written by Eugenio Ciamprone
During its existence and in the performance of economic activity, any company will have to interact with the surrounding environment, with the market in which it operates and compete in an ever-changing economic context. The notion of risk, therefore, is inherent in the very nature of the economic activity, and as such, its management is an essential activity for the company’s survival.
Risks can be defined as uncertain future events that can positively or negatively affect the achievement of business goals.
In the last decade, companies have competed in a context of exponential increase in risks and consequently the sensitivity of companies towards the assessment and management of the same has also increased. All this has led to a transition from sectoral Risk Management to the concept of Enterprise Risk Management – ERM, which is an integrated approach where all risks are assessed according to their comprehensive impact on the enterprise.
As a result, ERM provides an incentive towards greater corporate communication and encourages the spread of a risk culture within the company. The risk is no longer seen only as a threat but also as an opportunity, if proper management of the same is able to intercept the changes at an early stage.
ERM allows management to efficiently manage risks and opportunities arising from the uncertainty of the environment, in order to safeguard the company itself and the value creation.
Enterprise Risk Management is a process involving every level of the company and aims to highlight and manage potential events that may affect the enterprise’s business in order to develop a strategy to provide support for the achievement of the objectives. In fact, the main drivers behind the use of Enterprise Risk Management models are certainly the accountability of all company levels as well as the reduction of costs and unpredictability, and the improvement of creditworthiness and corporate reputation.
In the current market context, it is impossible to properly manage business risks without data analysis. And as a consequence, given the amount of data that companies have to manage every day, turns out of vital importance that they resort to the use of Data Science.
The role of Data Science in Enterprise Risk Management
As already mentioned in the previous paragraph, the new role of enterprise risk management involves all organizational functions and it not only prevents and reduces the risk, but it looks to the pursuit of growth opportunities that market changes present.
However, all this is only possible through the correct use of the available data. In order to make a transition from traditional to advanced risk management, companies must introduce advanced data analysis, by using artificial intelligence and machine learning.
With the help of Data Science, risk management presents a more efficient, prompt and effective approach in order to make more accurate decisions, but all this cannot be separated from the use of correct and relevant data, advanced analysis and appropriate risk models, from the use of Machine Learning, Advanced Analytics and Alternative Data.
The transition from traditional to advanced enterprise risk management involves the evolution from an evaluation model of what happened (Descriptive Analysis) to models that use past data to predict future events (Predictive Analysis) and then, in their more evolved feature, to models that suggest the actions to be taken, based on the results of descriptive, diagnostic and predictive analysis (Predictive Analysis).
In its most advanced stage, therefore, risk management is linked to strategic decisions of the organization aimed at the sustainability and growth of the company, in both the short and long term.
Risk assessment and management also go through a process of score assignment and identification (scoring models) that represents the degree of risk to which the company is subjected within a given scope.
Alternative Scores in Enterprise Risk Management
The use of Data Science in enterprise risk management consists in the application of machine learning algorithms, both on financial data from traditional sources (e.g. financial statements) and on alternative data. This allows an assessment of a broader spectrum of risks and the construction of Alternative Scores, thanks to which new sets of information, that support the analysis of traditional data in risk management, are intercepted.
For example, in addition to the scores calculated on balance sheet data, companies can get an analysis of the risks associated with positioning and digital presence, geographical aspects and vulnerability linked to social networks through sentiment analysis.
Business risk analysis, with the use of Alternative Scores, allows the company to evaluate in advance possible market events and in the reference sector that may impact, positively or negatively, on the economic activity of the same.
