Le cooperative di dati nel settore bancario per la valutazione del merito creditizio: un alleato per le banche cooperative e per i clienti?

New technologies, and in particular big data, are at the heart of the «Digital revolution 4.0», which has profoundly innovated not only the world of science and technology but also the banking, insurance and financial sector. Regarding the banking sector, the impact of new technologies has been considered «revolutionary» for its multiple uses as the one for the assessment of the creditworthiness of a customer that allows to obtain, thanks to the immense pool of data analyzed, a more accurate and capillary assessment than the traditional one. The traditional assessment of creditworthiness, in fact, suffers from the difficulty of finding and accessing the information contained in the hard data. The innovative evaluation carried out through big data is capillary thanks to the huge amount of data collected and analyzed. However, the data in question derive mostly from the use of social networks and the internet, which contain the information released by a single user on the web, of a different nature and origin. The data transmitted by the user during the use of social networks, such as Facebook, Twitter and Instagram, are also subject to analysis. These data represent the new «black gold», as it is able to provide information on the propensities and preferences of each individual user, useful not only to modulate the offer of a product but also to predict the behaviors of the same in the world of the market or the finance. Although credit assessment obtained by big data and social data could appear more accurate than the traditional one, conducted with «hard» data, especially when relating to the credit history of a customer who lives in developing countries or in countries characterized by a low income, there are some critical profiles regarding the effectiveness of the results and the accuracy of the credit reference and the risk of a damage caused to the customer who will be granted a credit that will not be able to repay or be refused in a discriminatory way a credit to which it would have been entitled. There are also other critical profiles according to the risk of obtaining a distorted credit reference, from which an erroneous creditworthiness would result, and the existence of distortive and discriminatory effects. On the merits, some foreign cases have ascertained this risk as the Kevin Johnson case against American Express and the case of the Non-Discrimination Ombudsman at the Finnish NonDiscrimination and Equality Tribunal.In recognition of this, data cooperatives could become useful tools, in the credit assessment process, for the collection of customer data for the benefit of customers and banks, especially cooperatives banks which are closer to the territory and have smaller assets than those of large banking companies. These banks could provide access to a wider range of data from financial sources and not finance, while avoiding the use of big data from social networks. Thus, banks could make a more accurate assessment of credit risk, reducing the risk of consumer overindebtedness and business failure, and improving the overall quality of the credit portfolio. In addition, they could help identify predictive signals that can affect a customer’s ability to refund, such as changes in spending patterns, abnormal financial behavior, or changes in demographic data, by anticipating and taking measures to mitigate credit risk. Ultimately, data cooperatives could help cooperative banks in the process of assessing the creditworthiness of their customers without recourse to external services; At the same time, they could lead to greater customer protection by ensuring that social data is not used to determine the amount of funding to be granted. This is an unexplored path – but equally worthy of research and study – as there are no similar experiences in the banking sector, nor, in more general terms, in other sectors in the national context, unlike what has happened, until now, overseas.