Risk Assessment of unsecured loans – example of entering a new market

Abstract

Objective: The objective of the paper is to show the risk assessment of unsecured loans in theory and practise.Research Design & Methods: In the first part, the paper makes a literature review with regard to the theory unsecured loans and their risk assessment. In the second part, a case study discusses the risk assessment process as a practical application in the hypothetical case if a Swedish bank enters the German market.Findings: The risk assessment of unsecured loans is a standardized process there scoring models make a crucial contribution. This case study shows how difficult that process is in the event of cross-border activities, for example, a bank enters a new market in a new country.Implications & Recommendations: Assessing applicants on a new market is challenging due to the effect of asymmetric information. The problem can be solved by information sharing and by using the experiences from the existing portfolio. Based on that the bank can create an appropriate scorecard.Contribution & Value Added: The paper contributes to existing literature on risk assessment by applying scoring models to the case of cross-border activities.Article type: base on in-depth analysis of literature review

Financial Distress Prediction for Small and Medium Enterprises Using Machine Learning Techniques

“…Studies related to credit risk modelling have significantly grown during the past 50 years, with most notable breakthroughs by Altman (1968) and Merton (1974). For the last 20 years classical modelling techniques have been challenged, as researchers have been discussing modelling credit risk by applying different machine learning techniques (Qu et al, 2019;Atiya, 2001;Pickert, 2017;Shen et al, 2020;Uthayakumar et al, 2020). One of the first machine learning tool application in financial distress prediction was by Atiya (2001) who used Artificial Neural Networks in classic credit risk prediction model developed by Merton (1974).…”