Christian Steiner
Alternative sources of information for better credit management?
Currently, the term “alternative facts” is predominantly negative in the wake of politics – and the non-word of 2017. The advisor to US President Kellyanne Conway was the first to use this term. If Team Trump were to claim things that are proven false by all the media, it would not be lies or untruths, but rather “alternative facts”. What exactly is meant by this is still being talked shop about today. In principle, it is also left to the colleague. In the following, this article will take a closer look at alternative information and the evaluation of the respective sources for credit management. Once it has been clarified what exactly is involved, can this information be profitable for operational activities up to and including strategic decision-making? If so, what is the organisational and procedural approach to obtaining and using the alternative information? In principle, this article deals with the use of different sources of information with the aim of making the right decisions in a cost-effective and time-efficient way. The data strategy represents a major challenge for credit management today, especially for future prognoses regarding the potential and risks of new and existing customers.
The credit manager becomes the data procurement manager.
Basically, the idea of adding new sources to the usual “mainstream” information – which includes the classic credit information regarding companies and consumers – seems quite charming. But which sources of information can we trust in Credit Management? This question seems to be very difficult to answer, considering that today more than 500,000,000 tweets are sent worldwide every day and more than 2 billion active Facebook users are constantly generating a huge amount of data. Our world has changed completely. Years ago, a credit manager started the day by taking relevant news from his customers early in the morning from the most important newspapers. Most of these articles and other news items are now available online. There is no doubt that there is extremely relevant data on the Internet for the evaluation of companies, which helps to identify payment difficulties up to the risk of insolvency or even sales potential as early as possible. The challenge lies in finding this ‘gold’ in the hodgepodge of rocks – a modern credit management has to deal with this task in a very targeted manner.
The idea is basically to supplement existing data and in certain situations to make decisions based on faster and cheaper data. In this sense, the Credit Manager also becomes the Data Procurement Manager. To establish targeted measures, the right suppliers with the best prices and the right quality must be identified. In addition, an appropriate strategy is needed to determine which data should be used for which customer groups and processes. In this context, it must always be questioned to what extent the creditworthiness information of classic data providers is sufficient for coping with everyday tasks (or perhaps can even be replaced?)
Classical vs. alternative data sources.
But how is a distinction between data sources made and which are the alternative providers? Without a doubt, the most trustworthy data are the own experiences (e.g. many years of payment experience). This is why they are and will continue to be a major factor in influencing decisions in credit management. However, in order to make a valid prognosis for the future with regard to the potential and risks of new and existing customers, further meaningful information is required. Creditworthiness data from credit agencies is considered a reliable source. Ratings from trade credit insurers also provide a useful information base, which has the great advantage that the provider can counter the rating with its own liability sums. In addition, there are payment experience pools (e.g. the Debtors Register Germany by Creditreform), which have the advantage of being relatively inexpensive and, especially in the case of larger companies, offer a good mix of balanced and reliable data. They are also inexpensive and provide up-to-date information. However, the integration requires a considerable amount of administrative effort on the part of competent IT partners. In addition, basic data protection conditions must be clarified in advance due to the necessary registrations.
In addition to these established sources of information, there are now a number of other providers which do not usually focus specifically on credit management and are considerably cheaper. These are referred to as alternative information providers. First of all, company data for sales and marketing purposes is generally well suited to validating company addresses as part of the new customer process. If this is done automatically, for example via interfaces of the CRM and CM software, it saves credit management staff considerable time and administrative effort. Register information is available from almost all countries. In the meantime, a number of providers have specialised in making such data available in an edited form. Another approach is taken by providers who have specialised in so-called “crawling”. The simplest method for this is the evaluation of imprint data and websites of the respective companies. In this case, the company websites are automatically searched at regular intervals so that changes in the imprint or other structured information can be identified. For example, companies are usually very careful to announce a change of managing director or a change of address promptly in the imprint or in signatures, so that these adjustments can be quickly used for credit management. Other service providers specialise in monitoring supply chains and risks related to geopolitical or climatic aspects. Many companies in this country rely heavily on this information. For example, a crop failure in Brazil can have a huge impact on a food-producing company.
Social Media Monitoring – thumbs up.
“Social Media Monitoring” is a particularly modern form of alternative sources of information. A large number of providers use the latest technologies to filter relevant information about companies from the data volumes in social media, and to evaluate this information in real time. The large providers such as Google or Twitter, but also a number of smaller service providers who monitor closed forums and special industries are some examples. In this way, it is possible to find out whether negative attributes to a company or its products have occurred in the last days, hours or minutes. Credit managers can use this data to extract essential information for their operational activities. If something changes in a short time with one of their debtors (scandal, strike, court decision, accident), this may possibly provide information about a default or at least a delay in payment. There is no doubt, however, that this also involves a certain risk of manipulation. In terms of quality, new providers and unknown data are breaking new ground. Proven quality standards must be questioned. Attempts by companies or private individuals to spread negative news about competitors or to add value to their own news (also with the aim of manipulating share prices) have always accompanied economic events. Accordingly, important news which is an alleged indication of a risk of payment or insolvency must be systematically checked for accuracy. How can a company now prevent itself from becoming a victim of manipulative false information. An appropriate organisation at the beginning of the process is essential. In this way, it can be determined in advance which sources, media and authors are classified as serious and truthful. On the other hand, sources can be listed in a blacklist, which the credit manager – and only the credit manager – regards as fake news. In particular, if an important decision will be made in credit management on the basis of current news, the accuracy of this data should be ensured, e.g. by further research and second opinions. Here, a certain conflict of objectives arises, particularly with regard to the automated import of data. If information should appear quickly and automatically to the credit manager, part of the quality control is inevitably transferred to the data recipient.
Conclusion
For the credit manager, optimised data management always entails a quality control of the data used. Only in this way the many advantages of alternative information sources can be used. Modern credit management requires a strategy regarding the mix of information, the courage to question oneself and to continuously improve. Costs are an important factor in this regard, but not the only one. Modern CM systems can be expanded flexibly with various data sources. Based on this, scorecards are developed and simulations based on historical data are carried out to find the ideal setup for credit management.
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