We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented

One of the main challenges that banks face in modeling operational risk is the instability of risk estimates caused by heavy-tailed and insufficient loss data. To address these issues, we propose a loss scaling method to combine a bank's internal loss data with external loss data of other banks....

Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models' allows banks' management to make better risk decisions by better matching economic capital and risk appetite, and allows regulators to better understand the risk of banks. We show...

The literature proposes several alternatives for estimating compound distributions, which are widely used for risk quantification in the banking and insurance industries. In this paper, we evaluate the accuracy and time-efficiency of different approaches for estimating quantiles of compound...

Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models allows banks’ management to make more informed risk decisions by better matching economic capital and risk appetite, and allows regulators to enhance their understanding of...