Wiserfunding
Wiserfunding scores small and medium enterprises, and its distinguishing asset is provenance rather than technology. The company was co founded in 2016 by Professor Edward Altman, who created the Z Score bankruptcy prediction model in 1968, together with Dr Gabriele Sabato, and the product is the direct descendant of that work: an SME Z Score adapted from the original for smaller companies, expressed alongside a bond rating equivalent so that assessments are comparable across industries and markets, a twelve month probability of default, loss given default, debt capacity and a suggested commercial credit limit.
The adaptation is published rather than proprietary in the usual sense. Papers by the founders and their academic collaborators on SME credit scoring, on modifying the Z Score for smaller firms and on low default portfolios sit in the peer reviewed literature, which means the methodology behind the score can be read and criticized by anyone rather than taken on trust.
Inputs run wider than accounts. The platform analyzes more than 100 financial metrics alongside non financial and macroeconomic signals drawn from public, private and unstructured datasets across more than 45 countries, with the non financial assessment covering intangibles the company names as corporate governance, management capacity and macroeconomic outlook. Machine learning is described as woven into the platform to keep the models current rather than as the basis of the score itself.
Beyond scoring, the platform handles portfolio work: screening, underwriting, monitoring with proactive warning indicators and customizable alerts, peer comparison, and a portfolio wide view of score distribution overlaid with exposure to show expected loss. Bespoke risk modeling is offered, and one customer describes tailored loss given default models built to satisfy a United Kingdom regulatory requirement.
Customers are described by type rather than name, spanning neobanks, asset managers, embedded lenders and invoice financing marketplaces. Headquartered in London with incorporation in the United Kingdom and Italy, roughly 20 staff, and 3 million pounds raised from BGF.
Capability Axes
Capability grades
15 of 15 axes rated · 4 graded A or B
The scores are the company: an SME Z Score, bond rating equivalent, probability of default and loss given default, with portfolio features arranged around them. The flagship descends from a classic discriminant analysis model, a formula with coefficients rather than a learned function, though machine learning is said to keep the platform current.
The platform hands scores, limits and probabilities to a lender who decides, with no automated approval, decline or pricing described. A customer calls it a third lens for validating internal thinking, but the vendor never says whether it would object to a score wired straight to an automated decline.
Peer reviewed papers by the founders and named academics set out the approach, so a model risk function can read the method and its criticism directly. Bond rating equivalents map onto a scale credit committees know, but no discrimination statistic, validation result or drift disclosure covers the models in production today.
Every published testimonial is unattributed, and the only client count, 30, sits in an undated milestone. An invoice financing marketplace and an embedded lending platform are named relationships, and a 3 million pound Series A shows a funded business, but no outcome is measured.
Publishing the methodology in peer reviewed journals exposes its assumptions to reviewers with no commercial stake, an external check no internal process replicates. No model card or evaluation method covers the machine learning said to update scores, and nothing describes handling of a lender's ongoing portfolio feed.
Scoring companies from largely public registry and filing data avoids the consumer dossier problem, since no private individual is profiled. Governance and management capacity judgments still concern named directors, and no privacy content or reference to European data protection law appears.
The company publishes nothing on security: no certification, attestation report or contact. With no named customer, nothing shows any demanding party has examined controls that would hold a lender's live portfolio exposures.
A customer testimonial describes tailored loss given default models built so its risk function could meet new United Kingdom regulator requirements, demanding work entrusted to the vendor. That is service rather than standing, and despite European incorporations no position on the EU AI Act appears, though credit assessment is designated high risk under it.
A limited company has no protected attributes, but the management capacity assessment judges the people running it from unstructured sources across very different disclosure norms. If that assessment travels badly across countries it disadvantages founders in some markets while looking objective, and nothing describes how it is derived or tested.
Terms of service, warranty, liability cap and service level are all unpublished. Nothing says what a lender is owed if a probability of default is misstated or a monitoring alert fails to fire on a deteriorating borrower.
The score's lineage traces to a published 1968 bankruptcy model adapted for small firms by its own creator with academic collaborators, all in peer reviewed journals a buyer can read. Registry, filings and financial statement suppliers across dozens of countries are never named, so coverage quality by market cannot be judged.
Two partners embed the scores in their own products, evidence that an integration path works. No interface documentation, sandbox or supported system is named, so latency and batch options are unknown before contact.
Hosted delivery comes with no provider, region or residency statement. Registry data from markets that restrict its processing, and live client exposures held for monitoring, make placement a supervisory question for a regulated lender.
A dedicated free trial page lets a lender test scores against its own portfolio, which for an analytics product is the real purchase decision. No price or charging basis is published, and whether bespoke models, consulting work by nature, cost extra is unstated.
Small and medium enterprises are the only subject, scored for traditional and alternative lenders, neobanks and embedded lenders. Data covers more than 45 countries from incorporations in the United Kingdom and Italy, yet no institution is named in any segment.
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The index publishes no overall score or ranking. See what we assess and the methodology.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
| Entry Price | Pricing Basis | Data Protection Terms | Implementation | Source |
|---|---|---|---|---|
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Not published. No price, unit of billing, tier or contract term appears on any vendor surface; a free trial is offered
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Not published on any vendor surface. The platform combines scoring, covering an SME Z Score, bond rating equivalent, twelve month probability of default, loss given default, debt capacity and commercial credit limit, with portfolio functions spanning screening, underwriting, monitoring, proactive warning indicators, peer comparison and portfolio wide risk distribution against exposure. Nothing published indicates whether charging follows companies assessed, portfolio size, monitored exposures, seats, country coverage or a platform subscription, nor whether monitoring is priced separately from one time assessment, which is the natural commercial distinction in this category. | No tiered data protection terms are published. There is no public privacy policy content, data processing description or retention schedule, and no subprocessor list or security credential is published either. The subject scored is a company rather than an individual, which materially reduces personal data exposure. The exception is the non financial assessment covering corporate governance and management capacity, which concerns named directors and officers. For the monitoring product a client's live portfolio exposures sit with the vendor on an ongoing basis, and the handling of that data is not described. | No implementation, onboarding or professional services fee is published. The platform is described as software as a service easily integrated into existing systems, and the company emphasizes immediacy of use, describing analysis of more than 100 financial metrics and multiple non financial sources across more than 45 countries in seconds and presenting the resulting metrics on a single page, which suggests the base product is usable without a build. Two partnerships demonstrate deeper integration where a customer wants the score inside its own product rather than in the vendor's interface, though the effort involved is not described. Optional work sits alongside the platform without published terms: bespoke risk modeling and customized alerting are offered, and a customer describes tailored loss given default models built for regulatory compliance, which is a scoped engagement rather than configuration. Support is described through in house financial experts available to clients. | Vendor Published |
The company's site, platform and product pages and insights archive publish no price, unit or tier, and third party directories add none. The most useful published commercial feature is the free trial, which has its own page. A trial matters more for an analytics product than for most software, because the purchase decision turns on whether the scores separate good borrowers from bad on the buyer's own portfolio.
With no accuracy figures published, a trial is the only way to establish that. Two costs sit outside whatever the subscription covers, and neither is described. Bespoke risk modeling is offered as an option, and at least one customer commissioned tailored loss given default models to meet a regulatory requirement, which is consulting work.
The underlying company data across more than 45 countries depends on registry and financial data providers, and nothing published indicates whether that cost is bundled, passed through or varies by market coverage.