MQube

The removal test leaves a three week manual process, which is precisely the baseline the company measures itself against. Document extraction analyses bank statements, identity documents and supporting paperwork automatically, decisioning covers affordability and underwriting end to end with offers described as issued in seconds, a large language model chatbot ingests a lender's own policy documents to answer criteria questions, and a valuation model draws on around 180 property data points to flag likely mismatches before a physical valuation is instructed.

A human checkpoint is described at the decisive moment, with the assembled case presented to an underwriter who checks it before an initial binding offer is issued, which matters because the offer is legally binding on the lender. The valuation model warns brokers early when figures look unlikely to hold rather than failing silently later, and the chatbot is offered with a sandbox so a lender can test behaviour against its own policies before deployment.

Held at B because one customer description says the platform automates the entire underwriting process, and offers are elsewhere described as delivered in seconds, so where the checkpoint sits in the fastest cases is unclear.

The validation approach is the strongest feature: the company ran its own regulated lender on the platform for years and the results were confirmed externally by industry body data rather than self reported, which is a live market test rather than a benchmark. The sandbox lets a prospective lender evaluate the chatbot against its own policies before committing, which is customer owned testing.

Held at B because no extraction accuracy, decision error rate or valuation variance figure is published, and the chatbot's performance is stated inconsistently across sources, at 90 percent of broker criteria questions automated in one place and over 40 percent of criteria queries resolved in another.

The evidence is unusual in kind and stronger for it: rather than citing pilots, the company built a regulated lender on its own technology and let the market test it. That lender reached more than 97 percent of customers receiving a decision within one day against a three week industry average, was confirmed by the industry trade body's data as the fastest growing lender in the country by end 2024, and reported over two billion pounds in applications and 1.3 billion in completions since launch.

External adoption is now named, with a building society's broker portal running on the platform and a major adviser network partnership in place. Backers include three large financial institutions alongside venture funds.

No boundary statement was located, and the structure makes the question sharper than usual. The company owns a mortgage lender that competes directly with the institutions it sells the platform to, so a building society running its broker portal on this technology is routing application data through a competitor's system. Nothing states whether client lender data is isolated, whether models learn across lenders, or what separation exists between the technology business and the lending business.

No data protection agreement, retention schedule, subprocessor list or deletion commitment was located. The platform ingests bank statements, identity documents and full financial circumstances for mortgage applicants, which is among the most complete personal financial pictures any vendor in this index handles, and none of the handling terms are published.

No attestation, certification, trust centre or enumerated framework was located. A regulated building society has connected its broker portal to the platform and a regulated lender operates entirely on it, so security assessment has been passed at a serious standard, and nothing is published for other institutions to examine.

The technology has been operated inside a fully authorised mortgage lender since 2022, which is a stronger position than most vendors here can claim, since the platform has been run under regulatory supervision in a live market rather than merely designed for compliance. Binding offers imply the affordability and disclosure standards that govern regulated mortgage lending. Held at B because no regulator, rule or handbook provision is named for the technology business itself, and a lender licensing the platform carries its own obligations that are nowhere mapped.

Mortgage lending is among the most consequential automated decisions any consumer faces and nothing is published about how the models behave across applicants. Automated valuation drawing on property data carries locational loading, since historical price patterns encode past investment and disinvestment, and affordability assessment from bank statement analysis can read irregular income, benefits or informal work differently from salaried employment. No fairness testing, outcome analysis by applicant type, or explanation of how declines are reasoned appears.

No guarantee, indemnity or correction process was located. The applicant is the affected party and is unaddressed: someone declined by automated affordability assessment, or whose property is valued lower than expected by a model reading 180 data points, has no stated route to an explanation, to correcting a misread bank statement, or to human reconsideration. The binding nature of the offer protects the customer who succeeds, not the one who does not.

No base model, provider, hosting arrangement or subprocessor is identified. Large language models are named as the basis of the chatbot and a retrieval framework appears in a conference discussion the company participated in, which is incidental rather than disclosure. Property data feeding the valuation model, and the sources behind bank statement categorisation, are likewise unnamed, and both determine how the system performs on unusual cases.

Deployment reaches into customer environments rather than sitting alongside them, with a building society's broker portal built on the platform and the chatbot offered as a plug and play component for lenders' internal systems, broker and customer facing portals and existing chat interfaces. A major adviser network distributes products originated through it. Held at B because no core banking, servicing, bureau or valuation provider system is named, and no developer documentation was located.

No hosting provider, region selection, residency commitment or private deployment option was located. The company states an intention to take the technology international, which would make residency a live question, and building societies and lenders handling applicant financial records would examine it during procurement.

No pricing, packaging or basis of charge was located for the technology business. The company does publish an unusual commercial claim about outcomes, describing its own lender as the lowest marginal cost originator in the market, which speaks to the economics the platform produces without indicating what a lender pays to license it.

Product coverage within its chosen market is thorough, spanning residential and buy to let, individual, limited company and portfolio landlords, affordability assessment, portfolio lending and product switching, and the buyer set reaches lenders, building societies, brokers and adviser networks.

The constraint is deliberate concentration on one product in one country, with international expansion and a tokenisation offering stated as intent rather than delivered, so breadth is bought nowhere and depth everywhere.