Prism Data

The removal test leaves raw bank statements. Models do every step: categorising disorganised transaction data into clean account activity, deriving thousands of trended attributes across income, assets, expenses and behaviour, projecting future income, and producing consortium trained scores predicting twelve month default, first party fraud and first payment default. The company states it has seen more financial transaction data over a longer period than any other provider in the category, which is a modelling advantage rather than a distribution one.

The output is a score and a set of attributes that the lender applies within its own credit policy, so approval, pricing and portfolio decisions stay with the accountable institution, and the design is explicitly for dropping into existing loan applications rather than replacing the decision process. Adverse action reason codes accompany the score, which means a declining lender can state why rather than pointing at an opaque number. What is absent is any description of confidence handling on an individual score, or guidance on how a lender should treat a cash flow assessment that conflicts sharply with a bureau score.

Model governance is visible in the shape a model risk function would recognise. The score is versioned publicly across successive releases in 2022 and 2024, predictive power is expressed using a standard separation statistic rather than a marketing figure, the training base is described as millions of records across lenders and products, and the score is characterised as additive to conventional assessment rather than a replacement, which is a testable claim.

Independent research validated the efficacy of the approach. What holds this below the top grade is that the headline improvement is qualified as average lift based on the company's own analysis across client portfolios, with no absolute figure published and no external performance audit.

A major credit bureau distributes the score as part of its own open banking suite, which is an incumbent reselling an alternative data product rather than competing with it. A named decision platform is supported, itself an indexed vendor, and a lender serving independent contractors is named with its chief risk officer quoted on using the score, income and attribute products together.

A large consumer finance company took a strategic investment position and its chief strategy officer commented publicly on the platform. Independent research from a nonprofit innovation centre studied the score empirically. Operational figures are published at sub second response times and better than 99.9 percent availability, on nearly a decade of experience serving regulated institutions.

This vendor answers the pooling question by making pooling the disclosed product. The flagship is described as the first consortium based cash flow underwriting model, built from millions of anonymised, consumer permissioned records drawn from many different banks, credit products and customer segments, on the stated reasoning that the best models rest on many observations and diverse data.

Every participant therefore benefits from what the whole consortium contributes, the contribution is anonymised, and the consumer permissioned. That is a tenth distinct answer to the cross party boundary question in this index and an unusually honest one, since the alternative would be to obscure that a client's data improves a competitor's score.

The architecture is the strongest recorded for a consumer credit data business and it is described step by step. The client pulls deposit account data through its own systems or any aggregator, that data is de identified before it is shared with the company, and processing returns outputs without the underlying records ever arriving attached to an identity.

The consortium models behind the score were built from anonymised, consumer permissioned records, so both the technique and the consent basis are named. Delivery is additionally offered through consumer reporting agency and non agency channels so an institution can choose the legal framework that fits its use, which is a privacy posture designed around the statute rather than asserted alongside it.

No attestation, certification, trust centre or enumerated framework was located, which is the one conspicuous omission in an otherwise exceptionally well documented compliance posture. A major credit bureau has taken this into its own product line and banks and fintechs run it in live credit decisions, so assessment has been passed repeatedly, and publishing the control set would complete a disclosure record that is otherwise the most thorough in this index.

Both governing United States statutes are named and the obligations they create are addressed operationally rather than asserted. The company states compliance with credit reporting and equal credit opportunity law, confirms the score may be used for approve, decline and pricing decisions, and supplies adverse action reason codes so lenders can meet their notification duty.

It offers delivery through both consumer reporting agency and non agency channels to suit different use cases, and publishes an explanation of how disclosure duties differ between the two, including the consumer's right to request within sixty days the nature of non agency information used against them. That is a vendor engineering around the statute and teaching its customers the mechanics.

The strongest fairness evidence in this index, because the decisive piece is independent rather than self reported. A nonprofit innovation centre studied cash flow underwriting empirically across six non bank providers including users of this score, and found the models appeared to predict creditworthiness within protected populations at least as well as traditional scores and attributes, and better in select cases.

Alongside that the company states its models undergo rigorous fair lending review and have been shown not to create disparate impact, supplies adverse action reason codes so declined applicants learn the reasons, and targets consumers with no, thin or stale files explicitly.

It also publishes the uncomfortable half, that as many as 20 percent of prime and super prime consumers score poorly on cash flow and are genuinely higher risk, which shows the method reallocates rather than simply loosens credit.

The first vendor in this index whose recourse mechanism reaches the affected individual by law rather than by goodwill. Adverse action reason codes are supplied with the score, so a declined applicant is told which factors drove the outcome rather than that an algorithm said no. Delivery through a consumer reporting agency channel brings the full statutory dispute and correction apparatus with it, and the company publishes the mechanics of the alternative path, including that a consumer declined on non agency information may request within sixty days the nature of the information used. The lender is also served, since documented reason codes and fair lending review are what it produces when an examiner asks. What is missing is anything covering the vendor's own error.

The data path is described end to end with unusual clarity: deposit account data originates with the client, is pulled through any aggregator or directly from client systems, is de identified, and reaches the platform through aggregators, decision engines or a single endpoint connection. The consortium training base is characterised by breadth across banks, products and segments. The models are the company's own.

What is not named is any individual aggregator, which matters because coverage and parsing quality vary between them and determine what the score sees, and no subprocessor list or hosting arrangement was located.

Every practical route into a lender's stack is covered and two are named. A major credit bureau carries the score inside its own open banking product set, which reaches institutions that would never contract with a startup directly, and compatibility with a named decision platform lets lenders deploy and iterate their cash flow strategies without engineering work.

Beyond those, data can arrive through any aggregator, through decision engines, or directly from client systems via a single endpoint interface, and outputs return in under a second with published availability. The company's stated ambition is to become common infrastructure the whole industry uses, and the integration surface is built for it.

No hosting provider, region selection or residency commitment was located. Exposure is reduced by design rather than by disclosure, since data arrives de identified and the service is domestic, and lenders operating under supervisory expectations for third party processing would still expect the arrangement documented.

No pricing, packaging or basis of charge was located. The product line spans several scores and attribute sets that would ordinarily price separately, and distribution through a credit bureau introduces a second commercial route that is equally undescribed. Nothing indicates whether charge falls per score pulled, per applicant, by volume tier or as a licence.

Product coverage spans essentially the whole consumer credit market, with the score usable for credit cards, personal loans, mortgages, auto loans, buy now pay later, cash advance, earned wage access and lease to own, plus small business credit, and specialised variants exist for short term and high risk lending where default behaviour differs. Buyers span banks, lenders and fintechs, reached directly and through a credit bureau's distribution. Applicant coverage is the deliberate extension: the score works for consumers with no credit file, thin files or stale files who conventional scoring cannot assess at all.