In an increasingly complex environment where large volumes of heterogeneous data are made available to companies, it is becoming increasingly difficult for them to rationalize their decision-making in this mass of available information. 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: a challenge for effective decision-making

This is how companies capable of analyzing their data, using artificial intelligence technologies, gain a clear 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 field of corporate solvency, the risk of default or cessation of payment is modeled by the probability of default. This variable is a valuable decision aid in predicting the risk of default, classifying the risk of a business portfolio or estimating the loss of turnover.

Predictive models, the tool for risk anticipation

Integrating predictive models into the credit management activity allows the credit manager to better anticipate and prevent risks. They give a probability of default and are an operational decision aid, conceptualized by a score associated with each company. These algorithms process volumes of data faster than humans could. They go through data sorting, informationextraction and correlation, decision tree building and learning, from examples around instruction sequences, to make robust models in behavior anticipation. The difficulty, in the creation of these models, is to be able to gather sufficiently important and quality data to elaborate scenarios as close as possible to reality.

Today, machine learning, in the predictive analysis of failure risks, is among the most advanced techniques for predicting behavior. This technology, based on mathematical reasoning, allows to extract value from various data masses in order to discover hidden behaviors with more efficiency than human intelligence. It also enables real-time scorings that are more efficient than traditional analytical models. However, it does not remove the human element from the evaluation.

For the credit manager in an increasingly complex environment, the benefits of these predictive mathematical models are an essential tool, just like the dashboard, in the management of risk and the securing and development of the business relationship linked to each company.