A User-Centered Approach to Create Realistic Datasets for AI. Case Study: Creditworthiness in the Banking Sector

Nowadays businesses are evolving, as new digital tools ensure greater efficiency of their information systems. Decision-making and strategic processes can benefit from innovation opportunities such as Machine Learning. The main issue encountered in Artificial Intelligence applications, is that data can be not available or unsuitable for the case of study. This paper proposes the solution for this problem, by generating simulated data for AI. The case of study is creditworthiness in the banking sector; a loan is considered the main source of income for the banking sector, as well as the main source of risk. Consequently, the evaluation of creditworthiness is a key activity both for banks and for customers. To addressthis need, we propose a solution tailored to lenders to evaluate credit applications and to customers to be aware of behaviors that can reduce their credit score. The approach proposed in this paper aims at realizing realistic datasets for Artificial Intelligence (named IDEA) to meet specific business needs, and to respect users’ requests. An analysis of the current literature and methods for the evolution of conceptual models will be conducted, through pre-existing datasets. The proposed approach draws from and extends such literature. The intended application is to adopt this approach in the banking sector for considering the creditworthiness of customers who have entered into financial relationships. Therefore, the envisaged use case is to forecast the probability of borrowers going bankrupt. The paper defines the approach applied to specific financial datasets for the use case. Moreover, a validation of datasets is done, thanks to the Data Quality Index, before applying IDEA to predict credit solvency.