Term

Credit scoring

An automated points-based assessment of a client's creditworthiness using income data, repayment history, credit register records and other factors. Banks and leasing companies use it to decide on approval and pricing.

Updated

What it means

Credit scoring is an automated, points-based assessment of a client's creditworthiness using income data, repayment history, entries in credit registers and other factors. The resulting score helps a bank, leasing company or instalment seller decide on approval and pricing.

A scoring model assigns points to individual characteristics according to how strongly they correlate statistically with default risk. Unlike the individual credit rating of large companies, scoring is quick and cheap, so it is used mainly for consumer credit, small firms and sole traders.

Besides banks, scoring is used by suppliers deciding on payment terms, credit insurers and telecom operators. Business scoring may include data from financial statements, payment behaviour and commercial register entries.

How it works in practice

A business owner can improve their score by paying obligations on time, keeping income stable, filing financial statements promptly and maintaining sensible leverage. Records of late payments remain visible in registers for some time after repayment and affect the assessment.

Before applying for a loan, it pays to check one's data in credit registers and correct any errors. A wrong entry, such as a repaid loan that was never closed, can needlessly lower the score and lead to rejection.

A company that grants payment terms itself can set up a simple internal scoring of customers: payment history, outstanding receivables, financial statement data and industry. This helps set credit limits and reduce credit risk.

What to watch out for

Scoring is profiling under the GDPR. Where a loan decision is made solely by an automated system, the person has the right to human intervention, to express their view and to an explanation of the decision's logic. European case law has strengthened these rights with regard to external scoring agencies as well.

Models can contain biases that disadvantage certain groups, for example by place of residence or length of employment. Providers must ensure scoring is not discriminatory, and clients should know which factors led to a rejection.

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