In an increasingly complex environment or large volumes of heterogeneous data are made available to companies, it must be noted that it is increasingly difficult for them, in this pool of available information, to rationalize their decision-making. Indeed, the volume of data is exploding. According to JDN (Journal Du Net), the world had only two zettabytes of digital data in 2010. In 2015, this figure was multiplied by 6, and will be multiplied by 3.7 between 2020 and 2025.
Data is an issue of effective decision-making
For example, companies capable of analysing their data, with technologies derived from artificial intelligence, then take an obvious competitive advantage over their competitors in risk analysis. Risk is defined as the possibility that a negative event, difficult to anticipate, will occur. In the area of corporate solvency, the risk of default or cessation of payment, is modelled by the probability of default. This variable is a valuable aid to decision-making in predicting the risk of default, classifying the risk of a business portfolio or estimating the loss of turnover.
Predictive Models, the Risk Anticipation Tool
Integrate predictive models into the management of credit management activity allows the credit manager to better anticipate and prevent risks. They give a probability of failure and are an aid to operational decision-making, conceptualized by a score associated with each company. These algorithms process data volumes faster than humans could achieve. They involve sorting data, extracting and correlation of information, setting decision trees and learning, using examples around instruction sequences, to make them robust models in behaviour anticipation. The difficulty in creating these models is to be able to gather data of sufficient importance and quality to develop scenarios that are closest to reality.
Today, machine learning, in predictive analysis of the risks of failure, is one of the most advanced techniques of behavior prediction. This technology, made on mathematical reasoning, allows to extract value on various masses of data in order to discover behaviors buried with more efficiency than human intelligence. It also allows real-time scorings that perform better than conventional analytical models. However, this does not remove the whole human part in evaluation.
For the credit manager in an increasingly complex environment, the benefits of these predictive mathematical models are an essential tool, as is the dashboard in risk management, securing and developing the business relationship related to each company.
