scholarly journals Good practice guide to setting inputs for operational risk models

2016 ◽  
Vol 22 (1) ◽  
pp. 68-108 ◽  
Author(s):  
P. O. J. Kelliher ◽  
M. Acharyya ◽  
A. Couper ◽  
K. Grant ◽  
E. Maguire ◽  
...  

AbstractThis paper seeks to establish good practice in setting inputs for operational risk models for banks, insurers and other financial service firms. It reviews Basel, Solvency II and other regulatory requirements as well as publicly available literature on operational risk modelling. It recommends a combination of historic loss data and scenario analysis for modelling of individual risks, setting out issues with these data, and outlining good practice for loss data collection and scenario analysis. It recommends the use of expert judgement for setting correlations, and addresses information requirements for risk mitigation allowances and capital allocation, before briefly covering Bayesian network methods for modelling operational risks.

2016 ◽  
Vol 22 (1) ◽  
pp. 109-126

This abstract relates to the following paper: KelliherP. O. J, AcharyyaM., CouperA., GrantK., MaguireE., NicholasP., SmeraldC., StevensonD., ThirlwellJ. & CantleN.British Actuarial Journal. doi: 10.1017/S1357321716000210


Author(s):  
Răzvan Tudor ◽  
Dumitru Badea

Abstract This paper aims at covering and describing the shortcomings of various models used to quantify and model the operational risk within insurance industry with a particular focus on Romanian specific regulation: Norm 6/2015 concerning the operational risk issued by IT systems. While most of the local insurers are focusing on implementing the standard model to compute the Operational Risk solvency capital required, the local regulator has issued a local norm that requires to identify and assess the IT based operational risks from an ISO 27001 perspective. The challenges raised by the correlations assumed in the Standard model are substantially increased by this new regulation that requires only the identification and quantification of the IT operational risks. The solvency capital requirement stipulated by the implementation of Solvency II doesn’t recommend a model or formula on how to integrate the newly identified risks in the Operational Risk capital requirements. In this context we are going to assess the academic and practitioner’s understanding in what concerns: The Frequency-Severity approach, Bayesian estimation techniques, Scenario Analysis and Risk Accounting based on risk units, and how they could support the modelling of operational risk that are IT based. Developing an internal model only for the operational risk capital requirement proved to be, so far, costly and not necessarily beneficial for the local insurers. As the IT component will play a key role in the future of the insurance industry, the result of this analysis will provide a specific approach in operational risk modelling that can be implemented in the context of Solvency II, in a particular situation when (internal or external) operational risk databases are scarce or not available.


2019 ◽  
Vol 13 (1) ◽  
pp. 1204-1215
Author(s):  
Răzvan Tudor

Abstract From the Solvency II perspective, the capital requirement for operational risk is based on the application of a standard formula. The limitation imposed by this approach as well as the definition of operational risk by establishing certain types of activities (i.e. internal processes, people, systems, etc.) as generating causes does not allow, at least for the time being, the establishment of an effective way of managing the operational risk regardless of the type of strategy chosen. Any human operator involved in the risk identification and evaluation processes, within most of the organizations, would use the logic of the included middle based on Boolean binary values (i.e. true/false, 1/0, etc.). This article attempts to logically analyze the methodological impact that would result from using a logic of the included middle which accepts that an identified operational risk and an unidentified operational risk may coexist at the same time, in a risk profile, provided that the identified one is actual and the unidentified one is potential, reciprocal and alternative but never up to the 100% limit. The included middle in this approach is the transition state, which is perfectly possible in terms of defining the topological properties of the time in which the identified operational risks analyzed are assessed. The novelty of this approach is based on the fact that the logic of the included middle, which we include in research as a concept and as a tool, was one of the nudging factors that underpinned the development of the wave mechanics (e.g. Schrodinger’s Cat Paradox) and some of the quantum physics theories later, and its use has never been tested in risk management.


2018 ◽  
Vol 23 ◽  

This abstract relates to the following paper: KelliherP.O.J., AcharyyaM., CouperA., GrantK., MaguireE., NicholasP., SmeraldC., StevensonD., ThirlwellJ. and CantleN.J.Good practice guide to setting inputs for operational risk models. British Actuarial Journal. doi: https://doi.org/10.1017/S1357321716000179


2018 ◽  
Vol 29 (77) ◽  
pp. 283-296
Author(s):  
Macelly Oliveira Morais ◽  
Antonio Carlos Figueiredo Pinto ◽  
Marcelo Cabus Klotzle

ABSTRACT Internal operational risk models have not yet been established as a methodology for calculating regulatory capital. These models, which must be integrated with operational risk management, have been criticized for the subjectivity of some of their fundamental elements. The purpose of this paper is to demonstrate the use of the "scenario analysis" element in the Loss Distribution Approach (LDA) methodology for calculating regulatory capital relative to operational risk, based on the experience of the Brazilian Development Bank (BNDES) in integrating operational risk management with the measurement of capital. The proposed methodology, which applied the Delphi technique through questionnaires, enabled: (i) the measurement of regulatory capital considering feasible scenarios; (ii) the identification of tail and body scenarios for the aggregate distribution of losses, which are not reflected in the internal loss database; (iii) the identification and comprehensive measurement of BNDES’s operational risks; (iv) the obtainment of information that can guide risk management with regard to identifying risks that must be given prioritized treatment; (v) the development of a risk culture, with a view to involving specialists from different units; (vi) the use of a methodology that can be understood by all business experts, who are the ones that are aware of the risks of their activities.


2012 ◽  
Vol 2012 ◽  
pp. 1-57
Author(s):  
E. Karam ◽  
F. Planchet

A new risk was born in the mid-1990s known as operational risk. Though its application varied by institutions—Basel II for banks and Solvency II for insurance companies—the idea stays the same. Firms are interested in operational risk because exposure can be fatal. Hence, it has become one of the major risks of the financial sector. In this study, we are going to define operational risk in addition to its applications regarding banks and insurance companies. Moreover, we will discuss the different measurement criteria related to some examples and applications that explain how things work in real life.


2019 ◽  
Vol 1 (1) ◽  
pp. 28-43
Author(s):  
Iwan Lesmana

Managing bank’s operational risks becoming an important feature of sound risk management practice in modern financial markets. The most important types of operational risk involve breakdown in internal controls and corporate governance, which could lead to financial losses through fraud, error or failure to perform. Development of statistic has accelarated banks to create internal operational risk models in different ways. Although those models created in different ways, they surely use the pattern of risk management that is developed by Basel Committee on Banking Supervision. Basel Committee on Banking Supervision has proposed three increasingly sophisticated approaches of operational risk, i.e basic indicator approach, standardized approach and advanced measurement approach. Applying those approaches will help banks to eliminate the operational risk, that will lead them to a better intermediation process.


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