Provenir

Machine learning runs through the product: real time graph learning for fraud and relationship profiling, per customer models built by an in house data science team, generative assistance for analytics and an agentic execution layer. The company's own framing is that it consolidates data, models and agents into one governed environment. That makes the platform the container and the models the contents.

Strip the models and a low code decisioning engine still stands, with data orchestration, a rules layer and a prebuilt data marketplace. That is substantially what the company sold before the AI layer arrived.

Three controls are described. Agents act within guardrails the customer defines. Strategy changes are validated against real production data before going live rather than after. Integrated case management provides a referral and investigation route for decisions pulled out of the automated path. The pre production validation step is the strongest of these because it is a vendor built mechanism rather than a customer setting.

The guardrails themselves are entirely customer defined with no published default or floor. The human in the loop framing appears in launch coverage rather than as a described control on the vendor's own pages.

Strong on the customer's models and silent on its own. What is published is real and useful to a supervised buyer. It covers model management with monitoring dashboards, explainability extended to Python and other model types, and simulation and scenario modeling. Strategy changes are validated against real production data before deployment. All of it governs models the customer built or deployed.

Nothing describes the vendor's own layer: there is no documentation of the graph learning engine, the generative assistant or the agentic protocol. Versioning, drift disclosure and evaluation results are absent too.

Named customers report quantified outcomes. tbi bank reports a fourteenfold capacity improvement with decisions in milliseconds. MTN reports a hundred and thirty five percent increase in high risk transactions stopped, alongside a hundred and thirty percent increase in pre approvals. SoFi is documented as live on a student loan refinancing build within ten weeks. Beneath the case studies sit platform scale figures of four billion decisions a year across a hundred and twenty customers.

Four independent analyst firms recognize the platform. Forrester lists it as a strong performer in AI decisioning platforms, and Chartis as a category leader in retail credit solutions. IDC MarketScape names it a major player in decision intelligence platforms, and Datos cites it in fraud orchestration. All four evaluators are current and still operating.

The stewardship claim is about model quality, not data handling. The company states repeatedly that models are built from the individual customer's own historical data and outcomes, not generic market models trained on industry averages. Phrased as a data commitment, that would be a strong stewardship disclosure. It is instead phrased as a competitive claim.

It never comes with a statement that customer data is excluded from shared or pooled training, retained only for that customer, or withheld from third party model providers. That gap matters because the platform integrates external LLMs.

The privacy posture rests on independent verification rather than published detail. A third party privacy compliance verification seal is displayed with a checkable registry identifier, a privacy specific external attestation rather than a self assertion. Compliance and data protection are presented as a distinct platform layer rather than a policy page. A privacy policy, cookie policy, modern slavery statement and transparency in coverage disclosure are published alongside it. Retention periods, subprocessors and cross border transfer mechanisms are not published.

Three credentials sit in the site footer, and two of them can be verified independently in one click. An ISO 27001 information security badge links through to the certificate itself. A third party privacy verification seal links to the verifying firm's register with a unique identifier. The third badge is an accountancy body mark carrying no report name, examination period or type. There is no trust portal, no penetration testing disclosure and no subprocessor list.

A software vendor with no license, registration or supervised standing of its own. Regulated environments are addressed as a design target, with the platform described as transparent, compliant and audit ready, but the regulatory obligation sits entirely with the lending institution that buys it. There is no sandbox participation, no supervisory program enrollment and no direct examination exposure.

More than silence and less than a disclosure. Bias mitigation in decision making is named as a platform capability, and explainability runs through the product. The company keeps a dedicated ethics and compliance page in its site footer. No fairness testing method, disparate impact measurement, protected class handling or fair lending validation approach is described. A named capability without a described method is an assertion. What the ethics and compliance page itself says is not reflected here.

Nothing published on recourse for the declined applicant. For a platform whose agents execute credit decisions in real time, the relevant disclosures would be adverse action reasoning, the route by which a rejected consumer contests a decision, and how responsibility is allocated between the platform and the lender when an automated decline is wrong. None appears. Liability falls where it usually does in this market: the lender is the regulated party and absorbs the statutory consequence of a model it did not build and cannot fully validate.

Vendors that buy their models usually name them. This one buys and names none on its own pages. Its marketplace advertises prebuilt integrations to LLMs, and its launch coverage describes integrating leading public and private models, with private instances available through a cloud provider's managed model service. Yet its own dedicated AI page names not one model, provider or version, referring only to LLMs in general. The disclosure exists in trade coverage of a product launch and not in the vendor's own material, which is the wrong way around. The February 2026 launch coverage enumerates the integrated models.

The data marketplace is the real integration asset, offering prebuilt on demand connections to global identity, fraud and credit data sources so a buyer can add a bureau or an alternative data provider without building the integration. Low code drag and drop configuration is positioned as removing the dependency on the buyer's own development team. The depth is toward data suppliers rather than toward systems of record: no core banking, loan origination or servicing platform connectors are named publicly.

Cloud native delivery is stated and private model instances are offered for sensitive workloads, reportedly hosted through Amazon Bedrock, which is a real deployment option. Data residency itself is undocumented: no regions, no countries, no residency commitments and no statement of where decision data is processed or stored. Local legal entities in the United Kingdom, Singapore, the United Arab Emirates, Brazil and Mexico imply regional capability but are not a published residency position.

Nothing is published on price. There is no pricing page and no tier structure. No unit or per decision rates, minimum volumes or indicative bands appear either. Cost comes only through a sales process: every route through the site ends at contact us, book a meeting or talk to an expert. Software directory listings carry no figures.

More than a hundred and twenty financial services customers across over sixty countries process upwards of four billion decisions a year on the platform. Named buyers include a global bank (BBVA), captive auto finance (GM Financial), consumer and card lenders (NewDay, Novuna, Resurs) and a credit union (Meridian). They also include challenger banks (tbi, Bigbank), telecom operators (Telia, MTN), a Brazilian fintech (Jeitto) and a credit bureau (Dun and Bradstreet).

Segments addressed separately are banks, credit unions, fintechs, payments and auto financing. Buy now pay later, credit cards, digital banking, embedded finance, retail lending and small and midsize enterprise lending complete the set, supported by legal entities on five continents.