Risk Management Metrics Research Articles

Cross contamination of Escherichia coli O157:H7 in fresh-cut leafy vegetables: Derivation of a food safety objective and other risk management metrics

In the present study, the distribution of Escherichia coli O157:H7 was investigated in fresh-cut lettuce by simulating a commercial process at a pilot plant scale with different initial inoculum levels. A deterministic approach was used to derive a potential Food Safety Objective (FSO) for the studied pathogen. The experimental outcomes, together with literature data, were used to develop a probabilistic exposure model for E. coli O157:H7 to elucidate potential risk management metrics, i.e., Performance Objectives (POs) and a Performance Criterion (PC) at specific process steps within the food chain of fresh-cut leafy vegetables. The proposed FSO was estimated to be −6.0 log CFU/g for E. coli O157:H7 at consumption. The experimental results indicated that the pathogen was homogeneously distributed in fresh-cut lettuce after processing. Based on the modelling results, the average concentration estimated as PO in unprocessed lettuce should be −5.8 log CFU/g to meet the proposed FSO. On the other hand, the PO established after washing and packaging (processed lettuce) showed a lower value, with −6.8 log CFU/g E. coli O157:H7. Reduction levels during the washing or disinfection step estimated as the PC should be > 2.9 log CFU/g. The establishment of PC for the washing step could be considered as a suitable measure to reach the FSO in fresh-cut leafy vegetables. The suggested POs and PC would help food businesses and authorities to establish safety targets and corrective actions to reduce the number of potentially contaminated lettuce lots on the market, thus reducing the number of E. coli O157:H7 infections.

An age-friendly population health dashboard geolocating by clinical and social determinant needs.

An age-friendly population health dashboard geolocating by clinical and social determinant needs.

Climate change financial disclosure and firm performance: empirical evidence from Indian energy sector based on TCFD recommendations

PurposeThe paper aims to examine the climate change-related disclosure patterns of listed Indian firms and its impact on firm performance. Specifically, it strives to analyse the conformance of the selected firms with the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD) established by the Financial Stability Board of G20 nations.Design/methodology/approachThe study conducts content analysis of the annual reports and/or sustainability reports of 22 selected firms from the energy sector for the period spanning 2018–2019 and 2019–2020 based on the four-fold recommendations of TCFD, namely, governance, strategy, risk management and target and metrics, to compute the overall and respective climate-change disclosure scores. Further, a panel data regression model is used to appraise the impact of such disclosure on the performance of the firms.FindingsThe findings of the study indicate that the disclosure level of Indian firms in the energy sector is moderate. The regression results establish a positive relation between climate change-related financial disclosure and firm performance indicating that firms can witness improved financial performance by disclosing more information on climate change.Originality/valueThis is the first study in the Indian context to evaluate the climate change-related disclosure practices of the selected firms based on the TCFD’s recommendations and to trace its association with the performance of the firms. The results of the study shall hence be of relevance for the policymakers and diverse stakeholders.

MCMC-based credit rating aggregation algorithm to tackle data insufficiency

This paper investigates how credit rating aggregation might lead to a more efficient estimation of key portfolio risk management metrics: expected credit losses (ECL) and risk‐weighted assets (RWA). The proposed technique for credit rating aggregation is based on the Markov Chain Monte‐Carlo methodology and leads to a statistically smaller variance of ECL and RWA than the naïve and distribution‐based alternatives. This conclusion holds for three public datasets and four simulated studies. The paper results might be helpful for portfolios that suffer from data insufficiency or rely on external ratings for credit risk assessment: portfolios of international companies, interbank loans, and sovereign debt.

Differential Machine Learning

Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high-quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed-form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA. TensorFlow implementation is available on https://github.com/differential-machine-learning

Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility

Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly-traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability.

Climate risk reporting practices by UK insurance companies and pension schemes

Abstract Increasing global concern over the impact of climate change has recently led to public scrutiny over the adequacy of existing risk management practices by insurance companies and pension schemes in dealing with these challenges that potentially impact both individual actuaries and the Institute and Faculty of Actuaries generally. Most recently, the Prudential Regulation Authority has issued further guidance concerning its expectations for the UK insurance industry regarding the development of an approach to disclosure on and management of the financial risks from climate change, while a Parliamentary Committee has demanded public clarification from UK pension scheme trustees regarding their degree of engagement with incorporating climate-related financial risks into their investment decision-making. The aim of this paper is to identify the dominating factors of the current evolvement of UK insurance companies’ and pension schemes’ climate risk disclosure practices. This paper analyses both the nature and extent of changes in the risk reporting practices of these entities that have evolved in order to meet these demands for increased accountability. We first analyse relevant sections of latest annual reports produced by a sample of 15 UK insurance companies and 15 pension schemes. We find only limited alignment of insurance firm and pension scheme annual reports with the 11 specific Task Force on Climate-Related Financial Disclosure’s (TCFD) recommended disclosures. We also examine what key financial risk and/or other organisational characteristics are most closely associated with the degree of alignment with TCFD specified disclosures related to governance, strategy, risk management and performance metrics. We find that incentives facing sample insurance companies to align their climate-related disclosures with TCFD recommendations are related to their management of reputation risk (measured on the basis of size and type of business). Whereas the incentives facing pension schemes are related to the desire to reduce information asymmetry (measured by liability risk) among their stakeholders concerning this issue. Further, consistent with a stakeholder theory explanation, it appears that only a minority of large, publicly listed insurance companies and large local government pension schemes are taking action to report on their actions to mitigate climate risk. We also discuss examples of best practice climate risk reporting. The implications for the actuarial profession in engaging with climate risk are discussed in line with the findings of the study.

A QRM Discussion of Microbial Contamination of Non-sterile Drug Products, Using FDA and EMA Warning Letters Recorded between 2008 and 2016.

Microbial quality control of non-sterile drug products has been a concern to regulatory agencies and the pharmaceutical industry since the 1960s. Despite being an old challenge to companies, microbial contamination still affects a high number of manufacturers of non-sterile products. Consequences go well beyond the obvious direct costs related to batch rejections or product recalls, as human lives and a company's reputation are significantly impacted if such events occur. To better manage risk and establish effective mitigation strategies, it is necessary to understand the microbial hazards involved in non-sterile drug products manufacturing, be able to evaluate their potential impact on final product quality, and apply mitigation actions. Herein we discuss the most likely root causes involved in microbial contaminations referenced in warning letters issued by US health authorities and non-compliance reports issued by European health authorities over a period of several years. The quality risk management tools proposed were applied to the data gathered from those databases, and a generic risk ranking was provided based on a panel of non-sterile drug product manufacturers that was assembled and given the opportunity to perform the risk assessments. That panel identified gaps and defined potential mitigation actions, based on their own experience of potential risks expected for their processes. Major findings clearly indicate that the manufacturers affected by the warning letters should focus their attention on process improvements and microbial control strategies, especially those related to microbial analysis and raw material quality control. Additionally, the WLs considered frequently referred to failures in quality-related issues, which indicates that the quality commitment should be reinforced at most companies to avoid microbiological contaminations.LAY ABSTRACT: Microbial contamination of drug products affects the quality of non-sterile drug products produced by numerous manufacturers, representing a major risk to patients. It is necessary to understand the microbial hazards involved in the manufacturing process and evaluate their impact on final product quality so that effective prevention strategies can be implemented. A risk-based classification of most likely root causes for microbial contamination found in the warning letters issued by the US Food and Drug Administration and the European Medicines Agency is proposed. To validate the likely root causes extracted from the warning letters, a subject matter expert panel made of several manufacturers was formed and consulted. A quality risk management approach to assess microbiological contamination of non-sterile drug products is proposed for the identification of microbial hazards involved in the manufacturing process. To enable ranking of microbial contamination risks, quality risk management metrics related to criticality and overall risk were applied. The results showed that manufacturers of non-sterile drug products should improve their microbial control strategy, with special attention to quality controls of raw materials, primary containers, and closures. Besides that, they should invest in a more robust quality system and culture. As a start, manufacturers may consider investigating their specific microbiological risks, adressing their sites' own microbial ecology, type of manufacturing processes, and dosage form characteristics, as these may lead to increased contamination risks. Authorities should allow and enforce innovative, more comprehensive, and more effective approaches to in-process contamination monitoring and controls.

Effects of Operating Conditions on Gas Release Thermal Consequences: a case study of the Trans- Saharan Gas Pipeline

A recent study concluded that militant activities/insurgency constitutes the biggest threat to the proposed Trans-Saharan Gas Pipeline (TSGP) intended to traverse Nigeria, Niger and Algeria. Hence gas release is a credible source of concern. Using the potential leak of inventory from the TSGP as a case study; this paper assesses the Simplex Source Term and Multivariate Consequence Modelling approaches. Aspen HYSYS® platform was used to determine the average Molecular Weight and Lower Heat of Combustion of the gas mixture. Thereafter, the gas discharge rates for various leak scenarios were evaluated. The gas release rates and the flame length of the potential jet fires were initially estimated using Simplex Source Term Models which pay limited attention to operating conditions. Finally a more detailed follow-up study, accounting for a range of practical factors was conducted. A number of useful risk management metrics were determined. For example, the release rate for the 100mm leak is about 130.50 kg/s and 162.13 kg/s for scenarios with and without modifiers respectively. Similarly, the maximum flame lengths were found to be significantly different with values of 8m and 142m with and without modifiers respectively. The results show that the direct approach could be overly conservative, hence more expensive to implement.

Closing gaps for performing a risk assessment on Listeria monocytogenes in ready‐to‐eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage

Closing gaps for performing a risk assessment on Listeria monocytogenes in ready‐to‐eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage

Proposal of Risk Management Metrics for Multiple Project Software Development

Abstract Multiple project management currently is a reality in software development environments. In tactical and strategic context, the management of only one single project does not exist, and it is necessary the execution of projects simultaneously in order to achieve organizational objectives. In the case of software projects, some characteristics are highlighted, such as, constant changes in levels of scope or product, software complexity and aspects related to human resources, such as technical knowledge and experience, among others. We may consider these characteristics as risk factors that should be managed. In this aspect, a tactical management requires the usage of better-structured information, which leads us to think about the usage of a metrics-based strategy as a support tool for multiple project managers with emphasis on risk factors. In this context, a metric called “Risk Point”. This work presents and discusses the metric “Risk Point”, identifying some points of adjustments. Also, this paper shows an application of the metrics in an environment of multiple projects of software development with the goal of analyzing its applicability and utility as support tool for decision-making and risk monitoring during project life cycle.

Analysis of SCOR’s approach to supply chain risk management

Purpose – SCOR 10.0, released in late 2010, is the second version of the supply chain operations reference model (SCOR) to incorporate risk management processes, metrics and best practices. Given the paucity of studies that have explored the coverage and integration of supply chain risk management (SCRM) within SCOR, the analysis and suggested improvements for SCRM are designed to enhance SCOR’s collaborative and coordinated management of supply chain (SC) risks. The paper aims to dicsuss these issues. Design/methodology/approach – Critical analysis was used to analyse the coverage and integration of SCRM within SCOR 10.0. Findings – Discrepancies were identified in how SCRM has been incorporated into SCOR, including issues with the hierarchical representation of SCRM processes, metrics, best practices and skills. These may potentially propagate into difficulties in embedding risk management processes within other SC processes, visualizing risk metrics in a SC’s value hierarchy and reconciling SCOR’s SCRM with organizational enterprise risk management. Research limitations/implications – This paper is limited to theoretical analysis of the coverage and integration of risk in SCOR 10.0. Once the issues identified are remedied, the subsequent suggested improvements require validation through empirical testing. Originality/value – Despite SCOR’s wide acceptance as a reference model in managing SC operations, there has been no investigation of its approach to SCRM. The analysis addresses this lack of prior investigation by analysing SCRM in the latest version, SCOR 10.0. The paper identifies deficiencies and suggests amendments regarding SCRM’s coverage and integration of SCRM.

A Security Risk Management Metric for Cloud Computing Systems

Cloud computing is a growing technology used by several organizations because it presents a cost effective policy to manage and control Information Technology (IT). It delivers computing services as a public utility rather than a personal one. However, despite these benefits, it presents many challenges including access control and security problems. In order to assess security risks, the paper gives an overview of security risk management metrics. Then, it illustrates the use of a cyber security measure to describe an economic security model for cloud computing system. Moreover, it proposes a cloud provider business model for security issues. Finally, the paper shows a solution related to the vulnerabilities in cloud systems using a new quantitative metric to reduce the probability that an architectural components fails. The main aim of this article is to quantify security threats in cloud computing environments due to security breaches using a new security metric.

Measuring risk management performance within a capital program

Purpose – The purpose of this study is to describe how the University of Minnesota's capital program implemented risk management metrics on 266 construction projects and to present the results of the risk metrics. Design/methodology/approach – The implementation of Weekly Risk Reports (WRR) on the university construction projects captured information on the internal and external efforts related to minimizing project risks. The report implemented captured project risks, management plans, cost changes and schedule delays. Findings – Findings reveal that the university was able to effectively capture project risk metrics through the WRR. The risk metrics identified the risks categories that impacted the 266 project costs and schedules. Through these findings, the university has a better understanding of how their internal stakeholders create the greatest risk to impacting the project cost and schedule. This paper presents the risk impacts collected from the 266 projects. Research limitations/implications – A complete analysis of the risk metrics was limited in this research due to the extensive measurements collected. Future analysis will provide additional findings from the risk information. Originality/value – The paper presents both the implementation and the risk management measurements used within a capital program of a major university to provide understanding of the common risks that are involved with capital projects.

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk-based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo-contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What-if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.

Characterizing uncertainty when evaluating risk management metrics: Risk assessment modeling of Listeria monocytogenes contamination in ready-to-eat deli meats

This report illustrates how the uncertainty about food safety metrics may influence the selection of a performance objective (PO). To accomplish this goal, we developed a model concerning Listeria monocytogenes in ready-to-eat (RTE) deli meats. This application used a second order Monte Carlo model that simulates L. monocytogenes concentrations through a series of steps: the food-processing establishment, transport, retail, the consumer's home and consumption. The model accounted for growth inhibitor use, retail cross contamination, and applied an FAO/WHO dose response model for evaluating the probability of illness. An appropriate level of protection (ALOP) risk metric was selected as the average risk of illness per serving across all consumed servings-per-annum and the model was used to solve for the corresponding performance objective (PO) risk metric as the maximum allowable L. monocytogenes concentration (cfu/g) at the processing establishment where regulatory monitoring would occur. Given uncertainty about model inputs, an uncertainty distribution of the PO was estimated. Additionally, we considered how RTE deli meats contaminated at levels above the PO would be handled by the industry using three alternative approaches. Points on the PO distribution represent the probability that – if the industry complies with a particular PO – the resulting risk-per-serving is less than or equal to the target ALOP. For example, assuming 1) a target ALOP of −6.41 log10 risk of illness per serving, 2) industry concentrations above the PO that are re-distributed throughout the remaining concentration distribution and 3) no dose response uncertainty, establishment PO's of −4.98 and −4.39 log10 cfu/g would be required for 90% and 75% confidence that the target ALOP is met, respectively. The PO concentrations from this example scenario are more stringent than the current typical monitoring level of an absence in 25g (i.e. −1.40 log10 cfu/g) or a stricter criteria of absence in 125g (i.e., −2.1 log10 cfu/g). This example, and others, demonstrates that a PO for L. monocytogenes would be far below any current monitoring capabilities. Furthermore, this work highlights the demands placed on risk managers and risk assessors when applying uncertain risk models to the current risk metric framework.

Risk management towards food safety objective achievement regarding to mycotoxins in pistachio: The sampling and measurement uncertainty issue

The emerging risk management metrics, FSO, PO and PC, were applied to the aflatoxins (AFs) and ochratoxin A (OTA) determination in 8 commercial lots of pistachio. In order to determine the sampling uncertainty, two sampling plans (EU official sampling and company plan) in quadruplicate, and two analytical methods (ELISA and HPLC), were considered in parallel. The combination of EU official sampling plan and HPLC proved to be the most appropriate option. The major variability was associated with the subfraction selection and, therefore, increasing the number of the analysed subfractions could be an alternative for reducing uncertainty. AFs were present in all lots, mainly AFB1 and AFB2, while OTA was never detected. The effect of toasting on AFs presence in pistachio (a performance criteria, PC) was evaluated in order to achieve a given PO, taking into account the FSO, i.e., the EC limits. Percentages of AFs reduction were 87.62% ± 11.89, 81.05% ± 15.51 and 86.74% ± 11.31 for AFB1, AFB2 and total AFs, respectively. Given an initial AFB1 and AFBs level ≤12 μg/kg and ≤15 μg/kg, respectively, the toasting would ensure the AFB1 and AFs legal limits compliance before human consumption (FSO).

Safety assessment of smoked fish related to Listeria monocytogenes prevalence using risk management metrics

One of the fundamental objectives of European food law is the protection of human health. In this framework, the administration has to ensure that there are control measures from “farm to fork”, which maintain product safety in each stage of the food chain. With this in mind, the objective of this paper was to assess the level of safety of smoked fish in relation to Listeria monocytogenes in the early stages of the chain. This was carried out by evaluating the results obtained by the official control of the Valencian region related to the level of implementation of pre-requisites and HACCP. The prevalence of this organism in the industry and the retail stage was also measured. In order to discern whether these values were within the international consumer protection objectives a practical case focusing on smoked salmon was studied. The results showed that the management system in the industry is effective. However, there is a real increase in the prevalence in the samples taken in the supermarket. The ALOP values estimated for smoked salmon indicated that the level of safety achieved is good in a very high percentage of cases, though governments and the different agents in the food chain must continue working to improve and attain new safety goals.

Directional entropy and tail uncertainty, with applications to financial hazard

“Mine is a long and sad tale”, said the Mouse, turning to Alice and sighing. “It is a long tail certainly,” said Alice, looking down with wonder at the Mouse's tail; “but why do you call it sad?” And she kept on puzzling about it while the mouse was speaking … Financial risk management metrics such as value at risk (VaR) can be illuminated by means of a regime-specific concept of directional entropy. This enables a change of measure via a rescaling function to an equivalent logistic distribution, one that has the same total and directional entropies at the chosen critical point. VaR rescaling adjusts the critical probability to capture the long tail entropy. The scaling function can be used as a comparative metric for tail length, or equivalent conditional value at risk, even where moments do not exist. Directional entropy can also be used to identify regions of maximal exposure to new information, which can actually increase entropy rather than collapse it.

How Useful Are Banks' Earnings-At-Risk And Economic Value Of Equity-At-Risk Public Disclosures?

This paper examines the information content and the usefulness of banks' interest rate risk public disclosures. ALM managers use Earnings at Risk ( EAR ) and Economic Value of Equity at Risk ( EVEAR ) as measures of the dollar amount of potential loss to net interest income and common shareholders' equity as a result of unforeseen interest rate changes. These two interest rate risk management metrics are now recognized benchmarks for measuring interest rate risk exposure, and its potential impact on a bank's financial position. At the explicit request of regulators, financial analysts and competitive pressures, more commercial banks are now reporting EAR and EVEAR numbers in their annual financial reports. To examine preliminary evidence on the information content of such public disclosures, we composed a sample of some of North America's largest commercial banks. The Canadian peer group is based on Canada's seven largest banks, and the U.S. peer group is composed of twelve of its largest banks. In particular, we investigate if "ex ante" EAR and EVEAR numbers help regulators, financial analysts and investors to explain the subsequent variability of commercial banks' net interest income and net income over time.