When applying for an installment, credit card, or loan application, you may sometimes wonder how your own creditworthiness assessment will be conducted. In short, creditworthiness is estimated as the sum of many factors, most often the revenue / expenditure ratio. If your credit institution estimates your spending is too high for your income, no credit will be granted.
Traditionally, the assessment of creditworthiness has been made by looking at the financial situation of each applicant and the resulting decision to grant a loan or credit. Digitalisation has increasingly provided automated solutions that have significantly accelerated the processing of loan applications. Since there is no positive credit register in Finland for the lender to find out the borrower’s existing loans, each credit institution uses slightly different methods.
However, creditors are not entirely free to decide how they assess and determine their creditworthiness. The European Banking Authority (EBA) has issued guidance on the assessment of creditworthiness. The supervisory authority in each country is responsible for following these guidelines and in Finland the name of the supervisory authority for credit institutions is the Financial Supervisory Authority. In Finland, credit rating is also regulated, for example, by the Consumer Protection Act and the Financial Supervisory Authority’s standard on credit risk.
Credit institutions shall consider the following three points in their credit assessment:
- Statement of income of the applicant
- Detection of false information
- The ability of the consumer to meet the payment obligation now and in the future
Statement of income of the applicant
In order for a credit institution to be able to carry out an assessment of an applicant’s creditworthiness, the credit institution must request sufficient information on the applicant’s creditworthiness. If the applicant’s income is fluctuating, the credit institution must ensure that the loan can be repaid even with fluctuations in income.
Detection of false information
In order to determine creditworthiness, loan documentation must be formatted so that the applicant, the lender and the potential loan intermediary (eg peer loans) can easily identify false information. Providing false information is not beneficial to anyone in the loan process, so identifying and preventing false information increases the security of borrowing for all involved.
Also read: What to consider when applying for a loan?
The ability of the consumer to meet the payment obligation now and in the future
In addition to income, the lender must ensure the borrower’s ability to meet his payment obligation, ie repayment of the loan. Solvency is affected by such things as other loans, default on other payments, commitments, necessary expenses, and any taxes and insurance that may affect your solvency.
The creditworthiness assessment must also take into account future solvency: how a potential increase in reference rates or anticipated changes in income, such as retirement, will affect creditworthiness and thus creditworthiness.
Although the consumer should be extremely vigilant when applying for a loan, the loans granted by the lenders are also monitored. Laws and regulations make the loan world safer for all parties.
Creditworthiness assessment shall be non-discriminatory
In April 2018, the Equality and Gender Equality Board prohibited credit institutions from making credit decisions based on discriminatory statistical methods. This was a case where an online credit application was rejected on the basis of statistical risk without a personal assessment of the borrower’s creditworthiness.
The credit institution had an automated creditworthiness assessment based on statistical risk. The borrower’s gender, mother tongue, age and place of residence were compared with the corresponding statistics on the basis of which the creditworthiness was assessed. If the applicant had been a woman instead of a man, or had her mother tongue been Swedish, the same loan application would have been approved. In addition, the applicant had not been asked for income or wealth information, so the decision to reject the credit application was based solely on statistics. The Equality and Gender Equality Committee saw this as problematic and discriminatory.
The Equality and Gender Equality Committee
Does not see the use of statistics and artificial intelligence as problematic as there is often justification for their use, but neither should statistics be used in a discriminatory manner. This is the first legal solution concerning discrimination in automated lending. Since the penalty payment is set at EUR 100,000, we can assume that discrimination will not continue to be seen in the assessment of creditworthiness.
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