Depending on their legal form, many enterprises such as family-owned SMEs and start-ups, unlike large corporations, do not legally need to disclose financial statements to the public. These entities therefore may not be fully covered by large credit rating agencies and trade insurers and may not have a credit scorig due to lack of accounting figures or hard facts.
For this reason, companies who want to target these legal entities (that may well go on to become larger accounts), have no other choice but to research qualitative information (soft facts) to assess creditworthiness before making a decision to do business.
In this scenario the quality of available soft facts are correlated with more accurate assessments to optimise credit limit decisions. This helps to limit smaller credit losses, especially in the case of start-ups, since studies consider their first three years of business existence as the most critical ones before assuring business continuity.
Understanding the differences between hard and soft information
In the context of business transactions, hard information refers to the quantitative data available in disclosing financial statements such as assets, liabilities, own equities, revenue development, profitability and cash-flow. Soft information is much more qualitative and refers to intangibles such as:
- Human capital: ownership and management structure, skills and reputation.
- Market: size, development, position, competitors.
- Industry: growth, entry barriers, concentration, economies of scale.
- Company strategy: diversification of customers and suppliers.
- Payment moral/character from owners and managing directors.
- Business continuity and governance, particularly in family owned companies.
- Social networking contacts to access to technical and human resources.
Soft information contributes significantly to improving rating models and to gaining more insight of the probability of default, complementing available hard facts and predicting payment behaviour, not just by extrapolating ‘backward-looking’ quantitative data.
For this reason, soft facts are a very valuable source for looking forward and can be integrated in scoring models to rate not only start-ups and SMEs with no disclosure obligation, but all kinds of legal entities at any stage of their business cycle.
In addition to these soft facts, data coming from business social media sources throughout online-channels, such as business networks (LinkedIn, Xing), microblogs (Twitter), video platforms (YouTube) and social networks (Facebook), also gains weight in soft facts as risk factors. Although using social media for credit decisions on the consumer side goes against equal and fair credit opportunity rights, there is no restriction for companies to evaluate this data for business credit considerations. For this reason, this data is becoming very valuable to rate business partners. It is not surprising that some leading credit rating agencies have started to use social media as a data source to enhance predictive analysis and help companies to get a more complete picture of their business partners, behind the figures.
However, despite being forward looking, social media information is based on user-generated content and is subjective and highly dependent on the environment in which it is processed. Furthermore, it can be easily manipulated with fake data. The challenge now is how to identify trustworthy sources, based on credible user’s authentication, profile and usage history in order to get useful information.
Full SAP integration with Risk Management tool
Risk Management solution from SOA People enables all kinds of soft data integration within your own automated credit scoring and decision workflow process. The solution is a software component fully integrated in the SAP Credit Management Suite platform and seamlessly integrates with SAP ECC6 and SAP S/4HANA, running with your SAP-HANA database to optimise data processing performance.
With the tool you can integrate soft data with highly configurable scorecards, as Risk Management can handle, weigh and monitor soft information as well as hard data to deliver founded credit scores and credit limit recommendations.
Risk Management integration in the corporate finance and accounting process within SAP also enables you to monitor continuous changes in customer’s payment behaviour, revenue trends, outstanding debts and incoming credit needs.
Risk Management can also be used with Credit Information Management to interface with reputable credit bureaus such as Bisnode/D&B, Creditsafe, Creditreform, CRIF/Skyminder. This enables instant access to additional hard facts like public accounting statements and qualitative information, as well as soft facts such as ownerships, managing directors, business connections and industry analysis. With both software components companies can track and continuously rate hard and soft data allowing them to trigger early warnings to any relevant changes and giving them time to react.
The scorecards in Risk Management are configurable and structured, and based on different attribute groups containing soft or hard data such as credit agency information‚ payment pools, payment performance, financial key figures, social media reputation, business and professional know how, industry indicators and sales force surveys.
Risk Management allows you to create all kinds of attributes for soft data evaluation through definable surveys containing questions and predefined answers (multiple choice), which can be easily mapped into the system.
Surveys can be assigned to specific attribute groups in the scorecards and permissible answers can contain numerical values as well as predefined and free texts, which can be transferred directly to the scorecard as scoring influencer input or just as a pure information item. The sum of each punctuation related to all ticked answers in the survey can be integrated into the scorecard and weighted accordingly together with hard data.
Some attribute groups can be set to evaluate different kinds of soft data sources. For instance, an attribute group such as social media reputation can consist of a single attribute or a survey such as followers in social media, with questions like number of followers on LinkedIn, number of followers on Facebook, each one with different punctuations. Other surveys in the same attribute group can tackle publications in social media or business connections in social networking.
An attribute group such as business and professional know-how can refer to single attributes or surveys such as quality business certifications available. Another attribute group based on sales forces surveys can be composed by surveys about maintenance facilities and transportation trucks, work climate perception and so on.
Every single attribute or survey can achieve maximum punctuation and their corresponding attribute groups can also be weighted differently in the final calculation process in order to deliver the final score, which is correlated with a creditworthiness rating.
Depending on the customer status, for example new or existing, legal form, locations, branches, credit segments and any available and up-to-date internal and external data sources in Risk Management , the system dynamically assigns a scorecard to the business partner with an specific weighting structure and scoring methodology.
As a result, new customers such as start-ups or SME with no obligation to disclose financial statements and showing a lack of hard facts can be rated by mainly considering attributes such as soft facts and sales force surveys. On the other side, existing customers with whom own payment experience and other accounting figures are available can be rated with a scoring model assigning a higher weight in hard data figures.
With this tool companies gain a systematic evaluation of all kinds of soft facts with predefined surveys, check lists and questionnaires. As soon as a new soft fact flows into the score card in Risk Management, the event can trigger a new scoring which can downgrade or improve the creditworthiness rating and affect the current credit limit recommendation, initiating a decision workflow.
Focusing on social media data, as long as more complex algorithmic and semantic predictive models like Big Data are able to be further standardised and interfaced to more information technology systems, companies using Risk Management can rate all kinds of meaningful soft facts in their risk management in a very comprehensive, flexible and effective manner. It provides full insight into the financial performance of business partners looking forward and predicts hard facts and business failures while staying technologically ready for the future with SAP Leonardo.