The 2017 edition of the CreditExpo is already several weeks behind us. What did we discuss during this last edition? Time for a brief review of our findings and shared knowledge.
The Credit Manager won't exist in 20 years
A sentence from my presentation that I had the privilege of giving as part of the CreditExpo 2017, both in Belgium and in the Netherlands. During the presentation, I discussed the changing playing field of the credit manager. Aspects of this are changing laws and regulations, the economy, which is moving from a user economy to a sharing economy, artificial intelligence and of course the endless growth of new data.
So why will there be no credit manager in 20 years? This is actually an extension of the current 4th industrial revolution. Processes are automating, machines - think chatbots - are taking the place of people. Credit management processes can therefore be artificially defined and taken over by machines. I see much more of a shift:
'From credit management to data management'
The credit manager's access to data is becoming increasingly easier and he can take advantage of this. However, he must ensure that the right data combination is used. In other words, the data must contain information that is relevant. As we are living in a 'golden data era', I dwelled on this central topic of my presentation: the growth of data explained in 2 datasets:
In a well-organized database neatly structured and easy to use. Examples are your ERP and CRM system or an external database such as Dun & Bradstreet. However, structured data is always overshadowed by ROTte data. In this case ROT stands for Redundant, Obsolete and Trivial. Uitdaging is dus bij gestructureerde data om deze schoon en inzichtelijk te houden.
Since the awakening of the Internet and "connected" data, the growth of unstructured data has taken off. Add in social media and the fact that there are currently as many people with access to the internet than lived on earth 1960, (3.x billion) and the data revolution is unstoppable.
Problem is, as the name implies, the data is not structured. Challenge is therefore to structure the data so that this data set can also provide insights for example for the Credit Manager. Common problems include:
- Is the data even realistic?
- Who created the data?
- What emotional value can be attached to the statements or fragments of text?
The impact of unstructured data obtained from social media
So it remains a challenge to extract meaningful information from the growing supply of data, not only for data service providers but also for you as a company or credit manager. This slows down the development of new data sets for use during credit approval.
However, there is a core of information in the unstructured data and it is well advised to do something with it as well. Take for example social media monitoring. I showed some examples of websites or tools that enable social monitoring here during the session, including:
- Google Alerts
- Social mention [Link]
- D&B Credit
The last one might be an odd one out, but we as a data service provider are also experimenting with unstructured data. So in our new platform D&B Credit there is also a 'Web & Social' section containing social information about a business relation, which we add to the credit report.
The future of unstructured data and decision models
For now, this section has no rating of its own and is for information purposes only. Also the coverage is limited to the top 200,000 companies in a country, but it does indicate that we too want to move forward with unstructured data into the future.
In short, the Credit Management profession will change because more and especially unstructured data is readily available, but also because process automation is becoming increasingly sophisticated. Besides devising the rules for automated decision models, reliability and quality of data sets are the new spearheads of the credit manager. Data and digitalization will only increase in the future. So now the question is: how does one deploy the right decision models in the future and by what data is the model driven?
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