Credit Scoring Software

Who it is for and what it solves

In many companies risk assessment lives in spreadsheets, in rules scattered across the application system and in analysts' heads. Basic questions then become hard to answer: why was this application declined, which rule version applied in March, how did portfolio quality change after the cut-off was lowered? A scoring system brings order to the process and leaves a trace of every decision.

Buyers are teams with named legal, risk and data owners, including:

• consumer lenders, leasing companies and BNPL platforms;
• factoring and invoice finance providers for SMEs;
• distributors and wholesalers setting trade-credit limits for B2B customers;
• fintechs embedding finance into their platform together with a licensed lender.

Assessing businesses for trade credit is usually regulated differently from consumer lending, so we define the regulatory scope during discovery together with your adviser.

System modules

Data validation

Checks for completeness and consistency of the application, detection of duplicates and suspicious patterns (for example many applications from one device in a short time), and an explicit policy for missing data.

Score calculation

Three layers kept separate in code and configuration: knock-out rules from the credit policy, the scoring model (a logistic-regression scorecard or a gradient-boosting model) and the strategy — approval cut-offs and limits. The result is deterministic: the same inputs and the same model version always produce the same outcome.

Reason codes

Every decision carries the main factors behind it — from the scorecard or from model explanations such as SHAP values. They form the basis of the explanation for both the applicant and the analyst.

Review and override

A queue of applications for manual assessment, four-eyes approval above a limit, overrides allowed only with a reason code, appeal handling and override statistics — a high override rate signals a problem with the model or the policy.

Performance and fairness monitoring

Population stability (PSI), approval rates, repayment performance against expectations once outcomes mature, and outcome analysis across groups where the law permits it. Alerts go to the model owner.

Versioning and champion/challenger

A registry of models and rules with change and approval history, plus the option to run a new version in parallel on part of the traffic or in shadow mode.

Data and integrations

• Authorised data sources — credit bureaus, account data shared with the applicant's consent through a licensed open-banking provider, company registries and tax registers.
• Application system and CRM — forms, onboarding, customer data and relationship history.
• KYC/AML — identity and sanctions checks before risk assessment.
• Documents — reading statements and certificates with OCR document recognition.
• Case management and loan system — handing over the decision, limit and terms.
• Model registry and reporting — for example MLflow and a data warehouse for portfolio analysis.

Every source needs a lawful basis and a contract. The specification records which data may be used in the model and which only for verification.

Architecture and technology options

The core is a decision service with an API: it receives the application, retrieves data, calculates the score and returns a decision with its explanation within the time the sales process allows. Models usually run in Python (FastAPI) or are exported to ONNX or PMML to separate the training environment from production; policy rules can run in .NET, Java or Node.js.

Risk analysts get a console for configuring rules and cut-offs, with versioning and change approval. Data lives in PostgreSQL, and every decision writes its inputs, model version, outcome and user actions to an immutable log. Portfolio re-scoring runs in batch mode.

Compliance and service boundaries

Sensitive data handling, model changes, adverse outcomes, explanations and human oversight are designed together with specialists on your side. The specification most often addresses:

• GDPR, including Article 22 on decisions based solely on automated processing;
• the AI Act, which classifies AI systems used to assess the creditworthiness of individuals as high-risk, with requirements on data, logging, transparency and human oversight; obligations apply in stages;
• consumer credit rules and the EBA guidelines on loan origination and monitoring;
• outside the EU, for example the US ECOA and FCRA and the UK Consumer Duty.

The service covers software engineering only. Lending and credit decisions require separate authorisation and legal review, and model validation is performed by your risk function or an independent validator.

Delivery process

1. Discovery — decision and jurisdiction assessment: which product, who decides, which data is available and permitted, what explanations are required.
2. Specification — data dictionary, feature definitions, model interface, policy rules, manual review flow, acceptance criteria.
3. Design — analyst console, review queue, decision explanation view.
4. Build in iterations — data retrieval and validation, scoring on the approved model, reason codes, manual review, monitoring.
5. Testing — back-testing on historical applications, comparison with the current process, performance and security testing.
6. Shadow mode — the system calculates decisions alongside the existing process, with no effect on customers, until the risk owner accepts the results.
7. Launch and maintenance — staged cut-over, controlled model changes, integration updates.

The timeline depends on the number of data sources, availability of historical repayment data, explanation requirements, the complexity of manual review and the length of shadow mode.

Price and options

The indicative budget is from $12,000 excluding VAT for a focused release, such as a decision engine for one product with two data sources, reason codes and a review queue. The price is shaped by:

• the number of data sources and credit products;
• rules with a scorecard versus a machine-learning model;
• real-time versus batch decisions;
• explanation and reporting requirements;
• the depth of the analyst console and monitoring;
• the number of markets and languages.

Discovery ($490–$1,500) delivers a data dictionary, a jurisdiction assessment and a proposal in three options: Essential, Recommended and Scale. Developing a new model with data scientists can be quoted separately. Credit bureau query fees, open-banking and KYC services, independent model validation and legal opinions are excluded. Maintenance starts from $249 per month; the full list of anchors is on the pricing page.

What the client provides

• the credit policy, cut-offs and knock-out rules;
• historical applications with repayment outcomes, pseudonymised where possible;
• the current scorecard or model with documentation;
• the lawful basis for processing and contracts with data sources;
• named owners for risk, data and legal matters;
• acceptance criteria for shadow mode.

Scoring often works alongside financial dashboards for portfolio analysis, payment applications that collect repayments and ETL connectors that feed the model with data. The category overview is on the fintech software page, and you can submit a project via the contact form.

Related services

Frequently asked questions