Operational Risk Modeling Research Articles

A 4IR-Driven operational risk model for CO2 storage in deepwater abandoned hydrocarbon reservoirs

This study presents the development of an advanced operational risk model that leverages Fourth Industrial Revolution (4IR) technologies to optimize carbon dioxide (CO2) storage within deepwater abandoned hydrocarbon reservoirs. The model systematically combines Artificial Neural Networks (ANN) with the optimization capabilities of Genetic Algorithms (GA) and the probabilistic analysis strengths of a Bayesian Network (BN) to perform dynamic and comprehensive risk assessments. By applying the model to a dataset covering a 200-year timeframe, it effectively forecasts CO2 storage capacities while simultaneously evaluating associated risks across different operational scenarios. One of the key innovations of this model is the introduction of a novel loss function designed to precisely manage forecast deviations and enhance the efficiency of operational processes. This function is critical in ensuring that the model remains robust and accurate in real-time risk assessments, allowing for more reliable decision-making in CO2 storage operations. In addition, the study conducts an economic evaluation that underscores the crucial role of 45Q tax credits in bolstering the financial sustainability of carbon sequestration projects. The analysis highlights how these credits significantly reduce the economic barriers to adopting carbon utilization, storage, and sequestration (CUSS) technologies, making large-scale implementation more feasible. The model's performance is underscored by its ability to achieve a 49% CO2 retention rate over two centuries, with an impressively low average error margin of 0.249%. These results highlight the model's impressive efficiency and accuracy, while also demonstrating its capacity to markedly improve the predictability of CO2 storage outcomes. The findings suggest that this model could play a pivotal role in advancing global sustainability efforts by optimizing CO2 storage processes, thereby contributing to the reduction of atmospheric CO2 levels and supporting long-term climate goals.

Factor Analysis Framework for Credit, Operational, and Market Risk Modeling

Factor Analysis Framework for Credit, Operational, and Market Risk Modeling

Validating operational risk models

Validating operational risk models

Operational Risk Working Party - Validating Operational Risk Models

Abstract Operational Risk is one of the most difficult risks to model. It is a large and diverse category covering anything from cyber losses to mis-selling fines; and from processing errors to HR issues. Data is usually lacking, particularly for low frequency, high impact losses, and consequently there can be a heavy reliance on expert judgement. This paper seeks to help actuaries and other risk professionals tasked with the challenge of validating models of operational risks. It covers the loss distribution and scenario-based approaches most commonly used to model operational risks, as well as Bayesian Networks. It aims to give a comprehensive yet practical guide to how one may validate each of these and provide assurance that the model is appropriate for a firm’s operational risk profile.

Risk assessment and electrical control of high-permeability renewable energy photovoltaic system under extreme weather conditions

Abstract High-permeability photovoltaic (PV) systems are a key technical way to bring about new energy changes in electric power networks. Risk assessment and electrical regulation during extreme weather can make PV systems with high permeability work much more efficiently during severe weather. We establish an operational risk model for a high penetration rate PV system using the PV probability model, load parameter model, and probabilistic trend calculation model. Based on the results of the tidal current calculation, the risk assessment indexes of PV systems are constructed, including the voltage overrun risk index and branch circuit tidal current overrun risk index. The Monte Carlo simulation method using the Latin hypercubic sampling method is used to simulate the operational loads of PV systems under extreme weather conditions such as windy, rainy, and snowy conditions and to form a fault simulation set. Finally, we implement the QMC method to assess the risk of PV systems with high permeability and use the RBF neural network design to achieve automatic control of PV electrical equipment. When the weather is the same extreme, the risk of overrun at each node goes up with the number of nodes. When the weather is different, the risk of voltage overrun at each node under ice-covered load goes up by about half compared to rain load, and the risk of active overrun at each branch goes up by almost six times compared to rain load.

Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study

In an effort to contribute a quantitative, objective and real-time tool to proactively and precisely manage the factors underlying and exacerbating operational risks, this pre-registered study executes the empirical methodology approved in the associated pre-registered report (Cornwell et al., 2023). The application of the Bayesian network-based approach to an Australian insurance company shows that integrating a financial institution's loss and operational data in this way can effectively model the probability of an operational loss event within its interconnected operational risk environment. Further insights and efficiencies are gained by modelling multiple operational loss events together, rather than in isolation. A novel two-module framework derived specifically for causal factors analysis from the resulting operational risk model helps to highlight the relative importance of causal factors, their collective effects and critical thresholds requiring proactivity. These insights derived from the framework are expected to be strategically valuable in helping an organisation design intentional and targeted controls for and monitoring of operational risks. Given existing knowledge of the improvements quantitative risk management tools make to risk management effectiveness and subsequently firm value, the enhanced risk management and the operational efficiencies this tool seeks to afford should ultimately contribute to driving financial performance and firm value.

The information value of past losses in operational risk

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 it allows regulators to enhance their understanding of banks’ operational risk. We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from their capturing hard-to-quantify factors such as the quality of operational risk controls, the risk culture and the risk appetite of the bank.

The Information Value of Past Losses in Operational Risk

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 banks’ operational risk. We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from them capturing hard to quantify factors such as the quality of operational risk controls, the risk culture, and the risk appetite of the bank.

Measuring tail operational risk in univariate and multivariate models with extreme losses

This paper considers some univariate and multivariate operational risk models, in which the loss severities are modeled by some weakly tail dependent and heavy-tailed positive random variables, and the loss frequency processes are some general counting processes. We derive some limit behaviors for the value-at-risk and conditional tail expectation of aggregate operational risks in such models. The methodology is based on capital approximation within the Basel II/III framework (the so-called loss distribution approach). We also conduct some simulation studies to check the accuracy of our approximations and the (in)sensitivity due to different dependence structures or to the heavy-tailedness of the severities.

Logic-based data-driven operational risk model for augmented downhole petroleum production systems

This paper presents an operational risk model for a pressure-augmented downhole petroleum production system. The model is built by integrating multilayer perceptron (MLP) and early warning index system (EWIS) with Bayesian network (BN). The introduced model employs its evidence-based dynamic risk features to monitor the associated operational risks of downhole pump discharge pressure, downhole pump intake pressure, downhole pump pressure difference, drawdown, and bottom-hole pressure. The evidence-based mechanism enables the proposed model to accurately predict the resultant real-time production risks as the wells are being produced from the reservoirs. Hence, the model facilitates management decisions to make operational adjustments to avert downtime or “no flow”. The model captures the temporal and spatial dependence of the variables. The failure probabilities of the downhole pressure system are modelled as a function of time while using the evidence-based risk model. The results demonstrate downhole process system's contribution to the overall risk and its vulnerability to the overall production scenarios. The proposed novel strategy can simulate the progressive cavity pump (PCP) impacts on reservoir systems during production. The introduced model serves an important tool for operational decision-making to manage risks of reservoir systems equipped with downhole pressure pumps for production.

Modeling and probabilistic analysis of civil aircraft operational risk for suborbital disintegration accidents.

To reduce the collision risk to civil airliners caused by suborbital vehicle disintegration events, this paper uses a covariance propagation algorithm to model the debris landing point of suborbital disintegration accidents and gives a collision probability analysis method for civil airliners encountering debris during the cruise. Collision warning is performed for airborne risk targets to improve the emergency response capability of the ATC surveillance system to hazardous situations. The algorithm models the three-dimensional spatial motion target localization problem as a Gauss-Markov process, quantifying the location of debris landing points in the vicinity of nominal trajectories. By predicting the aircraft trajectory, the calculation of the inter-target collision probability is converted into an integration problem of a two-dimensional normally distributed probability density function in a circular domain. Compared with the traditional Monte Carlo method, the calculation speed of debris drop points is improved, which can meet the requirements of civil aviation for real-time response to unexpected situations.

Press freedom and operational losses: The monitoring role of the media

We investigate the monitoring role of the media, its detection and deterrence effects in corporate operational losses. Relying on our theoretical model, we analyze 8,144 loss events from 132 countries between 2008 and 2019. Controlling for factors related to governance, living standards, business cycles, and firm size, we find that press freedom has a significant positive effect both on the frequency and severity of the observed operationallosses. An improvement of one standard deviation in press freedom yields around 43% more and 71% higher public losses. Our estimations on hidden losses indicate that the worldwide detection rate of operational losses might be smaller than 53% and 13% in terms of number and value, respectively. Furthermore, in countries with a tightly controlled media, hidden operational risks might be tremendous. We suggest using public databases more carefully, adjusting operational risk models for the reporting bias, and promoting press freedom to improve corporate governance structures.

Multi-objective unit commitment of jointly concentrating solar power plant and wind farm for providing peak-shaving considering operational risk

The large-scale integration of intermittent and uncertain renewable energy poses challenges for power system scheduling, especially for peak-shaving. In this paper, a multi-objective unit commitment model of jointly concentrating solar power plant and wind farm for providing peak-shaving considering operational risk (RMUC) is proposed. A concentrating solar power (CSP) plant is employed to improve renewable energy consumption and compensate wind power fluctuations. Firstly, an operational risk model under renewable energy integration with intermittency is constructed to quantify risks of wind curtailment and load shedding. Then, a novel RMUC model is established by incorporating operational risk into the unit commitment (UC) model, which can optimally allocate operational flexibility of power systems over spatial and temporal domains to reduce operational risk. The proposed model can co-optimize the uncertainty level and the peak-shaving operation, which is able to obtain an optimal trade-off between peak-shaving effect and reliability. Finally, the proposed model is applied on an IEEE six-bus test system and on a simplified real power system for verification, and the cost-effectiveness of the CSP plant in reducing the operational risk and providing peak-shaving is quantified.

A Wind Power Accommodation Capability Assessment Method for Multi-Energy Microgrids

With the increase in wind generation installed capacity, the uncertainty of wind power has brought great challenges to wind power accommodation. The accurate assessment result of wind power accommodation capability (WPAC) has important guiding significance to the formulation of the day-ahead wind power trading plan for the multi-energy microgrid (MEMG). This paper proposes a WPAC assessment method for the MEMG. A liner energy flow model is first derived based on the equivalent infinitesimal replacement and the mild assumption that supply temperatures of different heat load nodes are nearly the same. Then, the operational risk model easily embedded into the assessment model is established based on the probability distributions of prediction error corresponding to multiple wind power output ranges. Finally, the WPAC assessment model is constructed as a two-stage robust optimization problem and solved by the column-and-constraint generation (C&CG) algorithm. Simulations on a MEMG composed of the IEEE 33-bus power system and the 23-node district heating network (DHN) verify the effectiveness of the proposed method.

Exploring and Validating Container Operational Risk Scale in Container Shipping: The Case of Ethiopian Shipping and Logistics Service Enterprise

The risk associated with container shipping has been a major concern in recent decades. This study presents three major risk frameworks to systematically and inclusively explore and validate container operational risk scales based on risk factors derived from the extant literature. The three risk frameworks identified are risks related to information flow, risks related to physical flow, and risks related to payment flow. Each risk factor is grouped into sub-factors (dimensions), three factors for information flow, two factors for physical flow, and two factors for payment flow. The study uses Ethiopia as a case study and employed both qualitative and quantitative research methods. An interview survey was conducted to explore additional risk factors and validate the identified risk factors in container shipping, and a questionnaire survey was then accompanied to collect the relevant data. A pairwise comparison chart (PCC) was employed to rank the risk dimensions. The results showed that the container operational risk model is satisfactory by employing exploratory and confirmatory factor analysis. Furthermore, the PCC result indicates that risk of loss or damage of goods/assets, payment delay, and decrease in or total loss of payment were ranked first, second, and third, respectively, and consequently the most significant dimensions of the risk factors. This study provides a reliable and valid scale for measuring container operational risk in container shipping companies. It also unlocks future works for using the identified risk factors as guidelines for researchers and experts to design and develop container operational risk dimensions.

STS Adult Cardiac Surgery Database: 2021 Update on Outcomes, Quality, and Research

The Society of Thoracic Surgeons Adult Cardiac Surgery Database is the most mature and comprehensive cardiac surgery database. It has been the foundation for quality measurement and improvement activities in cardiac surgery, facilitated the generation of accurate risk adjusted performance benchmarks and serves as a platform for novel research. Recent enhancements have added to the database's functionality, ease of use, and value to multiple stakeholders. This report is the sixth in a series of annual reports that provide updated volumes, outcomes, database-related developments, quality improvement initiatives, and research summaries using the Adult Cardiac Surgery Database in the past year.

Delta operators, power series distributions and recursions for compound sums

This paper uses some basic elements of the finite operator calculus to develop a new recursive method for calculating the probability distribution of a compound sum. The counting random variable here has a power series distribution of convolution type, which allows us to cover and extend a large number of classical counting distributions. This new approach is introduced in an actuarial framework and leads us to generalize the famous Panjer algorithm whose applications are numerous in collective, operational and credit risk models.

Вимірювання (оцінка) та моделювання операційного ризику банку

In modern conditions of implementing banking activity under the influence of rapid technologization, the worldwide globalization and transformation processes, there is a significant vulnerability to risks. The risks that banks are exposed to during their activities can be divided into those that are subject to quantitative measurement (assessment) and those that cannot be quantified. Among all banking risks, operational risk has the greatest unpredictability and destructive nature of influence on the bank, able of instantly jeopardizing as the bank’s activities so its functioning. Operational risk is one of the risks that are inherent in quantitative measurement (assessment). Measurement (assessment) of the bank’s operational risk is an element of management of such a risk. The impact of operational risk on banking activities is manifested in the amount of economic effect (losses). To assess the economic effect of the impact of operational risk on banks, a retrospective approach was used as part of the study described in the article. The study on assessment of losses from operational risk was carried out on the basis of analysis of statistical data on the operational risk events in both the worldwide and the domestic banking practice for the last 30 years: from 1991 to 2021. Since the norms for maintaining databases of internal and external events of operational risk by the banks of Ukraine are correspondingly applied as mandatory and additional instruments for measuring operational risk since 2018, the authors of the article carry out modeling and analysis of the aforementioned databases of operational risk events on the basis of statistical data on the operational risk events for 2018–2021. As a result of the study, the model of measurement (assessment) of economic effect (losses) of operational risk, taking into account its sources, is specified. The model approbation in practical banking activities will increase efficiency of the operational risk management. A prospect for further research on measurement (assessment) and modeling of operational risk can be the accumulation of data on operational risk events for more annual periods and the construction on their basis of the model of losses from the operational risk of bank.

Foundations & Quantitative Aspects of Operational Risk Modelling

Presentation on fundamentals of Operational Risk Modelling. The core components of a quantitative operational risk modelling framework. Based on references: 1. Cruz MG, Peters GW, Shevchenko PV. Fundamental aspects of operational risk and insurance analytics: A handbook of operational risk. John Wiley & Sons; 2015 Jan 29. 2. Peters GW, Shevchenko PV. Advances in heavy tailed risk modeling: A handbook of operational risk. John Wiley & Sons; 2015 May 5.

On the Kesten-type inequality for randomly weighted sums with applications to an operational risk model

This paper considers the randomly weighted sums generated by some dependent subexponential primary random variables and some arbitrarily dependent random weights. To study the randomly weighted sums with infinitely many terms, we establish a Kesten-type upper bound for their tail probabilities in presence of subexponential primary random variables and under a certain dependence among them. Our result extends the study of Chen [5] to the dependent case. As applications, we derive some asymptotic formulas for the tail probability and the Value-at-Risk of total aggregate loss in a multivariate operational risk cell model.