Ignosis

The removal test leaves raw consented bank data with nothing done to it, which is the gap the company exists to close, describing its purpose as making financial data both accessible and actionable. Models detect income where no formal proof exists, assess repayment capacity, analyse spending, generate fraud and financial health signals and drive personalised prompts, and the chief technology officer frames the shift precisely as moving institutions away from legacy bank statement analytics into financial data intelligence. Agents handle customer interaction, lead qualification and support, with finance specific language models in development.

The platform supplies signals into decisions institutions make rather than making them, and a customer describes the arrangement in exactly those terms, layering derived signals into case allocation and follow up to complement an existing in house collections operation rather than replace it. Underwriting improvements are described as enhancing an existing process.

Against that, automated agents handle customer interactions and lead qualification directly, and nothing describes what those agents may do unsupervised, when a case escalates, or what review applies to an income determination before a lender relies on it.

Accuracy is claimed for the capability that matters most, income detection, and unusually a customer corroborates it independently, describing improved income detection accuracy in its underwriting process rather than leaving the claim to the vendor. The stated positioning against legacy statement analytics implies a comparison baseline.

What is absent is any figure: no detection accuracy, error rate, false positive rate or validation result appears, which matters because an income estimate derived from transaction patterns determines whether a person with no payslip is lent to at all.

More than 125 financial institutions in three years is substantial adoption for a company of 35 people, and the financial evidence is rarer still: the company reports breaking even in one financial year and turning profitable in the next while growing over 100 percent annually. A leading regional venture firm's accelerator led the pre Series A alongside a payments company's venture arm and a prominent consumer fintech founder.

It won a national fintech award for technology innovation in 2025. Three customer testimonials describe specific effects, including improved income detection accuracy in underwriting and account aggregator signals layered into case allocation and collections follow up.

The regulated framework does substantial work that a vendor policy could not, since data retrieved under the account aggregator regime is bound to the purpose the customer consented to and cannot be repurposed freely, which limits what any participant may do with it. That is a real structural boundary and it applies to the platform as much as to its customers.

Held at B because the analytics layer sits above the framework: nothing states whether models trained on one institution's borrower outcomes inform scoring at another among the 125 served, or what a lender contributes by participating.

Consent here is architectural rather than contractual, which is what earns this grade. The account aggregator framework the platform is built on requires explicit, purpose limited and revocable customer consent before any financial data moves, so the individual controls the flow by regulatory design rather than by vendor policy, and the company describes data as consented and encrypted throughout.

Compliance with the national data protection statute is stated alongside compliance with the central bank's framework. The chief executive states plainly that compliance matters as much as scale in a sector where consumer trust and data protection are critical, and funding is allocated to deepening governance.

No attestation, certification, trust centre or enumerated framework was located, with security addressed through the description of data as encrypted and through regulatory compliance claims. More than 125 financial institutions have completed supplier assessment, and the company states it is expanding compliance capacity with new funding, so a published control set is the natural next disclosure.

Four named public infrastructure instruments and two named authorities anchor this, which is the deepest jurisdiction specific regulatory disclosure in the index. The platform operates on the central bank's account aggregator framework, the open credit enablement network and the open commerce network's financial services layer, and states compliance with both the central bank and the national data protection statute.

That is not vocabulary borrowed for marketing: these are the rails the business is built on, and operating on them means accepting the consent, purpose limitation and participant obligations each imposes. The company also allocates funding specifically to compliance capacity.

The inclusion argument is structural and the mechanism matches it precisely, which is what distinguishes this from the usual claim. More than 160 million consumers are excluded from affordable credit, insurance and planning specifically because they cannot produce formal proof of income, and around 80 percent of small businesses cannot access formal credit.

The platform's core capability is detecting income from consented bank data, which substitutes observed cash flow for a document the person does not have. That is the same argument recorded elsewhere in this index, applied in the market where the affected population is largest. Held at B because no fairness testing, approval rate analysis or outcome data by segment is published, and the same signals also drive collections targeting and personalised prompts.

No guarantee, indemnity or correction process was located. The individual is better positioned than at most vendors here because the underlying framework gives them consent rights they can exercise and withdraw, which is genuine control over whether data flows at all.

What is missing is control over what is concluded from it: nothing describes how someone contests an income detection that understates their earnings, how a wrong financial health signal is corrected, or what recourse exists when a derived signal rather than a document determines a declined application.

The data chain is unusually visible because it is public infrastructure: inputs come through the regulated account aggregator network with orchestration across multiple aggregators, and distribution runs on two further named open networks, so a buyer knows exactly which rails the platform depends on and who governs them. That is a stronger position than a proprietary data arrangement because the rules are published. What is not disclosed is any individual aggregator partner, any model provider behind the analytics, or the base models for the finance specific language models in development.

Multi aggregator orchestration is the substantive integration, routing across several account aggregators rather than depending on one, which matters because coverage and reliability vary between them and a single connection would leave gaps. Distribution extends through two further open network rails with pre integrated lending solutions, so institutions reach new segments without separate build. A customer describes the orchestration as seamless to integrate. No named core banking, loan origination or aggregator partner appears, and no developer documentation was located.

No hosting provider, region selection, residency commitment or private deployment option was located. Operating entirely within one country under its central bank framework and data protection statute implies domestic processing, and the national regime has specific expectations about financial data localisation that the company does not address despite naming compliance with the statute itself.

No pricing, packaging or basis of charge was located. The platform spans data orchestration, analytics and distribution rails, which are different commercial objects, and nothing indicates whether charge falls per data fetch, per decision, per institution or by volume. For infrastructure whose economics depend on per consent retrieval costs, that is the material question.

Buyers span banks, non bank finance companies, insurers, wealth managers and fintechs, which covers most of the domestic financial sector, and functional coverage runs across underwriting, collections, advisory, servicing and product distribution. Both consumer and small business segments are addressed.

The constraint is geographic and structural: this is built on one country's regulated data infrastructure, so the platform is not portable to markets without equivalent rails, though it is deeply matched to the one it serves.