Scienaptic AI

The removal test leaves a bureau score, which is precisely the baseline the company measures against when it claims twelve times more risk differentiation. Machine learning scorecards are built on each client's own loan book and augmented by more than 3,000 signals across bureau, banking and alternative data, fraud models run inside the same decisioning call, and the current product adds language models and agentic capability to the decisioning platform. A decade of model development underpins it.

The automation range is published honestly at 60 to 80 percent of decisions, which leaves a stated remainder for human judgement rather than implying full coverage, and one independent case describes a credit union moving from 28 to 75 percent automated.

The current product's stated purpose is to combine predictive intelligence with conversational capability in order to put humans back at the centre of lending, and counter-offers are described as automatically prepared rather than automatically issued. Held at B because no threshold, referral rule or approval requirement is published for the automated majority.

Performance is quantified against a stated baseline rather than in the abstract, claiming twelve times more risk differentiation than bureau scores alone, and portfolio monitoring surfaces movement at vintage level with delinquency changes measured in basis points over defined windows. The 20 percent loss reduction at a named customer is an outcome rather than a capability claim.

Held at B because no validation methodology, backtest or independent assessment accompanies the differentiation figure, and no accuracy or error rate is published for the fraud models running inside the same call.

Three credit unions are named as live deployments, including one with over a billion dollars in assets serving 72,000 members across twelve branches, and one publishes an outcome triple that is the strongest single-customer result in this index: nine million dollars of incremental indirect vehicle loan originations, an 82 percent lift in credit card approvals, and a 20 percent reduction in losses across credit card and personal loan portfolios.

More originations, higher approvals and lower losses together is the combination that matters, because each alone is easy. A 2026 integration with a major core banking provider reaches a base of more than 950 core banking customers.

One design choice helps: scorecards are built on each client's own book rather than pooled into a shared model, which limits cross-client exposure structurally. Held at C because no boundary statement accompanies it, the alternative data layer is common across clients by definition, and nothing states whether performance observed at one institution informs the modelling approach applied at another.

No data protection agreement, retention schedule, subprocessor list or consent framework was located. More than 3,000 signals per applicant are assembled from bureau, banking and alternative sources, and pre-qualified offers are continuously refreshed against current bureau and behaviour data, meaning members are re-assessed on an ongoing basis rather than only when they apply. Neither the composition of that data nor its retention is described.

No attestation, certification, trust centre or enumerated framework was located. Credit unions holding a billion dollars in assets have completed supplier review before connecting member application data, and a core banking provider has admitted the platform into its own environment, so assurance exists privately and none of it is published.

Fair lending monitoring is described as a built-in process rather than an optional module, running alongside risk monitoring, and deployments are announced explicitly as fair and compliant underwriting with regulatory standards described as integral to the platform. Protected classes are named as a monitored category. Held at B because no statute, regulator or supervisory guidance is identified, so a buyer cannot see which standard the monitoring is calibrated against.

This is the second grade of its kind in the index and it rests on something no other vendor publishes: an outcome figure for protected classes specifically, with approval rates for those groups reported as rising more than 45 percent, alongside up to 40 percent more members approved overall and more than 90 percent of individuals without traditional credit histories becoming assessable.

Where the other fairness leader in this index names the legal standard and the debiasing technique, this one publishes the result, and the two are complementary. The named customer triple supports it materially, since approvals rose while losses fell by 20 percent, which is the evidence that wider access came from better discrimination between risks rather than from loosened standards.

No guarantee, indemnity or correction process was located. The declined member is unaddressed: someone assessed on 3,000 signals including alternative data has no described route to see which signals mattered, to correct inaccurate third-party information, or to contest a decline, and fraud flags raised before underwriting even sees the file are the least visible of all to the person they concern.

Data is characterised by count and category, more than 3,000 signals across bureau, banking and alternative sources, without any individual bureau, banking data aggregator or alternative data provider being named, and no base model or provider is identified behind the language model capability. For an underwriting platform whose central claim concerns fairness outcomes, which alternative sources feed the model is exactly what determines whether disparity enters, and it is undisclosed.

The substantive integration is native placement inside a major core banking provider's loan origination system, so decisioning signals reach credit unions within workflows they already run rather than requiring a separate implementation, and that provider serves more than 950 core banking and 600 digital banking customers globally. Distribution also runs through a credit union service organisation. Held at B because one integration partner is named and no other origination, core or servicing system appears.

No hosting provider, region selection, residency commitment or private deployment option was located. Client-specific scorecards imply per-institution model artefacts, which is a meaningful distinction, and where those models are trained, stored and executed is not described.

No pricing, packaging or basis of charge was located. Distribution through a credit union service organisation implies negotiated collective terms for smaller institutions, which is commercially interesting and nowhere quantified, and nothing indicates whether charge follows decisions, applications or portfolio size.

Product coverage across consumer lending is broad, spanning direct and indirect auto, credit cards, personal loans, home equity and refinance, with a separate risk-based pricing engine calibrated to each portfolio's yield targets and lifecycle pre-qualified offers that refresh against current bureau and behaviour data. The buyer base is heavily concentrated in United States credit unions, with banks and other lenders addressed more generally, so depth in one segment is bought at the cost of breadth.