification, fraud signals and policy results for a human decision. One supplies signals; the other is the workspace where a file gets reviewed. Many lenders could use both.

This page compares the two using Prism Data's own public pages, linked throughout, and Cevrynt's published product descriptions. It covers what each says it does, a feature-by-feature table, when each is the better fit, how Prism Data sits among other cash-flow tools buyers search for, and the questions worth asking both vendors before a pilot.

01At a glance

02What Prism Data says it does

Prism Data's homepage leads with expanding approvals and lowering losses "with cash flow underwriting", and says Prism "transforms raw transaction data into actionable insights and scores." Its About page says Prism "pioneered cash flow underwriting to expand credit access for consumers without traditional credit history," and traces its data work back to the team behind Petal. The product line, as the site presents it:

• CashScore. Described on its product page as a cash flow underwriting score built from deposit account data and "independent of credit scores." The page shows a three-digit score with a risk rating and a list of reasons, says the score is explainable "for consumer and SMB credit decisioning," and lists FCRA compliance and model governance among its trust points. It also names specialty scores, including CashScore Detect, aimed at first-party fraud and early payment default.
• Insights. "Tens of thousands of attributes" grouped as behaviors, counts, patterns, ratios, stability, sums, trends and volatility, with spending categories and risk red flags (Insights).
• Income. Estimates gross and net pay from transaction activity, breaks income out by source, and measures trends, frequency and stability (Income).
• Categories. Transaction categorization that turns "messy bank data from any source" into categorized account activity for underwriting (Categories).
• Pulse. Continuous cash flow monitoring with time-series access to CashScore, Insights and Income, plus event alerts such as a new income source or decreasing liquidity, for uses like credit line management, collections and risk monitoring (Pulse).
• SMB Intelligence. Cash-flow analytics for small-business lending, covered in more detail below (SMB Intelligence).

Prism Data for small-business and MCA lending

The page most relevant to MCA funders is SMB Intelligence, headlined "Make better SMB lending decisions, faster." Prism says it works "with transaction data from bank statements, first-party bank data, or any open banking aggregator," consolidates activity across submitted business accounts, and offers calendar-month, month-to-date and rolling 90-day views. The page lists SMB-specific attributes for revenue, cash flow, debt and obligations, liquidity and stress, repayment capacity and stability.

Two lines on that page map directly onto MCA underwriting work. Prism says the product can "separate operating revenue from transfers, financing activity, and owner draws," and "detect merchant cash advances, stacked financing, and other debt signals traditional reports miss." For portfolios, it describes "time-series attributes to track deterioration, new debt, and renewal readiness." The page is explicit about the division of labor: the attributes feed "risk models, rules engines, policy thresholds, analyst review, digital experiences, and portfolio monitoring," which the lender runs.

How Prism Data is delivered

According to its homepage, Prism Data is reached through a single-endpoint API or through partners: data aggregators (it names Plaid Check), credit reporting agencies (it names Equifax) and decision engines (it names Alloy, Provenir and Taktile). The homepage also says the client pulls the deposit account data, which is de-identified before it reaches Prism, and that Prism does not itself supply consumer transaction data. Prism's developer documentation was password-protected when we reviewed it, so this page does not describe its API in detail.

03What Cevrynt does

Cevrynt is an early-stage, AI-assisted underwriting and decision-intelligence platform built first for U.S. merchant cash advance funders and alternative lenders. It works on the borrower file as it actually arrives, usually a submission with bank statements, an application and supporting documents, and turns it into a structured, reviewable analysis for an underwriter. The workflow runs Intake → Documents → Financials → Verification → Fraud → Policy → Report → Human decision. Around that review sits a built-in MCA CRM, so the same deal moves from submission to offer, contract, funding and servicing in one system.

• Document intelligence: classifies each submitted file and extracts the fields underwriting depends on, keeping every value linked to the page and line it came from.
• Bank statement analysis: deposits, true revenue versus transfers, average daily balance, negative days, NSFs, and existing MCA positions and stacking, with each figure traceable to the statement.
• Business verification: KYB checks on the business behind the file, presented for review alongside the financials.
• Fraud signals: document and application risk signals surfaced as flags for a person, never as automatic declines.
• Policy engine: the lender's own credit policy applied to the file, showing what passed, what failed and what needs judgment.
• Underwriting report: an evidence-linked report that carries reviewer notes, overrides and audit history.
• MCA CRM: a built-in CRM that carries the deal past the memo: deal pipeline and stages, ISO and broker records with their submissions, offers generated from the funder's own pricing and terms, contracts with e-signature, and funding, daily remittances, collections and renewals.

Cevrynt is not a lender. It scores each deal and recommends a decision and amount with the reasons, and the funder's underwriter makes the final decision. It does not fund deals or guarantee funding; offers are built from the funder's own pricing and terms and go out only when the funder decides. It is not an open-banking data provider, and syndication reporting is not part of Cevrynt today. Integrations with systems a funder already runs are scoped with each team before a pilot (Integrations); the product is evaluated through a guided walkthrough or a scoped pilot, and pricing is not published. For how Cevrynt handles data, see Security.

04Feature comparison

The table names each capability the way each company describes it. Where Prism Data's public site does not describe something a buyer would reasonably ask about, the cell says so; that is a prompt to ask, not a statement that the capability is missing.

05When Prism Data may be the better fit

Prism Data is built to put cash-flow signals into systems a lender already runs. Based on how it describes itself, it is likely the better choice when:

• You want a cash-flow score in your decision stack. If your team runs scorecards, rules or a decision engine and wants a three-digit cash-flow score with reasons next to bureau data, CashScore is described for exactly that.
• You lend across consumer products. Prism's use-case list spans credit cards, personal loans, BNPL, auto, mortgage, earned wage access and more. Cevrynt does not serve consumer credit or mortgage.
• You need attributes for your own models. Data science teams that build and govern their own models can take Prism's attributes as features rather than adopting a review workspace.
• You already use its partners. Prism says it is reachable through aggregators, credit reporting agencies and decision engines such as Provenir, Alloy and Taktile, which can shorten integration if those are in place.
• You monitor a book after funding. Pulse and SMB Intelligence time-series attributes are described for tracking deterioration, new debt and renewal readiness over the life of a customer. Cevrynt's CRM tracks remittances, collections and renewals, but it does not produce monitoring attributes or alerts of that kind.
• Your flow is high-volume and automated. If most decisions are made by a model with little manual file review, an API that returns scores and attributes may be all you need.

06When Cevrynt may be the better fit

Cevrynt is built for the part of MCA and small-business lending where an underwriter still opens the file. It is likely the better choice when:

• Your underwriters review every file by hand. If deals arrive from ISOs as PDFs and an underwriter rebuilds revenue, balances and positions in a spreadsheet, Cevrynt structures that file for them.
• You need to see where each number came from. Cevrynt keeps every extracted figure linked to the statement page and line, so credit committee, investors or an auditor can check it.
• Your policy is yours, and judgment matters. Cevrynt applies your rules and shows pass, fail or needs-judgment per rule, then leaves exceptions open for a person rather than scoring them away.
• You need more than bank data in one place. Documents, business verification, fraud signals, policy results, reviewer notes, overrides and the final report sit in one reviewable file.
• You want an MCA CRM with underwriting built in. The pipeline, ISO submissions, offers, contracts, funding and servicing sit in the same system as the underwriting file.
• You do not run a model or decision engine. Many smaller funders decide by policy and underwriter judgment; a score and attribute feed assumes infrastructure they may not have.
• You want a hands-on, MCA-first pilot. Cevrynt is early-stage and works directly with each team on a scoped pilot. Teams that want a mature, broadly deployed data provider today may prefer Prism.

07Prism Data vs other cash-flow tools

People searching for Prism CashScore alternatives are usually comparing cash-flow data and scoring providers, decision platforms and underwriting workspaces, which do different jobs. Each tool below is described from its own site; our other comparison pages go deeper.

• Nova Credit Cash Atlas. Nova Credit describes Cash Atlas as cash flow underwriting that turns consumer bank transaction data into FCRA-compliant attributes, reports and scores, under the line "Lift your Underwriting with Granular Cash Flow Data." It is the closest in kind to Prism Data's consumer products. See Cevrynt vs Nova Credit Cash Atlas.
• Carrington Labs. Carrington Labs offers "custom credit risk models built on the data you already have," including a Cashflow Score, and lists small business cash flow underwriting as a use case. See Cevrynt vs Carrington Labs.
• Provenir. Provenir describes a "Decision Intelligence Platform" for risk decisioning across credit, fraud and collections. Prism's homepage lists Provenir among the decision engines it works through, so the two are often used together rather than instead of each other. See Cevrynt vs Provenir.
• Bank statement and document tools. If the job is turning statement PDFs into data or checking documents for tampering, compare Ocrolus, MoneyThumb and Inscribe.
• SMB underwriting automation. For tools that automate intake and analysis across the whole SMB file, see Kaaj and Heron Data.

For the categories behind these tools, read the four types of AI underwriting software, decision engine vs underwriting engine and cash-flow underwriting for SMB loans, or start at the comparison hub.

08Questions to ask both vendors

1. What input does it need: bank statement PDFs as ISOs send them, open-banking data, or both? Who obtains and converts it?
2. How does it separate operating revenue from transfers, loan proceeds, owner draws and existing advance repayments, and can an underwriter see why each transaction was classed that way?
3. Which existing MCA positions does it detect, and how does it show the evidence for each daily or weekly debit?
4. Can a reviewer trace any figure or attribute back to the statement page and line, or to the raw transaction?
5. Whose credit policy is applied, where do the rules live, and how is each result explained to the underwriter?
6. What happens to exceptions and flags: are they decided automatically, or held open for a person? How are overrides recorded?
7. What does a credit committee or auditor receive at the end: a score, an attribute file, or a report with evidence?
8. Which FCRA, adverse action and fair lending responsibilities stay with the lender, and what documentation does the vendor provide?
9. What data leaves your environment, where is it stored, for how long, and is it used to train shared models?
10. Which performance claims can the vendor show on files like yours, in a retro study or pilot you design?

09About this comparison

Prism Data descriptions summarize Prism Data's own public website as reviewed on October 30, 2026, and link the page each statement came from. Nova Credit, Carrington Labs and Provenir are described only from their own sites. We did not use review sites, analyst lists or third-party articles as sources for vendor facts, and we do not repeat any vendor's pricing, accuracy, lift or customer figures. Prism Data's developer documentation was password-protected at review, so API details are not covered. Cevrynt descriptions come from Cevrynt's own product pages.

Sources reviewed: Prism Data homepage, CashScore, SMB Intelligence, Insights, Income, Categories, Pulse, About, Nova Credit Cash Atlas, Carrington Labs and Provenir.

Products change; confirm current capabilities with each vendor. Prism Data, CashScore and all other product and company names are trademarks of their respective owners and are used only for identification. Cevrynt is not affiliated with, endorsed by or partnered with Prism Data or any other company named here. Spot something out of date or inaccurate? Email sales@cevrynt.com and we will correct it.