Operational Risk Losses Research Articles

The potential impacts of the digital revolution on the operational risk profiles of banks

Society is undergoing a digital revolution. This is altering the business profiles of banks in terms of their systems, processes, controls and usage of third parties; their competitive landscape, ie competition from both digitising incumbents and new BigTech and FinTech entrants; and the behaviours of stakeholders, ranging from customers to cyber-criminals. This digital revolution is amplifying some of their existing risks, while also creating new risks, eg the potential for artificial intelligence (AI) tools to change behaviours over time (AI model drift). Some of these changes in operational risk profile will be transient, as they are associated with digital transformation, while others will be both ongoing and characterised by a high degree of dynamism. In this digitised endstate higher frequency/lower value human errors, may be replaced by lower frequency/higher impact systemic losses, arising from both catastrophic and silent failures. The influence of the digital revolution spans almost all of the Basel operational risk event categories, and may also lead to the enhancement of some controls (eg surveillance), while others may be undermined (eg by voice-spoofing). There is no silver bullet to mitigate these risks; instead, a portfolio of existing control frameworks need to be enhanced, including the following: change management; model risk management; third party vendor management; business continuity management, disaster recovery and operational resilience; and cybersecurity, with new controls required to address the new risks associated with AI. This will be a key factor in the operational risk losses of banks over the next decade.

A Model of Risk Management Strategy at Banks in their Operational Activities

By partnering with the business, the operational-risk discipline can create a more secure and profitable institution. Successful banks embrace risks while developing powerful mechanisms to prevent or manage risk and stay ahead. This study is devoted to the problems of improving the banking risk management, taking into account the new regulatory and technological requirements based on the use of modern technology and combining the latest achievements in numerical mathematics, statistics and information technology. Focusing on literature and by conducting a case study on operational risk management on one of largest retail banks, this paper aims on answering how operational risk management can be improved by implicating risk governance and internal control frameworks. It was concluded that although improvements have taken place in how operational risks are being managed, there is still room for improvements. An intersection of risk type and business/functional area of focus, prioritized based on the business environment is critical when fulfilling this life cycle of risk management activities. When too linear, the steps are not agile enough, so the cycle needs to be performed on a micro-level and aggregated for macro-level insights/risk decision-making. The suggested model outlines the understanding of the safe and prudent management of risks under the current prudential framework. It also demonstrates the ability of appropriate operations management techniques to reduce operational risk losses in the banking industry significantly.

Internal Control Activities and Risk Assessment Effect on the Operations of Quoted Banks in the Nigerian Stock Exchange

Banks are more likely to fail from operational risk than from credit risk, and internal control at banks create operational risk losses. This study investigated internal control activities and risk assessment on operational risk of quoted banks in Nigeria. 16 quoted banks from 2013- 2020 were studied based on the 2012 banking reform on corporate governance by the then CBN governor Sanusi Lamido Sanusi’s “Project Alpha Initiative” (PAI).The analysis carried out included pooled OLS regression, fixed and random effect and Hausman tests utilizing E-View 9 software. The findings showed that internal control activities has a negative correlation and internal control risk assessment has positive significant effect on operational risk. The researchers therefore recommend that internal check of staff at banks should be sustained as there was an inverse relationship between internal check and operational risk at banks. Penalties should be spelt out for banking staff who are non-compliant with bank policies and guidelines especially in the area of breech in software codes. Banks should ensure that internal control unit personnel are qualified and adequately trained especially IT staff. Keywords: Internal Control, Control Activities, Risk Assessment and Operational Risk

A text analysis of operational risk loss descriptions

A text analysis of operational risk loss descriptions

Review of Phosphorus-Based Polymers for Mineral Scale and Corrosion Control in Oilfield.

Production chemistry is an important field in the petroleum industry to study the physicochemical changes in the production system and associated impact on production fluid flow from reservoir to topsides facilities. Mineral scale deposition and metal corrosion are among the top three water-related production chemistry threats in the petroleum industry, particularly for offshore deepwater and shale operations. Mineral scale deposition is mainly driven by local supersaturation due to operational condition change and/or mixing of incompatible waters. Corrosion, in contrast, is an electrochemical oxidation–reduction process with local cathodic and anodic reactions taking place on metal surfaces. Both mineral scaling and metal corrosion can lead to severe operational risk and financial loss. The most common engineering solution for oilfield scale and corrosion control is to deploy chemical inhibitors, including scale inhibitors and corrosion inhibitors. In the past few decades, various chemical inhibitors have been prepared and applied for scaling and corrosion control. Phosphorus-based polymers are an important class of chemical inhibitors commonly adopted in oilfield operations. Due to the versatile molecular structures of these chemicals, phosphorus-based polymeric inhibitors have the advantage of a higher calcium tolerance, a higher thermal stability, and a wider pH tolerance range compared with other types of inhibitors. However, there are limited review articles to cover these polymeric chemicals for oilfield scale and corrosion control. To address this gap, this review article systematically reviews the synthesis, laboratory testing, and field applications of various phosphorus-based polymeric inhibitors in the oil and gas industry. Future research directions in terms of optimizing inhibitor design are also discussed. The objective is to keep the readers abreast of the latest development in the synthesis and application of these materials and to bridge chemistry knowledge with oilfield scale and corrosion control practice.

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks.

The impact of the Covid-19 pandemic on the operational risk losses of Hungarian credit institutions

The operation of the banking sector in 2020 was determined by the outbreak of the coronavirus pandemic and, in connection with this, the maintenance of business continuity and safe operation. Due to the pandemic, losses appeared in different forms in banking operations, some of which are classified as operational risk losses. Through a quantitative and qualitative analysis of the operational risk losses of Hungarian banks linked to the pandemic, this study shows that despite the high nominal losses, the operation of the banking sector remained stable, and the capital allocated by the credit institutions for operational risks provided sufficient coverage for unexpected losses. The focus of the analysis on small and large banks showed that it was not the size of the institution and its capital calculation method, but the immediate decisions made to deal with the pandemic, as well as the infrastructural background, that determined the extent of the realized damages.

Systemic operational risk in the Australian banking system: the Royal Commission

The proceedings of the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry shocked the Australian public, the government and many in the financial industry. Over a period of just over a year the commission held open sessions in which directors and chief executive officers of the largest financial institutions in Australia were forensically questioned as to cases of serious misconduct in their firms and related entities. During the inquiry, many cases of misconduct were unearthed across a range of banking services (eg, consumer lending, including mortgage lending, car finance and credit cards; financial advice; and insurance and pensions). The most egregious of this misconduct was found in many financial institutions (in particular, in the largest banks, which together handle over 75% of banking products in Australia and New Zealand). The commission concluded that the financial institutions identified in the proceedings were at fault and that remediation would have to be paid to any customers harmed. This paper describes the role of the commission, documents some of the most prominent cases of misconduct, which it summarizes in terms of operational risk losses (using Turner’s framework for analyzing organizational disasters) and also details some egregious examples of operational risk events not covered by the commission. This paper can be seen as an analysis of the individual case studies of misconduct as identified by the Commission.

The Impact of Workload on Operational Risk: Evidence from a Commercial Bank

Operational risk has been among the three most significant types of risks in the financial services industry, and its management is mandated by Basel II regulations. To inform better labor decisions, this paper studies how workload affects banks’ operational risk event occurrence. To achieve this goal, we use a unique data set from a commercial bank in China that contains 1,441 operational risk events over 16 months. We find that workload has a U-shaped impact on operational risk error rate. More specifically, the error rate of operational risk events decreases first, as workload increases, and then increases. Furthermore, when workload is low, employees tend to make performance-seeking risks; however, when workload is high, employees tend to exhibit quality degradation due to cognitive multitasking. Based on the causal relationship between workload and operational risk events from the empirical analysis, we discuss staffing policies among branches aimed at reducing operational risk losses. We find that employing a flexible staffing rule can significantly reduce the number of operational risk events by 3.2%–10% under different scenarios. In addition, this significant performance improvement can be achieved by adding even a little bit of flexibility to the process by allowing employees to either switch their business lines in the same branch or switch branches within the same business lines on a quarterly basis. This paper was accepted by Vishal Gaur, operations management.

The impact of culture upon operational risk management guidelines in the banking sector of selected Asian countries

For banks, operational risk losses are likely to have a significant impact not only on their financial condition, but also on their reputation. This makes operational risk management (ORM) particularly important. In relation to ORM, the Basel Committee on Banking Supervision promotes a banking supervision policy based on the idea of “enforced self-regulation”. Thus, the central banks of different countries regulate ORM according to the specificities of their national banking industry. This paper tests the hypothesis that such regulatory openness results in legal texts that are highly influenced by the culture of the country in which each central bank issuing guidelines on ORM is located. The author analyzes a corpus of approximately 50 000 words that feature in ORM guidelines published in English by the central banks of China, Hong Kong, India, Indonesia, Japan, Singapore and South Korea. By applying the Kendall coefficient, the following significant correlations have been found: (a) the higher the masculinity dimension, the less clear the text; and (b) the higher the masculinity dimension, the less prescriptive the text. Moreover, our content analysis reveals that each operational risk-related item has a different weight in the guidelines of different countries. The research results should be useful to regulators looking to fine-tune their decisions in different cultural environments.

A Framework for Analysis and Prediction of Operational Risk Stress

A model for financial stress testing and stability analysis is presented. Given operational risk loss data within a time window, short-term projections are made using Loess fits to sequences of lognormal parameters. The projections can be scaled by a sequence of risk factors, derived from economic data in response to international regulatory requirements. Historic and projected loss data are combined using a lengthy nonlinear algorithm to calculate a capital reserve for the upcoming year. The model is embedded in a general framework, in which arrays of risk factors can be swapped in and out to assess their effect on the projected losses. Risk factor scaling is varied to assess the resilience and stability of financial institutions to economic shock. Symbolic analysis of projected losses shows that they are well-conditioned with respect to risk factors. Specific reference is made to the effect of the 2020 COVID-19 pandemic. For a 1-year projection, the framework indicates a requirement for an increase in regulatory capital of approximately 3% for mild stress, 8% for moderate stress, and 32% for extreme stress. The proposed framework is significant because it is the first formal methodology to link financial risk with economic factors in an objective way without recourse to correlations.

Idiosyncratic Viral Loss Theory: Systemic Operational Losses in Banks

Basel III regulation intent is to increase the resiliency of banks through effective risk management practices that can reduce significant idiosyncratic operational losses. A systemic risk event that leads to significant losses in a bank holding company (BHC) can expose them to become insolvent and cause significant volatility and unpredictable negative impact on the United States economy. The viral spread of operational losses through global markets by interconnected multinational banks can be compared to viruses spread through interconnected countries and the significant losses incurred; this can be referred to as idiosyncratic viral loss theory. This idiosyncratic viral loss theory discusses systemic operational losses that are evident in human error, fraud, and legal expenses that are aligned to systemic operational risk. The occurrences of significant losses that are idiosyncratic in nature and that are linked to failed internal processes, people, systems, and external events are defined by the Basel Committee on Banking Supervision as operational risk losses; these losses’ idiosyncratic nature makes them comparable to viruses. This study employs the Compliance and Ethics Group’s (OCEG’s) standard that integrates governance, risk management, internal control, assurance, and compliance (GRC capability model) into one functional goal to improve quality and principled performance through measurable tools that may enhance effectiveness and efficiency practices. This study concerns senior manager activities that can be effective towards meeting effective risk management practices posed by the Basel III regulation for BHCs, which may reduce the spread of significant losses in the banks. Through the use of a qualitative e-Delphi study, 10 banking finance experts were convened to build consensus on effective risk management practices. Data were collected from three electronic questionnaires submitted through Qualtrics. Data were analyzed using theoretical triangulation, coding, and thematic analysis. Four important considerations were identified that could bolster effective risk management practices: (a) a comprehensive enterprise-wide risk; (b) controlling fraud; (c) going beyond the minimum risk assessment requirements set forth by the banking regulators; (d) independent risk identification and management. These considerations towards effective risk management practices may help reduce systemic operational losses viral spread in banks.

On modeling contagion in the formation of operational risk loss

This paper models an overall operational risk loss caused by the accumulation of intermediate losses incurred at each process via a mechanism of network contagion across distinct processes within the boundary of a bank. We lay a theoretical foundation for the choice of an exponential–Pareto combined distribution to model the severity of the operational risk. We derive, on a theoretical basis, the functional form of the operational risk severity distribution. The resulting loss severity distribution, in theory, is consistent with the parametric distribution that previous empirical works suggest is the best fit for loss data.

Proposal of a Method for Analyzing Operational Risk Loss Data

Proposal of a Method for Analyzing Operational Risk Loss Data

Operational Risk Capital

We study the response of banks to the introduction of a new capital requirement relating to operational risk. To isolate the effect of this new regulation on realized operational risk losses, we take advantage of the partial US implementation relative to full European adoption. Operational risk losses are reduced in treated banks. The extent of loss reduction depends upon the measurement approach used to calibrate operational risk capital requirements. Banks with low institutional ownership and those without binding regulatory capital constraints also present significant loss reduction. We link these findings to incentives for improved risk management and governance post treatment.

Loss distribution approach for company operational risk analysis

The measurement of potential operational losses as an assessment of capital adequacy is needed by the company. One value that can be used to measure the potential loss of a company is Value at Risk (VaR). This paper aims to measure VaR based on the loss distribution using the Bayesian method where the loss frequency is assumed to be Poisson distribution and the loss severity is assumed to be the distribution Lognormal. VaR with Monte Carlo (MC) and Fast Fourier Transformation (FFT) simulations are compute using the values of the parameter of distributions that have been obtained by using the Bayesian method. In this paper, a numerical example of VaR estimation of a bank company is demonstrated to find the estimate VaR from several sources i.e. internal fraud, external fraud, business practices, employment practices, process failure, system failure and damage to assets. The results showed that the estimate VaR value equal to $ 8.2336044 with Monte Carlo and and $8.522028 with FFT simulation at 99% confidence level. These means that the company has the opportunity to experience operational risk losses which exceeds $ 8.2336044 and $8.522028 in the coming year by 1%.

ANALISA PENGUKURAN BEBAN MODAL RISIKO OPERASIONAL METODE BASIC INDICATOR APPROACH (BIA) DAN ADVANCE MEASUREMENT APPROACH (AMA) DI BANK EFG

This study aims to compare the magnitude of operational risk losses between the Basic Indicator Approach (BIA) method, and the loss distribution model in the Advanced Measurement Approach (AMA) approach so as to provide a more realistic picture for banks to determine the operational risk capital burden that must be provided based on the causes Operational risks are as follows Internal Process, Human and External Events. Measurement of operational risk capital burden by the AMA method is the determination of frequency of loss distribution, determination of severity of loss distribution, testing with goodness of fit test, then compilation of aggregated loss distribution, calculation of Operational Value at Risk (OpVar), testing the model with back testing and comparison of capital adequacy from the results of the calculation of the Basic Indicator Approach (BIA) and the Advance Measurement Approach (AMA). The results of research based on the BIA require an operational risk capital cost of Rp.291,652,000,000. The results of the research on the AMA approach use the frequency of loss distribution parameter for the internal causes of the process with a Geometric distribution of 0.17561, while for the human cause of 0.08511, for the cause of external events amounting to 0.83721. Determination of Frequency of Loss Distribution using Goodness of Fit for internal processes, people and external events. The results of the Operational Value at Risk (OpVar) with a geometric distribution pattern, then the maximum loss that can arise due to human factors is Rp.24,114,480,096, -, for internal process factors of Rp.6,010,929,367, whereas for external causes for Rp. 2,161,092,909. In total operational risk capital needs through the AMA method of Rp. 32,286,502,372.

Modelling extremal dependence for operational risk by a bipartite graph

Abstract We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between them. We then derive estimators and provide estimation methods for individual as well as aggregated tail risk, measured in terms of Value-at-Risk and Conditional-Tail-Expectation for very high confidence levels, and introduce also an asymptotically full capital allocation method for portfolio risk. Having access to real-world operational risk losses from the Italian banking system, we apply our model to these data, and carry out risk estimation in terms of the previously derived quantities. Simulation studies further reveal first that even with a small number of observations, the proposed estimation methods produce estimates that converge to the true asymptotic values, and second, that quantifying dependence by means of the empirical network has a big impact on estimates at both individual and aggregate level, as well as for capital allocations.

Operational Risk Management: Optimal Inspection Policy

Operational risk is one of the major risks in the financial industry; major banks around the world lost nearly $210 billion from operational risk events between 2011 and 2016 (Huber and Funaro 2018) and inspection on operational risk is required by the Basel Committee on Banking Supervision. Motivated by the importance of operational risk and its current industry regulation, we study how a financial firm can optimally design inspection policies to manage operational risk losses. Specifically, we propose a continuous-time principal-agent model framework to examine a financial firm's (principal) optimal inspection policy and their employees' (agent) effort towards lowering the risk event occurrences. We first consider two commonly used inspection policies, namely, random and periodic policies, and characterize the optimal inspection strategy under each policy. We identify conditions for two different modes of inspection (effort inducement and error correction) as well as nuanced interactions among inspection frequency, penalty charged on errors, and wage paid to employees. We then compare the performances of random and periodic policies. We find that, contrary to conventional wisdom, the random policy is not always optimal; it is dominated by the periodic policy if the inspection cost is sufficiently low. Furthermore, we construct a hybrid policy that strictly dominates random policy and weakly dominates periodic policy, which suggests that a proper reduction of the random element in the inspection policy can always improve its performance. Finally, calibrating model parameters using operational risk data from a major bank in China, we numerically show that our key insights about random, periodic, and hybrid policies are robust to various model extensions.

The effect of internal control environment on operational risk of quoted banks in Nigeria

Banks are more likely to fail from operational risk than from credit risk, and internal control at banks create operational risk losses. Internal control system is the backbone of every company and the successful attainment of the goals of most companies depend upon the effectiveness of their policies and internal control system. The objective of this study is to investigate the effect of internal control environment on operational risk of quoted banks in Nigeria. 16 selected quoted banks in the Nigerian stock exchange from 2013-2017 were studied based on the 2012 banking reform on corporate governance by the then CBN governor Sanusi Lamido Sanusi’s “Project Alpha Initiative” (PAI). Data were collected from banks published annual reports, CBN statistical bulletin, NDIC report, CBN fact book, company website and banks’ Pillar III disclosure report for the relevant years sampled for analysis. The analysis carried out included pooled OLS regression, fixed and random effect and Hausman tests to determine the most suitable model for result interpretation. This was conducted with the aid of E-View 7 software. The findings shows that internal control environment (BS, ID, LD, and ES) has positive significant effect on Operational risk (OPR). It was recommended that a well-established internal environmental control system should be mandatory for all companies, in the banking industry.