Dynamic Risk Assessment Model Research Articles

A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks

Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment. According to the Basel 2 guidelines, banks need to develop their own credit risk assessment systems. Some banks have such systems; nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers’ defaults. Traditionally, banks have used static models with demographic or static factors to model credit risk patterns. However, economic factors are not independent of political fluctuations, and as the political environment changes, the economic environment evolves with it.This has been especially evident in Iran after the 2008–2016 USA sanctions, as many previously reliable customers became unable to repay their debt (i.e., became bad customers). Nevertheless, a dynamic model that can accommodate fluctuating politico-economic factors has never been developed. In this paper, we propose a model that can accommodate factors associated with politico-economic crises. Human judgement is removed from the customer evaluation process. We used a fuzzy inference system to create a rule base using a set of uncertainty predictors. First, we train an adaptive network-based fuzzy inference system (ANFIS) using monthly data from a customer profile dataset. Then, using the newly defined factors and their underlying rules, a second round of assessment begins in a fuzzy inference system.Thus, we present a model that is both more flexible to politico-economic factors and can yield results that are max compatible with real-life situations. Comparison between the prediction made by proposed model and a real non-performing loan indicates little difference between them. Credit risk specialists also approve the results. The major innovation of this research is producing a table of bad customers on a monthly basis and creating a dynamic model based on the table. The latest created model is used for assessing customers henceforth, so the whole process of customer assessment need not be repeated. We assert that this model is a good substitute for the static models currently in use as it can outperform traditional models, especially in the face of economic crisis.

Personalized risk assessment for dynamic transition of gastric neoplasms

BackgroundTo develop an individually-tailored dynamic risk assessment model following a multistep, multifactorial process of the Correa’s gastric cancer model.MethodsFirst, we estimated the state-to-state transition rates following Correa’s five-step carcinogenic model and assessed the effect of risk factors, including Helicobacter pylori infection, history of upper gastrointestinal disease, lifestyle, and dietary habits, on the step-by-step transition rates using data from a high-risk population in Matsu Islands, Taiwan. Second, we incorporated information on the gastric cancer carcinogenesis affected by genomic risk factors (including inherited susceptibility and irreversible genomic changes) based on literature to generate a genetic and epigenetic risk assessment model by using a simulated cohort identical to the Matsu population. The combination of conventional and genomic risk factors enables us to develop the personalized transition risk scores and composite scores.ResultsThe state-by-state transition rates per year were 0.0053, 0.7523, 0.1750, and 0.0121 per year from normal mucosa to chronic active gastritis, chronic active gastritis to atrophic gastritis, atrophic gastritis to intestinal metaplasia, and intestinal metaplasia to gastric cancer, respectively. Compared with the median risk group, the most risky decile had a 5.22-fold risk of developing gastric cancer, and the least risky decile around one-twelfth of the risk. The median 10-year risk for gastric cancer incidence was 0.77%. The median lifetime risk for gastric cancer incidence was 5.43%. By decile, the 10-year risk ranged from 0.06 to 4.04% and the lifetime risk ranged from 0.42 to 21.04%.ConclusionsWe demonstrate how to develop a personalized dynamic risk assessment model with the underpinning of Correa’s cascade to stratify the population according to their risk for progression to gastric cancer. Such a risk assessment model not only facilitates the development of an individually-tailored preventive strategy with treatment for H. pylori infection and endoscopic screening but also provides short-term and long-term indicators to evaluate the program effectiveness.

Real time risk analysis of kick detection: Testing and validation

Oil and gas development is moving into harsh and remote locations where the highest level of safety is required. A blowout is one of the most feared accidents in oil and gas developments projects. The main objective of this paper is to test and validate the kick detection of blowout risk assessment model using uniquely developed experimental results. Kick detection is a major part of the blowout risk assessment model. The accuracy and timeliness of kick detection are dependent on the monitoring of multiple downhole parameters such as downhole pressure, fluid density, fluid conductivity and mass flow rate. In the present study these four parameters are considered in different logical combinations to assess the occurrence of kick and associated blowout risk. The assessed results are compared against the experimental observations. It is observed that simultaneous monitoring of mass flow rate combined with any one the three parameters provides most reliable detection of kick and potential blowout likelihood. The current work presents the framework for a dynamic risk assessment and management model. Upon success testing of this approach at the pilot and field levels, this approach could provide a paradigm shift in drilling safety.

Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China

Waterlogging disasters are one of the most destructive meteorological disasters, which lead to crop yield reduction and cause a great threat to humanity and economic structure. This study presents the methodology and procedure for dynamic risk assessment of waterlogging disasters for maize in Midwest of Jilin Province, China. We took the representative waterlogging disaster years of 1994, 2005, and 2010 as examples, the growth-stage waterlogging index was established to assess the waterlogging disaster hazard by using standard antecedent precipitation index and the relative humidity index. Maize growing data and maize planting area data were combined to assess the waterlogging disaster vulnerability of maize, in which the CERES-Maize model was used to simulate the growth of maize at a daily time step for each grid. Based on the theory of natural disaster risk, the dynamic risk assessment model of waterlogging disaster for maize was built. In this study, the risk indexes were divided into five classes by using an optimal partition method. The grid GIS technology was used to map the spatial distribution of data and the grade of waterlogging disaster risk at a resolution of 5000 × 5000 m. The results show that areas with very low waterlogging disaster risk are mainly located in western and northeastern regions; in contrast, very high and high waterlogging disaster risk levels are mainly located in southern and central regions. Meanwhile, high risk areas at different growth stages gradually spread from the southwestern to the Midwestern and southeastern regions. This study could help the government when they make strategic decisions regarding food security in China, and the method of dynamic waterlogging risk disaster assessment could also be applied for other crops to control and prevent the occurrence and development of waterlogging disasters and reduce their adverse influence.

Risk analysis for rumor propagation in metropolises based on improved 8-state ICSAR model and dynamic personal activity trajectories

Social media has developed extremely fast in metropolises in recent years resulting in more and more rumors disturbing our daily lives. Knowing the characteristics of rumor propagation in metropolises can help the government make efficient rumor refutation plans. In this paper, we established a dynamic spatio-temporal comprehensive risk assessment model for rumor propagation based on an improved 8-state ICSAR model (Ignorant, Information Carrier, Information Spreader, Advocate, Removal), large personal activity trajectory data, and governmental rumor refutation (anti-rumor) scenarios. Combining these relevant data with the ‘big’ traffic data on the use of subways, buses, and taxis, we simulated daily oral communications among inhabitants in Beijing. In order to analyze rumor and anti-rumor competition in the actual social network, personal resistance, personal preference, conformity, rumor intensity, government rumor refutation and other influencing factors were considered. Based on the developed risk assessment model, a long-term dynamic rumor propagation simulation for a seven day period was conducted and a comprehensive rumor propagation risk distribution map was obtained. A set of the sensitivity analyses were conducted for different social media and propagation routes. We assessed different anti-rumor coverage ratios and the rumor-spreading thresholds at which the government started to launch anti-rumor actions. The results we obtained provide worthwhile references useful for governmental decision making towards control of social-disrupting rumors.

Toward a Behavioral Model of “Homegrown” Radicalization Trajectories

This research note presents a dynamic risk assessment model of homegrown terrorists. The model was tested in a study of convicted “homegrown” American terrorism offenders inspired by Al Qaeda's ideology. The New York Police Department model developed by Silber and Bhatt was chosen as the basis for creating a typology of overt and detectable indicators of individual behaviors widely thought to be associated with extremism. Twenty-four specific cues associated with each stage of radicalization were used to code and estimate the sequencing of behaviors and the duration of the average radicalization trajectory. Sixty-eight cases have been analyzed thus far. A decision was made to publish the initial results when it became apparent that the model was an effective tool for the evaluation of terrorist offenders.

Grid based dynamic risk assessment for grassland fire disaster in Hulunbuir

Grassland fire disaster is one of the most destructive grassland disasters, which is sudden, destructive, and hard to handle. It caused a great threat to the humanity and property in pastoral. In this study, a dynamic risk assessment model was built based on the data of prairie fire statistics and related meteorological data from 1994 to 2005 in the six livestock counties of Hulunbuir grassland. Logistic regression model was used in the identification of the key factors influence grassland fire disaster risk. In the calculation of the weight of individual indicators, analytic hierarchy through scoring by experts was used. Grid GIS technology combined with regression analysis was used in the spatial distribution of indicators which has higher resolution than counties. Assessment factors of endogenous and exogenous sources of warning signs of grassland fires were analyzed through weighted comprehensive analysis. Fire disasters from 1994 to 2004 were taken as the sample cases to determine the threshold of risk level by using optimal partition method. Taking the grassland fires in 2005 as examples to validate the dynamic risk assessment model, results shown high risk areas coincide well with the distribution of fire points, this proved the accuracy of the model. The dynamic risk assessment results could be used to guide and manage the mitigation of grassland fire disaster and rescue goods distribution.

Dynamic risk assessment model for flood disaster on a projection pursuit cluster and its application

Aiming at reducing the losses from flood disaster, a dynamic risk assessment model for flood disaster is studied in this article. This model is built upon the projection pursuit cluster principle and risk indexes in the system, proceeding from the whole structure to its component parts. In this study, a fuzzy analytic hierarchy approach is employed to screen out the index system and determine the index weight, while the future value of each index is simulated by an improved back-propagation neural network algorithm. The proposed model adopts a dynamic evaluation method to analyze temporal data and assesses risk development by comprehensive analysis. The projection pursuit theory is used for clustering spatial data. The optimal projection vector is applied to calculate the risk cluster type. Therefore, the flood disaster risk level is confirmed and then the local conditions for presenting the control strategy. This study takes the Tunxi area, Huangshan city, as an example. After dynamic risk assessment model establishment, verification and application for flood disasters between the actual and simulated data from 2001 to 2013, the comprehensive risk assessment results show that the development trend for flood disaster risk is still in a decline on the whole, despite the rise in a few years. This is in accordance with the actual conditions. The proposed model is shown to be feasible for theory and application, providing a new way to assess flood disaster risk.

Risk-assessment models for predicting venous thromboembolism among hospitalized non-surgical patients: a systematic review

Venous thromboembolism (VTE) prophylaxis is suboptimal in American hospitals despite long-standing evidence-based recommendations. Data from observational studies indicate a lower uptake of effective prophylaxis in patients hospitalized with medical versus surgical conditions. Reluctance to use prophylaxis in medical patients has been attributed to difficulty in identifying at-risk patients and balancing risks of bleeding against occurrence of VTE. Several risk-assessment models (RAMs) have been proposed to assist physicians in identifying non-surgical patients who need prophylaxis. We conducted a systematic review of published RAMs, based on objective criteria, to determine whether any RAM is validated sufficiently to be employed in clinical practice. We identified 11 RAMs, six derived from primary data and five based on expert opinion. The number, types, and strength of association of VTE risk predictors were highly variable. The variability in methods and outcome measurement precluded pooled estimates of these different models. Published RAMs for VTE lack generalizability and adequate validation. As electronic health records become more ubiquitous, validated dynamic RAMs are needed to assess VTE risk at the point-of-care in real time.

Dynamic Investment Risk Assessment Model for Intelligent Control Firms

Refer to each stage of the investment in intelligent control firms, the risk is a dynamic system with complexity for the uncertain factors existing in the process of risk estimation. Through the analysis on the risk of the investment in intelligent control firms, this article constructs a dynamic model of intelligent control firms risk estimation, which is characterized by multi-stage, multi-factor and multi-actors, with the integration of many influence factors such as the attitude of risk estimator. Moreover, based on the method of D-S evidential reasoning method, this model could transform those qualitative indexes with influence on risk into the quantitative indexes, which has the advantage to reasonably and synthetically estimate the dynamic risk of the investment in the intelligent control firms.

A dynamic risk assessment model for the management of tuberculosis in the UK

A dynamic risk assessment model for the management of tuberculosis in the UK

Dynamic risk assessment model for water quality on projection pursuit cluster

With the aim of reducing the losses from water pollution, a dynamic risk assessment model for water quality is studied in this paper. This model is built on the projection pursuit cluster principle and risk indexes in the complex system, proceeding from the whole structure and its component parts. In this paper, the fuzzy analytic hierarchy process is used to screen out index system and determine index weight, while the further value of an index is simulated by hydrological model. The proposed model adopts the comprehensive dynamic evaluation method to analyze the time dimension data, and evaluates the development tendency by combining qualitative analysis with quantitative analysis. The projection pursuit theory is also employed for clustering the spatial dimension data, the optimal projection vector for calculating risk cluster type to compartmentalize risk, and then local conditions for proposing the regulation scheme. The applicational results show that the model has the strong logic superiority and regional adaptability with strict theoretical system, flexible methods, correct and reasonable results and simple implementation to provide a new way for research on risk assessment models of water quality.

Dynamic risk assessment of oil spill scenario for Three Gorges Reservoir in China based on numerical simulation

A novel method for dynamic risk assessment of oil spill accidents based on numerical simulation was presented in this paper. The dynamic risk assessment model was developed consisting of a comprehensive list of caused consequences like environmental damage, asset loss, health impact and social effect as well as emergency actions preventing these losses. Contributing events in the Mater Logic Diagram (MLD) of the dynamic risk assessment model were valued based on the simulated evolvement of oil contaminants under spill scenario on quasi-static fluid, which was obtained by coupling an oil spill model with hydrodynamic module in Jialing river of Three Gorges Reservoir in China after the impoundment of the reservoir to 175m water level in 2010. Calculated result of dynamic risk as grade IV indicated that the assessed oil spill was not as catastrophic as we thought because of the slow transport of oil fractions on water surface and absence of dispersed oil in water body due to the quasi-static fluid, very gentle wind and effective emergency actions, as well as by the reason that rare agriculture or industrial crops exist in spill adjacent area.

Managing the risks of physical intervention: developing a more inclusive approach

Since psychiatry evolved as a professional discipline, mental healthcare professionals have had to, as a last resort, physically intervene to manage physically aggressive patients. In the United Kingdom, physical intervention techniques migrated from the prison service in the mid 1980s where there was extensive use of two particularly controversial practices; 'pain compliance' and the 'prone restraint position'. This paper examines how the classification of the 'prone restraint position' has led to a narrowed focus on one technique and a resultant misunderstanding around the wider risks associated with the applying force and managing restraints. The paper goes on to propose the 'transitional stabilizing position' (TSP) as an alternative concept and puts forward a dynamic risk assessment model. It explores how a shift in staff training away from developing pure competence in the performance of tightly specified techniques to managing the patient in TSPs could conceivably reduce the relatively low risk of death or serious injury associated with the application of restraint techniques to an even lower level.

An uncertainty and sensitivity analysis of dynamic operational risk assessment model: A case study

In Dynamic Operational Risk Assessment (DORA) models, component repair time is an important parameter to characterize component state and the subsequent system-state trajectory. Specific distributions are fit to the industrial component repair time to be used as the input of Monte Carlo simulation of system-state trajectory. The objective of this study is to propose and apply statistical techniques to characterize the uncertainty and sensitivity on the distribution model selection and the associated parameters determination, in order to study how the DORA output that is the probability of operation out-of-control, can be apportioned by the distribution model selection. In this study, eight distribution fittings for each component are performed. Chi-square test, Kolmogorov–Smirnov test, and Anderson-Darling test are proposed to measure the goodness-of-fit to rank the distribution models for characterizing the component repair time distribution. Sensitivity analysis results show that the selection of distribution model among exponential distribution, gamma distribution, lognormal distribution and Weibull distribution to fit the industrial data has no significant impact on DORA results in the case study.

A dynamic risk assessment model (FUMAgrain) of fumonisin synthesis by Fusarium verticillioides in maize grain in Italy

Fumonisin contamination of maize grain starts in the field. Forecasting Fusarium infection and fumonisin synthesis could allow operators in the field to control contamination during the growing season and to make the best agronomic decisions for high quality yields while respecting the limits imposed by the European Union. A research project to develop a decision support system for the control of field-phase fumonisin contamination began in Italy in 2003. This paper presents a preliminary version of the engine of the decision support tool: FUMAgrain, a dynamic risk assessment model developed with data from the north of Italy. The structure of FUMAgrain is based on the pathosystem formed by maize, F. verticillioides and Ostrinia nubilalis (European Corn Borer). The elements of the pathosystem are simulated by three sub-models: (i) maize development, (ii) F. verticillioides infection and fumonisin synthesis, (iii) European Corn Borer wounding activity on maize grain. Inputs to the model are (i) planting date, (ii) hourly meteorological data including temperature, relative humidity, wind speed and rain intensity, (iii) information on the phenological development of the hybrid planted (flowering and dry-down), and (iv) information about the chemical treatment against European Corn Borer. FUMAgrain gives an initial risk alert at the end of flowering based on the meteorological conditions during this phase. A second alert follows maturation when an assessment is made from (i) maize grain moisture, (ii) European Corn Borer damage to the ear, and (iii) fumonisin synthesis risk. Following calibration and validation with data FUMAgrain demonstrated its good capability to simulate fumonisin synthesis in maize grain in Italy (calibration: R 2 = 0.70; validation: R 2 = 0.71) and its usefulness for determining the optimal harvest date while respecting grain safety levels required by the international market and limiting moisture content, hence drying costs.

A model for radiological consequences of nuclear power plant operational atmospheric releases

A dynamic dose and risk assessment model is developed to estimate radiological consequences of atmospheric emissions from nuclear power plants. Internal exposure via inhalation and ingestion, external exposure from clouds and radioactivity deposited on the ground are included in the model. The model allows to simulate interregional moves of people and multi-location food supply in the computational domain. Any long-range atmospheric dispersion model which yields radionuclide concentrations in air and on the ground at predetermined time intervals can easily be integrated into the model. The software developed is validated against radionuclide concentrations measured in different environmental media and dose values estimated after the Chernobyl accident. Results obtained using the model compare well with dose estimates and activities measured in foodstuffs and feedstuffs.

Dynamic security risk assessment and optimization of power transmission system

The paper presents a practical dynamic security region (PDSR) based dynamic security risk assessment and optimization model for power transmission system. The cost of comprehensive security control and the influence of uncertainties of power injections are considered in the model of dynamic security risk assessment. The transient stability constraints and uncertainties of power injections can be considered easily by PDSR in form of hyper-box. A method to define and classify contingency set is presented, and a risk control optimization model is given which takes total dynamic insecurity risk as the objective function for a dominant contingency set. An optimal solution of dynamic insecurity risk is obtained by optimizing preventive and emergency control cost and contingency set decomposition. The effectiveness of this model has been proved by test results on the New England 10-generator 39-bus system.

Forecasting mortality: dynamic assessment of risk in ST-segment elevation acute myocardial infarction

To demonstrate the feasibility and clinical utility of developing dynamic risk assessment models for ST-segment elevation myocardial infarction (STEMI) patients. In 6066 STEMI patients enrolled in the Assessment of the Safety and Efficacy of a New Thrombolytic-3 (ASSENT-3) trial with complete electrocardiographic data, we assessed the probability of 30-day mortality over the following forecasting periods beginning at day 0 (baseline), 3 h, day 2, and day 5 using multiple-logistic regression. These models were validated and simplified in independent samples of 1622 similar fibrinolytic-treated patients from the ASSENT-3 PLUS trial and in 814 STEMI patients undergoing primary percutaneous coronary intervention in the COMplement inhibition in Myocardial infarction treated with Angioplasty (COMMA) trial. The discriminatory power of these predictive models, from baseline to day 5, was excellent (c-statistics 0.80 to 0.87); and their predictive ability was supported by strong gradients in mortality outcomes as the risk score increased. Dynamic modelling also provided information on the change in prognosis over time which may be used to advise more appropriate therapeutic decisions, e.g. the identification of high-risk patients for possible co-interventions. Dynamic modelling for STEMI patients enhances the risk assessment and stratification and should provide valuable ongoing guidance for their management.

Risk Assessment: A Means for Linking HACCP Plans and Public Health

HACCP plan adoption has greatly enhanced the food industry's ability to systematically design programs to ensure the microbiological safety of foods. Yet, this widening acceptance of the HACCP system has revealed several areas where its application is limited due to reliance on qualitative consideration of hazards and their control. In particular, HACCP planning is limited both conceptually and practically by its inability to quantify the potential combined influence of multiple control-point deviations and to relate the successful operation of a HACCP system to a measurable public-health impact. Recent advances in quantitative microbiological risk assessment appear to offer a means of overcoming these limitations. The integration of HACCP plans with the development of dynamic risk-assessment models offers a means for considering the entire farm-to-table continuum and for relating food-manufacturing operations to public health goals. Such capabilities may be critical to establishing equivalence among HACCP systems.