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How credit risk is measured in 5 Steps

Knowing how customer default risk is measured is essential to understand how many and which ones are reliable, from a financial and payment point of view, and which ones are less secure and more at risk. A fundamental aspect for the funds and company results, and for the work of the administrative and financial sector of the company. Even more so with the difficulties brought by the Coronavirus effect on the markets, the economy, and companies. Even more today, it is important to analyze and keep the risk of default under control, in order not to incur nasty surprises, unpaid credits, and unpaid payments.

Again, new technologies can help and be a very effective solution. The ever-increasing increase in available data, together with the availability of increasingly high-performance software, has made new opportunities available that can be exploited by Risk management – to evaluate how the risk of customer default is measured -, alternatively or together with traditional methods of estimating risk factors based on traditional statistics. To carry out an adequate analysis of how the risk of default is measured, several steps must be taken.

Create scoring models to measure credit risk

An essential step to take is to develop scoring models. The goal is to distinguish customers, in the evaluation phase, between solid and less solid, based on the information available. With these systems, it is possible to define Credit Scoring, in order to have a synthetic judgment on the degree of solvency of the customer, the so-called Probability of Default (in technical jargon, abbreviated to Pd). In practice, to try to understand in advance whether a certain company could fail or not.

To properly develop a system on how default risk is measured, a lot of information needs to be kept in mind. Among these, for example, the balance sheets, the budget, the outstanding payments, the industrial plan, and the company organization.

Bias analysis, testing, and updating of application models

An important aspect to consider in the estimation phase is to quantify the phenomenon of the lack of adequate information, in practice the Bias, the defects or prejudices that can end up in the Input data, and thus lead to distorted or less reliable results.

The causes of these gaps and errors in customer information can derive from the loss of data during the archiving phase, or from incorrect management, or other variables. A high number of biases and errors can jeopardize the effectiveness of the scoring system on how credit risk is measured. Also, for this reason, the model must be tested and then kept constantly updated.

Application of systems for how credit risk is measured

The score obtained by the application model, which evaluates how credit risk is measured, is converted into a probability of customer default, allowing to estimate the probability that a company may fail or not.

The assignment of risk is made on the basis of risk classes, therefore dividing the Scoring scale into bands from the highest to the lowest risk. A threshold value, the so-called Cut-off, can also be used, below which the customer’s requests are rejected, or at least re-checked in an even more thorough manner.

How credit risk is measured: the use of innovative data

New and important opportunities, in the activities on how credit risk is measured, come from the integration of innovative data – all those that come from the digital world -, which can identify, on the one hand, new business opportunities, and on the other intercepting and limiting risks in the context of assessing the creditworthiness of customers.

After all, artificial intelligence and machine learning systems in recent years are assuming an increasingly important role for many companies in various sectors. And they are increasingly useful and applied also within the administrative and financial sector of companies, also to make activities more efficient on how credit risk is measured.

The use of artificial intelligence for how credit risk is measured

This broad applicability of machine learning and artificial intelligence is due to the fact that computers can learn to perform operations based on observing and learning from data, regardless of the type of activity or data in question. This automatic learning is favored by the increasing availability of data in terms of volumes and variety, and by increasingly powerful calculation tools. For this reason, in Risk management, Machine learning is increasingly being applied also in the techniques on how to measure the credit risk of customers and the market.

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