Banks face a ‘behavioralization’ of their balance sheets since deposit funding increasingly consists of non-maturing deposits with uncertain cash flows exposing them to asset liability (ALM) risk. Thus, this study examines the behavior of banks’ retail customers regarding non-maturing deposits. Our unique sample comprises the contract and cash flow data for 2.2 million individual contracts from 1991 to 2010. We find that contractual rewards, i.e., qualified interest payments, and government subsidies, effectively stabilize saving behavior and thus bank funding. The probability of an early deposit withdrawal decreases by approximately 40%, and cash flow volatility drops by about 25%. Our findings provide important insights for banks using pricing incentives to steer desired saving patterns for their non-maturing deposit portfolios. Finally, these results are informative regarding the bank liquidity regulations (Basel III) concerning the stability of deposits and the minimum requirements for risk management (European Commission DIRECTIVE 2006/48/EC).

This study employs a dataset from three German leasing companies with 14,322 defaulted leasing contracts to analyze different approaches to estimating the loss given default (LGD). Using the historical average LGD and simple OLS-regression as benchmarks, we compare hybrid finite mixture models (FMMs), model trees and regression trees and we calculate the mean absolute error, root mean squared error, and the Theil inequality coefficient. The relative estimation accuracy of the methods depends, among other things, on the number of observations and whether in-sample or out-of-sample estimations are considered. The latter is decisive for proper risk management and is required for regulatory purposes. FMMs aim to reproduce the distribution of realized LGDs and, therefore, perform best with respect to in-sample estimations, but they show poor performance with respect to out-of-sample estimations. Model trees, by contrast, are more robust and outperform all other methods if the sample size is sufficiently large.

This paper studies empirically the determinants of new account fraud risk within two dimensions: the probability of fraud, and the expected and unexpected (monetary) loss-per-account due to fraud. By fraud risk, we mean the risk that a bank fails to enforce a debt because the identity of the person incurring the debt cannot be ascertained. Using a unique and rich data set of account applicants, provided by a German Internet-only bank, we find that fraud risk is highly sensitive to demographic and socio-economic variables like nationality, gender, marital status, age, occupation, and urbanisation. For example, foreigners are 22.25 times more likely to commit account fraud than Germans, and men are 2.5 times more risky than women.