Artificial Intelligent Credit Risk Prediction: An Empirical Study of Analytical Artificial Intelligence Tools for Credit Risk Prediction in a Digital Era

Millennials service expectations drive transformation from traditional lending into digital lending. The CAGR for digital lending is 53% until 2025. Therefore, in this growing information age new methods for credit risk scoring could form the central pillar for the continuity of a financial institution. This paper contains the first research into AI application in individual risk assessment across two advanced lending markets. The research has been performed on 133.152 mortgage and credit card customers of 3 European lenders during the period January 2016 – July 2017. As candidate models, we chose neural nets and random forests. The research describes three experiments that develop the artificial intelligent probability of default models. In all experiments AI models performed better than the traditional models. Scalable automated credit risk solutions can therefore build on AI in their risk scoring

Motivation-need theories and consumer behavior

Includes bibliographical references (leaves 18-22)

Consumer confidence and the impact on retail output in the Netherlands

The paper provides a theoretical exposition of the different motives to save, i.e. not to spend. The structure of consumer confidence is described with the methodology of data (sources). It is followed by the re-estimation of the model of consumer confidence for both the 1972-1987 and the 1987-2000 periods. The actors investigate which specific motives to save are reflected in the identified components. The results are discussed and compared with other actors’ results. The model of firm output growth is specified by way of illustration in retail

The benefits of joint and separate financial management of couples

Financial management differs across households with consequences for financial outcomes and well-being of partners in households. A large-sample study has been performed, investigating the relationship between financial management of households and the occurrence of financial problems. To our knowledge, this is the first study on this relationship. Data from both partners was collected on having joint and separate bank accounts, on financial decision making, on drivers of financial management, and on financial outcomes. Based on the data, four financial management styles were derived: syncratic/joint, male-dominant, female-dominant, and autonomous financial management. In the syncratic style, partners have a joint bank account and take most financial decisions together. In the male/female-dominant styles, one partner (husband or wife) takes the main financial decisions. In the autonomous style, both partners have their own bank accounts and make their own decisions. As a conclusion, we find that syncratic financial management and having a joint instead of a separate bank account correlates with fewer financial problems, as compared with male-dominant money management and having separate bank accounts. Deciding together as partners is beneficial for the quality of financial management and for avoiding financial problems.</p