4 trends shaping the future of corporate credit risk management – continued

//4 trends shaping the future of corporate credit risk management – continued

In the last entry I described three trends that I think will shape the future of credit risk management in the company. These were: increasing significance of the prevention, individual approach towards each client, and “multi-roles” which modern Credit Manager will be (or already is) required to assume. While speaking of the last trend, I mentioned the roles of a coach (an „explainer” of influence of credit risk on financial results of the company as a whole), of a salesman (focusing on helping the company gain the maximal profit from the biggest possible closed sale), and of an optimist (noticing opportunities even in the direst situation). The following ones are no less difficult.

Probability calculator – it’s probably the most basic part. It requires knowledge of at least basic mathematically statistical techniques of credit risk assessment, including scoring models. Credit scoring (points-based system of assessment) of the client is a risk assessment technique, which allows to simultaneously identify (map out) risks and opportunities. Credit Manager uses this method to divide clients into groups and select the best ones in each risk group. On the other hand, he selects also the worst clients – ones who need most attention, precise prevention, and limiting adverse credit occurrences.

Guru (authority figure) – it’s a part which can’t be built right away, since it requires a lot of work on probably the most valuable feature of the Credit Manager, that is, trust. It helps to convince other departments such as marketing department or sales department, to the broader use of credit scoring in managing finances of the clients. It’s also a way to achieve clearer and more objective management of contractors in the company.

Bridge builder – it’s another part that requires developed soft skills. Modern Credit Manager will take on the role of a coordinator, since establishing clear communication channels between debt recvovery, marketing, and sales departments is necessary. It should prevent possible conflicts resulting in negative influence on financial flow and profits of the company.

Analyst – last but not least, it’s a part that should never be forgotten by modern Credit Manager. Building on the basis of predictive analysis, Credit Manager tries to predict future trends and dependences. It’s a source of knowledge about future behaviours of the clients for the company, and an opportunity to predict these behaviours.

Big data – don’t drown in … information

Market of data analysis is developing six times faster than entire IT sector. It has been estimated that during the next 5 years, 80% of business processes will get modernised. Skilful usage of big data and business analytics has already helped many companies with achieving commercial success. One of the well-known examples is Airbnb company, specialising in the broking – the vacation rentals of apartments and houses, to be exact. Thanks to data analysis, the company had discovered the reason behind clients leaving. It was poor quality of pictures used in the advertisements. When that element had been improved, immediate success followed, and sales increased. Another example, of just as famous Starbucks company, shows using advanced analytic tools for choosing new locations of service outlets, which guarantees reaching new clients perfectly.

Information is still considered to be one of the most vital factors of success. New research suggests that companies which use data and business analysis in the decision-making process are more productive and note higher profitability than companies that don’t. Thus, companies endeavour to collect, gather and analyse more and more information. The whole process is becoming more time and money consuming. Gathering big amount of data leads more and more often to wrong decisions, prolonged decision-making process, and wrong interpretation of reality not only in everyday life, but also in broadly defined business. Besides doubtless enthusiasm about development of business analytics, the biggest risks of using big data need to be emphasised:

  • huge shortage of specialists on analytics and managers experienced in utilising data in management
  • serious challenges related to privacy policy, intellectual property and data safety
  • accuracy of processed data
  • still insufficient predictive abilities of models/algorithms
  • adjusting to the changing legal regulations

Processed information comes not only from within the organisation, but also from outside, e.g. cooperation with credit information agencies and external debt collection agencies. Benefits accruing from access to the huge information supply are available not only for big concerns, but also small companies. And what are the consequences of using big data in credit risk management? It’s first and foremost change of approach towards key performance indicators (KPI). Precise data allow setting clearer and more sensible goals than before. On the other hand, processes based on automatic analisys of basic data will be less flexible. Therefore, it seems that the best solution is combination of sophisticated analytics with understanding of workings of human mind.

Data analysis in question, combined with well-defined business processes in the company, will be a vanguard of the work market revolution – the Robotic Process Automation (RPA). Accorind to the report, prepared by HfS Research, about 30% of order-to-cash processes offered by shared services centres (BPO/SSC) have already been automated by RPA. The potential reduction of costs tthank to RPA equals about 60%. It’s a great temptation to keep developing this technology, especially since 50% of all BPO/SSC actions can be taken by RPA. What will be the end of this? According to the research conducted by Oxford University’s Martin School, the probability of automation of the occupation equals 51% for credit risk analytic, 95% for debt collector, and 95% for accountant. Much safer can feel employees of sales and marketing – here, automation is nearly impossible (the probability equals only 1%).

According to another study, recently published report of McKinsey Global Institute, already available or currently developed technological solutions might have enabled replacing about 49% of actions currently performed by employees by 2055. As a result, over a billion of jobs all over the world might be cut. Authors of the report emphasise that only 5% of current occupations can be already fully replaced by the available technology. In addition, in case of 60% of 800 analysed occupations, automation rate equals at least 30%. This rate will increase along with development and maturing of technology, including various aspects of artificial intelligence, particularly understanding and analysing written and spoken natural language by machines. McKinsey Global Institute claims, however, that actual rate of replacing humans with machines and automation will depend on many indicators (economic situation, trends on the market, work market situation, law regulations, etc.). This means that the process itself is just as likely to take 20 years less than the report predicts, as 20 years more. Moreover, it’s economic calculation that will decide if and when particular profession will be automated, i.e. if it would be profitable. Considering the opportunities of cost cutting estimated in the report of HfS Research, I believe it is profitable already. Let us not forget, that implementing RPA doesn’t require changes of already existing ERP systems of the company, which causes period of time between identifying a field of automation and implementing ready solution to be very short.

Finally, to dampen the enthusiasm of fans of statistics in credit risk analysis and credit scoring, it’s worth emphasising that, just like there is no nil risk, there is no credit risk assessment model with 100% effectiveness. Even the most sophisticated and effective models need to be constantly improved and adjusted to the rapidly changing reality. Will it be done one day by a machine, instead of us humans? Perhaps. After all, development of artificial intelligence has been going on for numerous years already…

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