Honest from the first hello.

Hola Credit is designed for authorised Namibian microlenders and retail credit teams who assess applicants whose income patterns do not fit a salary-only model. If you lend to self-employed, freelance, or irregular-income applicants, Hola Credit can help structure the cash-flow evidence from their bank statements.

No. Hola Credit does not maintain a database of borrower credit histories, does not share information between lenders, and does not report to formal credit bureaus. It processes a single borrower-authorised statement for a single lender. Cross-lender shared borrower profiles are not part of the current product.

No. Hola Credit supplies evidence, confidence levels, and limitations. The lender adds their own notes, applies their own policies, and makes the final decision. That decision is recorded separately from the Hola Credit assessment, so the audit trail is always clear about who decided what and on what basis.

The assessment evaluates cash-flow evidence across seven components: income floor, consistency, volatility, trend, concentration, red flags, and data sufficiency. Each component captures a different dimension of the applicant's financial pattern, and each carries its own confidence level based on the quality and sufficiency of the underlying data.

The upload-to-assessment pipeline targets completion within sixty seconds at the defined percentile. This is a processing-time target, not a claim about decision quality or approval outcomes. Cases with low-confidence data are routed to a human review queue rather than rushed through.

The initial launch supports statements from FNB Namibia, Bank Windhoek, Standard Bank Namibia, and Nedbank Namibia. Format validation and extraction accuracy are measured separately for each bank.

PDF, PNG, and JPEG. PDF is always preferred because it preserves the original layout and text encoding, which makes extraction more reliable. Image-based uploads go through OCR as a fallback, which is less reliable than direct PDF text extraction.

Yes. The scoring engine uses versioned code with fixed rules. Given the same extracted transactions and the same policy version, the engine will always produce the same score. There is no randomness, no hidden state, and no model discretion in the scoring step. AI is used in the extraction and categorisation phase, but the scoring engine is a separate, deterministic process.

The income floor is the minimum reliable monthly income the statement can support. It is derived from consistent credit transactions, discounted for concentration risk and volatility. It is not a prediction of future earnings. It is a conservative measure of what the statement evidence can demonstrate. When data is insufficient, the income floor may be null rather than a potentially misleading number.

No. Predictive accuracy against repayment outcomes can only be tested after real pilot loans have seasoned. This process takes six to twelve months before the results are statistically meaningful. Hola Credit does not claim predictive accuracy before that data exists. The assessment should be treated as supplementary evidence, not as a validated predictor of repayment.