Amsterdam fintech Duqu has raised €1.5 million to expand a working-capital platform built around automated credit assessment, while offering the underwriting technology to banks, leasing companies, and other lenders facing the same cost problem when evaluating smaller business applications.

The pre-seed round comes from Curiosity VC and No Such Ventures. Duqu advances money against outstanding B2B invoices without requiring a business to sell the invoice or notify its customer.

The company has also developed an AI underwriting engine that it says automates around 95% of the credit-assessment process. The technology is being offered on a white-label basis, creating a second potential business alongside Duqu’s own financing product.

Small credit applications have a processing problem

Credit assessment combines data collection, policy checks, fraud controls, and judgement. Lenders need to understand the applicant, the underlying business, the quality of the receivable, repayment capacity, and whether the request fits their risk policy.

The economics can become unattractive when the amount being financed is small. Some of the administrative work required for a low-value facility resembles the process for a larger one, even though the revenue available to the lender is much lower.

Automation can change that calculation if more information can be gathered and checked without manual processing. Duqu’s proposition is that smaller transactions become commercially viable when the marginal cost of reviewing another case falls.

The company says its platform has attracted more than 1,400 users and several million euros of applications during its early months. Those figures remain small within the wider lending market, but they provide real transaction data on which its underwriting systems can operate.

Automation does not remove credit risk

A faster decision does not make an invoice more likely to be paid. Automated underwriting still needs to detect fraud, weak debtors, disputed invoices, deteriorating businesses, and cases that fall outside the lender’s intended risk appetite.

The challenge becomes more complicated when the technology is sold to other financial institutions. A bank may have different lending thresholds, customer segments, compliance rules, and risk policies from Duqu itself.

Its white-label proposition therefore depends on expressing the lender’s rules through the system rather than imposing one generic credit model.

Financial institutions also need an audit trail behind automated decisions. They may need to establish which information produced an approval or rejection, how exceptions were handled, and whether source data was correct.

A system that processes applications quickly but cannot support those operational requirements may simply move work from underwriting into compliance, complaints, or remediation.

AI moves into the machinery of lending

Duqu sits within a wider shift in financial technology as artificial intelligence moves away from customer-facing assistants and into document extraction, fraud checks, affordability analysis, transaction categorisation, risk assessment, and policy enforcement.

The productivity benefit is less visible than a chatbot, but potentially easier to measure. If the cost of processing a credit application falls, lenders can address transaction sizes or customer segments that were previously uneconomic.

For businesses, faster working-capital decisions can narrow the gap between completing work and being paid, particularly where customers routinely take weeks to settle invoices.

The crucial measure will be performance over time. Duqu’s claim that 95% of assessment is automated describes process efficiency rather than credit quality. Defaults, fraud, economic downturns, and difficult edge cases will determine whether the underwriting remains dependable.

The €1.5 million round is early-stage capital, but the company is targeting a practical use of enterprise AI: reducing the administrative cost of making a credit decision rather than attempting to replace the relationship between lender and business.