Credit checks and credit ratings are established tools for assessing and communicating credit risk before credit is granted. They give an indication of a company's financial position and ability to pay, based on the available information about the company. Fraud, however, is characterised by the manipulation and falsification of information. Credit scoring models can therefore produce misleading results when they rely on data the company has supplied itself, data that may be incorrect or falsified. At a time of rising economic crime and financial uncertainty, credit checks and credit ratings alone are not enough. Two factors in particular weaken their effectiveness.
1. The use of outdated information
Credit checks and credit ratings are largely based on historical data, with the financial statements as one of the main sources. Financial statements are normally published only once a year, typically around the summer, so the information underpinning the assessment will often be at least six months old, and in many cases up to 18 months old.
Enin's analyses show that even where the financial figures suggest ongoing operations, a closer review of other key factors such as the number of employees, changes in management or structural conditions can reveal that the company has in reality been transferred to new owners, and therefore in practice is an entirely different business from the one the accounts reflect.
In a credit assessment context, this opens the door for operators to secure financing on the basis of historical accounts that represent the situation before the change of ownership, even though the real business today is weakened or has ceased altogether.
2. Reliance on self-reported information
Credit checks and credit ratings rest on a central assumption: that the information companies report themselves, and the annual accounts in particular, is reliable. In practice, experience shows that fraudsters can systematically manipulate or fabricate accounts in order to appear far more solid than they really are.
One telling example is Nannestad Pizza, which filed almost identical accounts to seven other companies. A closer analysis revealed extensive manipulation of the figures for both 2021 and 2022. The changes were strategically small enough to avoid the statutory audit requirement, yet large enough to present artificially improved profitability. The audit requirement currently applies to companies that exceed two of the following three thresholds: NOK 7 million in sales revenue, NOK 27 million in total assets, or 10 employees. From 1 July 2024, the thresholds rise to NOK 9 million in sales revenue, NOK 39 million in total assets, or still 10 employees.
By staying below these thresholds, many companies avoid an external audit and can therefore file incorrect accounts without it being discovered. In the case of Nannestad Pizza, security was taken over the company after the accounts were changed, and attempts were made to smuggle assets out via border crossings between Turkey and Iran. A comparison with the industry benchmark showed that while the 2020 accounts reflected an ordinary pizza restaurant, in 2021 the company appeared highly profitable on the basis of fabricated figures.
This example underlines how important it is to supplement traditional credit assessment tools with independent data sources, advanced analytics and automated risk detection in order to uncover discrepancies that a standard credit check does not reveal.
Advanced fraud calls for advanced methods, and they are available to you
New indicators of disreputable operations and criminal activity can be hard to detect with a traditional credit assessment. Identifying irregularities requires extensive analysis of a wide range of information sources, a process that is both time-consuming and resource-intensive. Many cases of accounting manipulation are also far more sophisticated than the example above.
To make this "detective work" more efficient, several providers have developed tools that can identify patterns and signals of fraud in companies that would otherwise be well rated by traditional credit checks. We are one of them, and our tools can systematically pick up indicators of disreputable and criminal activity in companies. Our solutions make it possible for our users to uncover risk quickly and efficiently, without having to spend large amounts of time on manual data collection, pattern recognition and documentation.
You can read more here about the information we at Enin provide to support banks and businesses in their anti-fraud work.
