written by Eugenio Ciamprone
Credit risk Management is the series of activities and processes by means of which credit risk is estimated, measured and analyzed, and subsequently developing strategies to mitigate, partially or totally, its effects. To be more accurate, Credit Risk Management consists in statistical-mathematical methodologies for the identification and treatment of credit risk.
The management of credit risk is a fundamental activity mainly for banks and insurance companies, although in recent years the large number of variables to be taken into account, within the scope of business, demand greater deepening of the topic, also for sectors different from the ones already mentioned. Furthermore, the huge availability of data and the progress in computational methodologies allow a greater ability to identify credit risk even for entities other than banks and insurance companies.
Therefore, risk management models have also become a priority for companies. The measurement of a creditor’s creditworthiness (credit scoring), which in the past was achieved only by analysis of balance sheet data and company history, is obtained primarily using Machine Learning and Analytics.
Furthermore, the risk that the counterpart will not be able to fulfill, in whole or in part, the commitments undertaken is very often not due to only a company’s internal factors, but also to external factors such as the regulatory environment and the market in which it operates. Also, for these reasons, the use of Big Data can offer better performance in order to know the creditworthiness of a counterpart.
But how can Big Data impact and change the credit risk analysis for the better?
- Big Data in Credit Risk Management
- Alternative Data in Credit Risk Management
Big Data in Credit Risk Management
The new approach in the scope of Credit Risk Management can be summarized with the term “credit risk digitalization”. Thanks to the use of Big Data, the analysis of factors, that are “external” to the counterpart being evaluated, is equally important (or maybe even greater!) than the analysis of “internal” factors, typical of the traditional approach. The huge amount of data present on the web allows you to carry out a credit risk assessment by focusing on everything that surrounds the counterpart externally, and no longer just looking at its inside. It should be noted, however, that the data must be used correctly so that the output gives effective results.
If carefully managed, the data allow you to estimate the creditworthiness of business counterparts and carry out a more successful credit risk management, as long as you give much attention and importance to the data quality. A forecast made on reliable and relevant data can provide information on possible market changes in advance.
Historical data relating to the assessed entity are no longer sufficient, but its partnerships, the ecosystem of its suppliers and the trend of the sector in which it operates must be taken into consideration.
And equally, if we talk about a consumer, the credit risk assessment is obtained thanks to predictive models based, for example, on social network data, especially in areas where consumer’s historical data are non-existent or hardly accessible.
Alternative Data in Credit Risk Management
To analyze information and behaviors from the web it is necessary to work on Alternative Data. The tools that use Alternative Data, i.e. data obtained from non-traditional sources such as behavior on social media or online purchases, have the ability to analyze different types of data in order to intercept the signals that can improve performance in the scope of credit scoring and in credit risk management.
As regards the major players, the analysis of these data allows to detect in advance possible situations or events in the reference sector that may impact on the solvency of customers or suppliers. Analyzing the Alternative Data represents a competitive advantage in this context, since it enables an improvement in performance in identifying counterparts’ positioning and reliability. On the other hand, in the case of consumers, the use of these models makes it possible to intercept behavioral habits and social relationships which are quite often more reliable information to establish the credit rating of an entity compared to historical financial data thereof.
Furthermore, for some population groups and in some areas of the world, historical information often does not exist or is difficult to find. In such circumstances, “footprints” left on the web have a higher predictive ability than traditional historical data. The solvency “score” of an entity can be presumed from the information and behavior reported on the web.
The employment of Alternative Data also enables the identification of so-called weak signals, i.e. apparently random or initially unrelated information that, connected to other information, can be recognized as significant if viewed from a different outlook. If carefully selected, such data can be decisive for predicting the credit rating of entities or consumers prior than the ex-post analysis carried out on traditional data.
