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Lending decisions still wait on documents a person has to read.

In markets where most customers have never held a bank loan, bureau-based scoring excludes exactly the people who most need credit. The behaviour is already in the data. Turning it into a decision that is accurate, explainable and auditable is the engineering problem.

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Explainable AI credit scoring engine for mobile money and financial inclusion

Where the sector actually loses money

Credit exclusion is a data problem wearing a policy costume

Traditional bureau-based credit scoring works by looking at borrowing history. In markets where most customers have never borrowed formally, that method excludes precisely the population that mobile money and digital lending exist to serve.

The data to do better usually already exists. A mobile money platform holds years of real financial behaviour in its own wallet records: income stability, spending patterns, bill payment discipline, remittance flows. None of it appears in a bureau file.

The hard part is not prediction. It is building decisions that a credit committee, a regulator and an auditor can all trace, on every score, including the declines.

In one line

Algorizz builds explainable machine learning for lending: credit intelligence engineered to regulatory standards rather than black-box scoring.

Engineered to lending regulation, not to a demo

The engineering behind regulated credit decisions

Behavioural feature engineering

Transaction data converted into credit-relevant signals such as income stability, payment discipline and balance behaviour, so thin-file customers can still be scored.

Wallet dataBehavioural signalsThin-file

Explainability by design

Every score traceable back to the factors that produced it, so credit, risk and compliance teams can defend any decision including a decline.

Reason codesTraceabilityAudit

Enterprise ML infrastructure

Full model lineage and version control, so training and live scoring share identical definitions and any past decision can be reproduced for audit.

Model lineageVersioningReproducible

Validation and fairness

Measured on later data the model has not seen, back-tested against the historical book, fairness-tested in pipeline, and run in shadow before it touches a live decision.

Out-of-timeBack-testingShadow mode

Shipped and running, not pilots

What we build in financial services

Explainable AI credit scoring engine converting mobile wallet behaviour into credit scores

Capability

Explainable AI credit scoring for mobile money

Algorizz builds explainable AI credit scoring for mobile money lending, converting wallet transaction behaviour into calibrated credit scores so customers with no formal credit history can access credit safely.

Every decisionScore, risk band and reason codes returned

Thin-fileCustomers scored without borrowing history

Shadow firstNo live decision before validation

Explainable AIAlternative credit scoringFinancial inclusionModel governanceResponsible AI

10 minute read · Algorizz engineering leadership

BFSI & Financial Services

Frequently asked questions

Yes. Algorizz builds explainable credit scoring engines for mobile money lending, converting wallet transaction behaviour into calibrated scores with reason codes on every decision.

Same engine, different floor