The risk assessment of a new product is one of the most critical activities performed by the operational risk management of a company operating in the financial sector. There are few reference points for the operational risk management to assess the riskiness of a new product, due both to the lack of operational loss data and to the inexperience of the process owners in handling the new operation.

We investigate the applicability of semi-parametric approaches for estimating expected shortfall. More precisely, we examine the applicability of several models based on the historical simulation (HS) approach: one based on untransformed historical data, and others based on transformed historical data. Our research shows that the HS models based on certain transformed historical data can reliably be

The accurate modeling of operational risk is important for banks and the finance industry as a whole to prepare for potentially catastrophic losses. One modeling approach is the loss distribution approach, which requires a bank to group operational losses into risk categories and select a loss frequency and severity distribution for each category. The annual operational loss distribution is estimated

The disclosure of information by banks is beneficial to several categories of the public as well as to the stability of the financial system. Operational risk disclosures are particularly important, since operational losses may impact both the financial condition and the reputation of banks. This paper compares the levels of operational risk disclosure in the banking industries of India and Romania

Operational risk is becoming a major part of corporate governance in companies, especially in the financial services industry. In this paper, we review some of the existing methods used to quantify operational risks in the banking and insurance industries. These methods use recent statistical concepts such as extreme value theory and copula modeling. We explore the possibility of using a coherent risk

Cyber risk is one of the most challenging areas of risk, not only because it is relatively nascent but also because it remains an elusive moving target due to an ever-evolving threat landscape. A lack of structured data and the systemic implications of multifaceted impacts of overlapping risk frameworks are additional factors that make this risk difficult to quantify. As a starting point for overcoming

The market position of a company influences its performance. In hazard conditions, all the factors that determine a company’s market position and business are exposed to risk. An effective program of enterprise risk management (ERM) decreases the level of risk and improves company performance. ERM is a process that identifies and evaluates all potential losses that can occur in business organizations

The challenge of using small sample sizes for operational risk capital models fitted via maximum likelihood estimation is well recognized, yet the literature generally provides warning examples rather than a systematic approach. We present a general framework for analyzing maximum likelihood estimation error on operational value-at-risk as a function of sample size for five severity distributions commonly

An important problem in the banking and insurance industries is that of pricing risk. The two come together when a bank buys insurance to decrease the impact of potential operational risk losses. The price of such insurance hinges on the computation of risk premiums, which involves the computation of expected values with respect to the loss distribution. When the empirical data set is not large and

The importance of operational risk management in financial and commodity markets has increased significantly over the last few decades. This paper demonstrates the application of a nonhomogeneous Poisson model and dynamic extreme value theory (EVT) incorporating covariates on estimating frequency, severity and risk measures for operational risk. Compared with a classical EVT approach, the dynamic EVT

In the banking sector, machine-learning methods are applied in a wide variety of business areas: assessing a client’s risk profile (application and behavior scoring), forming targeted sales (x-sell, up-sell), choosing collection strategies (collection scoring), etc. The bank anti-fraud division is no exception, where with the help of machine-learning methods effective anti-fraud tools are developed

Tail dependence is a probability-based concept meant to address the challenge of detecting and modeling the extreme comovements that can be observed in many real-life situations. Huge financial losses for a bank, floods and epidemics are obvious instances of such extreme comovements. Like extreme value theory in the univariate case, tail dependence depends on asymptotic theory. Therefore, the statistical

The authors acknowledge the support received from the Spanish Ministry of Science/ FEDER grants ECO2016-76203-C2-1-P and ECO2016-76203-C2-2-P, and J. M. Sarabia and F. Prieto acknowledge the support received from the Santander Financial Institute (SANFI) of the Fundacion UCEIF through the University of Cantabria, funded by sponsorship from Banco Santander.

A method of allocating Operational Risk regulatory capital using the Shapley method for a large number of business units, supported by a service, is proposed. A closed-form formula for Shapley allocations is developed under two principal assumptions. First, if business units form coalitions, the value added to the coalition by a new entrant depends on a constant proportionality factor. This factor

The loss distribution approach (LDA) has evolved as the industry standard for operational risk models despite a number of known weaknesses. In particular, LDA’s traditional focus on historical loss data often neglects expert knowledge that is available for operational risk types of a more predictable nature. In this paper, we present an alternative quantification technique, so-called exposure-based

This article evaluates whether US large bank operational risk capital requirements are forward-looking, sensitive to banks' current exposures, and allow for risk mitigation, and discusses modifications that could bring regulation closer to these goals while also highlighting the potential pitfalls of doing so.

Within a loss distribution approach (LDA) framework, we propose to model catastrophic operational risk using a compound Neyman–Scott clustering model. The particularity of this compound model is that it relies on a Neyman–Scott process (the frequency component of the LDA) to model the occurrence behavior of catastrophic operational loss events. The motivation behind this is that catastrophic operational

Although people are a very important asset for financial firms, they are a key source of risk. Banks must allocate regulatory capital for covering their people-risk exposure. By using the Algo OpDataTM data set from US banks, and based on the loss distribution approach, we first estimate people-value-at-risk (people-VaR), assuming perfect correlation among people-risk categories but nonperfect dependence

The consultative document issued by the Basel Committee on Banking Supervision (BCBS) in March 2016 proposes the withdrawal of internal modeling for operational risk regulatory capital and describes the new proposed methodology for calculating operational risk capital requirement: the standardized measurement approach (SMA). One of the main problems with the SMA is that it does not allow the inclusion