Risk Identification Model Research Articles

Research on Personal Credit Risk Assessment Based on Data Fusion

In recent years, the domestic emphasis on systemic risk prevention has continued to increase. Chinese credit department, which is a crucial department to avoid significant systemic risks, has a particular responsibility to manage and control personal credit risks related to the lives of the public. Therefore, strengthening the management of the individual credit department, especially to guard against personal credit risks, build a data-driven quantitative model for scoring applicants’ credit, and improve the personal credit identification and evaluation methods on this basis is a vital part of individual credit risk prevention and systemic financial risk. In this paper, we first study the index system of personal credit risk identification, standard personal credit risk identification models, and some data fusion techniques. Then, we build an individual credit risk identification model based on the data fusion method on the above theoretical basis. After that, the selection of indicators was carried out among 20 variables of German credit data. Finally, an empirical study was conducted to compare the effect of the two data fusion methods on the performance of the individual credit risk identification model. The defects of single models in the personal credit risk problem domain are apparent. However, data fusion technology can help classification models to gain better uniformity in accuracy and robustness, but it also needs to be improved in terms of interpretability and promotion.

HY-RISE: Towards Risk Identification Learning from Massive Scientific Economic Activities

Scientific economic activities document the utilization of research funds, which form a critical component of scientific research. Detecting potential risk behaviors from scientific economic activities is crucial to risk management for research institutions. Most of the existing attempts, however, tackle the problem with traditional machine learning algorithms, which rely on the manual feature extraction. Undoubtedly, these methods cannot extract complex semantic features or fuse information from hybrid data for effective risk identification. To overcome these challenges, in this paper, we propose a novel Risk Identification model for Scientific Economic activities from HYbrid data (HY-RISE), which incorporates both textual and structured data. Firstly, we use a pretrained BERT module to capture the semantic information from textual data. After that, we introduce a BiGRU module to augment the contextual information in semantic embeddings. Finally, we use a shallow neural network to fuse the augmented semantic representation with other discrete features to obtain the final representation. Experimental results on the real reimbursement dataset demonstrate that HY-RISE apparently outperforms existing models in terms of effectiveness and robustness for risk identification.

Obstacle-Avoidance Path-Planning Algorithm for Autonomous Vehicles Based on B-Spline Algorithm

To solve the problem of the real-time path-planning of autonomous vehicles for obstacle avoidance on structured roads, a path-planning approach based on the B-spline algorithm is proposed in this paper. Firstly, the mechanism of driver path planning is analyzed, and a dynamic risk-identification model based on the support vector machine is proposed. It combines the driver’s risk perception characteristics and a risk model. Then, the B-spline algorithm model is improved based on the risk-identification model. Furthermore, road features, road constraints and dynamic constraints are considered to further optimize the planning algorithm. To verify the path-planning approach proposed in this paper, a co-simulation experiment based on CarSim/Simulink is conducted. Results show that the improved algorithm is effective in static and dynamic obstacles avoidance. The algorithm can generate collision-free obstacle avoidance paths, and the paths meet the real-time requirements and dynamic constraints of obstacle avoidance scenarios. In addition, the proposed algorithm optimizes the path according to the driver’s operating characteristics, which can further improve the safety and comfort of autonomous vehicles.

Research on the Risk Warning and Control of the Project Progress of Beijing Daxing International Airport

<p>Based on the theory of progress control and risk management, this paper systematically sorts out the progress risk of Beijing Daxing International Airport, discusses the general schedule and progress risk identification model and its approach, and constructs multi-level early warning and dynamic monitoring methods for progress risk. Meanwhile, the method for general schedule and progress risk management of large-scale airport projects is also studied. The practice of early warning and control of the general schedule and progress risk of Daxing Airport project has achieved good results, which provides support for the comprehensive progress control of Daxing Airport project, and guarantees the realization of the target completion time of general schedule.</p>

Analysis of the Influence of Back Home to Start Undertaking and Rural Revitalization Based on Artificial Intelligence

The strategy of rural revitalization is to take measures to deal with the big problem of rural decline, mainly from the aspects of developing rural economy, getting rid of poverty, and adjusting rural structure, and changing the traditional thinking of rural development when implementing policies. With the number and scale of rural laborer’s back home to start undertaking expanding continuously, it is more important to improve the quality of rural laborer’s back home to start undertaking. On the basis of analyzing the motivation and problems of rural laborer’s back home to start undertaking, this paper puts forward a risk identification model based on artificial intelligence (AI) algorithm and explores the development path of rural laborer’s back home to start undertaking under the background of rural revitalization. Through simulation experiments, the effectiveness and superiority of this algorithm are analyzed. The results show that the accuracy rate of venture risk assessment in this paper is 93.95%, and the error is 10.24% lower than that of the back propagation neural network (BPNN). It can be seen that this method has a significant effect in the analysis of the risk and influencing factors of rural laborer’s back home to start undertaking. The overall requirements of rural revitalization strategy for industrial prosperity will inevitably encourage and attract more rural laborer’s to back home to start undertaking, and “starting a business to help the poor” will become a new path for rural poverty alleviation and development in the future.

Adopting An Affordability Approach to Responsible Gambling and Harm Reduction: Considerations for Implementation in a North American Context

The proliferation of gambling opportunities worldwide, including continuous online gambling, has generated concern over how to protect individuals and families from harm caused by excessive spending. In response, researchers and operators have worked with big data to develop risk-identification models to identify indicators of problem gambling. Such models are generally proprietary, non-transparent, and non-generalizable across games, jurisdictions, or player populations, rendering them impractical as regulatory tools. In North America, responsible gambling efforts largely place the onus on players to control their behavior; however, in the UK and elsewhere, regulations have shifted to a model of shared responsibility that targets ‘affordability,’ the amount individual players can afford to lose, instead of indicators of problem gambling. This affordability approach avoids the need for regulators and operators to be clinicians, attempting to identify disorder. Rather, it builds on existing systems to determine creditworthiness and player risk levels. Using affordability as the key benchmark for responsible gambling, we discuss approaches to operationalizing affordability guidelines in a North American context. Such guidelines will aid in promoting the objective identification of players who are spending beyond their means and facilitate the necessary transition to a shared responsibility model for harm reduction.

A computed tomography-based preoperative risk scoring system to distinguish lymphoepithelioma-like gastric carcinoma from non-lymphoepithelioma-like gastric carcinoma.

PurposeThe aim of this study was to develop a preoperative risk scoring model for distinguishing lymphoepithelioma-like gastric carcinoma (LELGC) from non-LELGC based on contrast-enhanced computed tomography (CT) images.MethodsClinicopathological features and CT findings of patients with LELGC and non-LELGC in our hospital from January 2016 to July 2022 were retrospectively analyzed and compared. A preoperative risk stratification model and a risk scoring system were developed using logistic regression.ResultsTwenty patients with LELGC and 40 patients with non-LELGC were included in the training cohort. Significant differences were observed in Epstein–Barr virus (EBV) infection and vascular invasion between the two groups (p < 0.05). Significant differences were observed in the distribution of location, enhancement pattern, homogeneous enhancement, CT-defined lymph node status, and attenuations in the non-contrast, arterial, and venous phases (all p < 0.05). Enhancement pattern, CT-defined lymph node status, and attenuation in venous phase were independent predictors of LELGC. The optimal cutoff score of distinguishing LELGC from non-LELGC was 3.5. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the training cohort were 0.904, 87.5%, 80.0%, and 85.0%, respectively. The area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of risk identification model in the validation cohort were 0.705 (95% CI 0.434–0.957), 75.0%, 63.6%, and 66.7%, respectively.ConclusionA preoperative risk identification model based on CT imaging data could be helpful for distinguishing LELGC from non-LELGC.

Green Supply Chain Management Model of e-Commerce Enterprises Based on SCOR Model

With the rapid development of e-commerce technology, e-commerce enterprises, especially B2C enterprises, have sprung up. This competition based on e-commerce technology has changed the traditional supply chain model and improved the operation efficiency of the supply chain. However, with the use of e-commerce technology, the supply and demand relationship between e-commerce enterprises has become extremely complicated. The original risk management system in the past has long been unable to adapt to the development needs of e-commerce enterprises in the new era. In view of this, it has far-reaching practical significance to actively carry out research on supply chain risk management for e-commerce enterprises, especially for B2C enterprises. In this research, on the premise of some previous theoretical and practical results, a B2C enterprise supply chain risk identification model is established by means of the research on SCOR, namely, the supply chain operation mode, combined with the characteristics of the B2C enterprise supply chain. The comprehensive evaluation method is used to evaluate the supply chain risk. Finally, an empirical study is carried out with J Company, which represents the B2C enterprise, as an example, which proves the applicability and reliability of this model. The results of this study have a certain reference value for the identification and control of B2C enterprise supply chain risks and also an inspiration for future research.

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver’s incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%.

Construction of a financial default risk prediction model based on the LightGBM algorithm

Abstract The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer (P2P) companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM (Light Gradient Boosting Machine) algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base LightGBM model along with the integration of linear blending to build an optimal default risk identification model. The proposed approach is applicable for a large number of multi-dimensional data samples. The results show that the prediction accuracy rate of the LightGBM algorithm model on the test set reaches 80.25%, which can accurately identify more than 80% of users, and the model has the best prediction performance in terms of different performance evaluation indicators. The integration of LightGBM and the linear blending approach yield a precision value of 91.36%, a recall of 75.90%, and an accuracy of 84.36%. The established LightGBM algorithm can efficiently identify the default of the loan business on the P2P platform compared to the traditional machine learning models, such as logistic regression and support vector machine. For a large number of multi-dimensional data samples, the LightGBM algorithm can effectively judge the default risk of users on P2P platforms.

Predictive risk modeling for major transportation projects using historical data

Most of the construction practices in the field of risk identification focus on the expertise, views, and judgments of subject matter experts. While the conventional expert-based approaches provide worth, several challenges exist due to time-consuming and expensive aspects. Moreover, limited experience in major projects makes public agencies susceptible to subjective judgment biases. To address these limitations, this study introduced a data-driven framework for risk identification using historical data and artificial intelligence techniques, particularly word embedding models. The model matches various risk items in past projects by considering the semantic meaning of words to find high frequency and consequence risks. Risk registers from more than 70 U.S. major transportation projects form the input dataset. The model is tested with more than 66% recall and 0.59 F1-score for risk detection for new projects. Acquired knowledge from previous projects assists project teams and public agencies to be well-equipped with a risk identification model instead of starting from scratch.

Risk Identification and Conflict Prediction from Videos Based on TTC-ML of a Multi-Lane Weaving Area

Crash risk identification and prediction are expected to play an important role in traffic accident prevention. However, most of the existing studies focus only on highways, not on multi-lane weaving areas. In this paper, a potential collision risk identification and conflict prediction model based on extending Time-to-Collision-Machine Learning (TTC-ML) for multi-lane weaving zone was proposed. The model can accurately learn various features, such as vehicle operation characteristics, risk and conflict distributions, and physical zoning characteristics in the weaving area. Specifically, TTC was used to capture the collision risk severity, and ML extracted vehicle trajectory features. After normalizing and dimensionality reduction of the vehicle trajectory dataset, Naive Bayes, Logistic Regression, and Gradient Boosting Decision Tree (GBDT) models were selected for traffic conflict prediction, and the experiments showed that the GBDT model outperforms two remaining models in terms of prediction accuracy, precision, false-positive rate (FPR) and Area Under Curve (AUC). The research findings of this paper help traffic management departments develop and optimize traffic control schemes, which can be applied to Intelligent Vehicle Infrastructure Cooperative Systems (IVICS) dynamic warning.

Risk identification of civil aviation engine control system based on particle swarm optimization-mean impact value-support vector machine

The actual risk of the engine control system in operation is often higher than expected risk level, which brings hidden dangers to the safe operation of aircraft. Therefore, a PSO-MIV-SVM (particle swarm optimization-mean impact value-support vector machine) model is proposed to identify the risk of engine control system. Firstly, seven characteristic variables are extracted through the analysis of engine unsafe information, and 810 typical engine event samples were selected and normalized. Secondly, the SVM-based engine control system risk identification model is established and optimal kernel function of the SVM model is selected. Thirdly, the SVM-MIV method is used to sort the importance of the seven characteristic variables, and the identification accuracies of different characteristic variable groups are calculated to obtain the optimal combination of characteristic variables. Finally, the parameters of the SVM model are optimized by using the PSO algorithm and the accuracy of the PSO-MIV-SVM model for the risk identification of the engine control system reaches 93.58%.

An Enhanced TSA-MLP Model for Identifying Credit Default Problems

Credit default has always been one of the critical factors in the development of personal credit business. By establishing a default identification model, default can be avoided effectively. There are some existing methods to identify credit default. However, these methods have some problems: Problem (1): It is different to deal the non-linear data, Problem (2): The local stagnation results in the high error rate, and Problem (3): The premature convergence leads to the low classification rate. In this paper, the sinhTSA-MLP default risk identification model is proposed to solve these problems. In this model, the proposed sinhTSA method can effectively avoid the problems of falling into local optimum and premature convergence. And the benchmark test results demonstrate sinhTSA is superior to other methods. According to the two experiments, the classification rate reaches 77.35% and 96.48%. Therefore, the sinhTSA-MLP default identification model has some particular advantages in identifying credit problems The feasibility of the sinhTSA-MLP default identification model has been proved through helping to manage credit default more consciously.

Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning

Maintaining the stability of highway soft rock slopes is of critical importance for ensuring the safety of road networks. Although much research has been carried out to assess the stability of individual soft rock slope, the goal of efficient and effective risk management focusing on multiple highway soft rock slopes has not been fully achieved due to the many complex factors involved and the interactions among these factors. In the present study, a machine learning algorithm based on a fuzzy neural network (FNN) and a comprehensive evaluation method based on the FNN is developed, in order to identify and issue early warnings regarding the risks induced by soft rock slopes along highways, in an efficient and effective way. Using a large amount of collected soft rock slope information as training and validation data, an FNN-based risk identification model is first developed to identify the risk level of individual soft rock slope based on the meteorological conditions, topographical and geomorphological factors, geotechnical properties, and the measured horizontal displacement. An FNN-based comprehensive evaluation method is then developed, in order to quantify the risk level of a soft rock slope group according to the slope, road and external factors. The results show that the risk level identification accuracy obtained based on validation of the FNN model was higher than 90%, and the model showed a good training effect. On this basis, we further made early warnings of the risks of soft rock slope groups. The proposed early-warning model can quickly and accurately evaluate the risk posed by multiple soft rock slopes to a highway, thereby ensuring the safety of the highway.

A financial risk identification model based on artificial intelligence

A financial risk identification model based on artificial intelligence

Identification and Correlation Analysis of Engineering Environmental Risk Factors along the Qinghai–Tibet Engineering Corridor

Global warming has increased the security risk of permafrost environment in the Tibetan Plateau, which has been threatening infrastructures along the Qinghai–Tibet Engineering Corridor (QTEC). Combined with the traditional risk identification and the causal feedback relationship of system dynamics, the authors present a novel engineering environment risk identification model including five risk subsystems, i.e., regional geomorphology, climate change, ecological environment, permafrost environment and water environment. Our model could successfully identify the interaction relationships and transmission path among risk factors of the environment of the QTEC. The basic data calculation, interaction degree analysis and regional distribution characteristic analysis of the identified risk factors were carried out by using a geographic information system (GIS), a partial correlation analysis and a zoning analysis. The results show that the static factors (i.e., elevation, slope, aspect, relief degree of land surface and volume ice content) mainly affected the spatial distribution of environmental risk factors, while the climate change factors (i.e., mean annual air temperature, mean annual precipitation and surface solar radiation), among the dynamic factors, were the root factors of the dynamic changes in environmental risks. The model identified five types of parallel risk paths in the QTEC. This novel method and proposed model can be used to identify and assess multi-scale engineering environmental risks in the cryosphere.

Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.

Comparison of traditional versus artificial intelligence based coronary artery disease risk prediction scores in young patients with acute coronary syndrome

Abstract Background Ever since the concept of preventive cardiology has come into vogue, several risk identification models have come up which combine several risk factors to create a risk prediction score for occurrence of cardiovascular (CV) event. While carrying a proven validation in Western population, none of the risk prediction model has been satisfactorily evaluated in Indians especially young &amp;lt;40 years old. Objectives To compare Artificial Intelligence based novel risk score with traditional risk scores in young (less than 40 years age) patients presenting with acute coronary syndrome (ACS) and to estimate the relative efficacy of different coronary artery disease (CAD) risk scores in young Indian Patients. Design Single center, Observational, Non-interventional study. Participants Cohort of Patients more than 20 but less than 40 years old with ACS in the department of Cardiology from 1st January 2019 to 31st October 2019. Methods 314 young patients [mean age 36.14±4.17 years] presenting with acute coronary syndrome (ACS) were enrolled. The three clinically most pertinent risk assessment models [Framingham Risk score (FRS), World Health Organization risk prediction charts (WHO/ISH), and QRISK3 scores] and Artificial Intelligence based novel risk score (AICVD) were applied on day 1 of presentation, and tried to see whether one risk score versus other risk score could have predicted the event earlier had we applied it before the occurrence of ACS. Risk factors considered included those already in traditional scoring systems and new risk factors (diet, alcohol, tobacco, dyslipidemia, physical activity, family history of heart disease, history of heart disease, heart rate, respiratory rate, chronic heart symptoms and psychological stress). Results WHO/ISH provided the lowest high risk estimate with only 1 (0.9%) patient estimated to be having &amp;gt;20% 10-year risk. The FRS estimated high risk (&amp;gt;20% 10-year risk) in 3 (1%) patients. The QRISK3 estimated high risk (&amp;gt;10% 10-year risk) in 20 (6.5%) patient. In comparison, AICVD risk prediction model stood tall by identifying 73 (23.2%) patients as high risk and 62.74% patients as more than moderate risk for having CV events at 7 years (p&amp;lt;0.001). Conclusion Perhaps, this is the first study which has compared artificial intelligence based novel risk prediction model with the three most commonly applied models, in the young Indian patients. We found that a cohort of young Indian patients presenting with ACS, when studied retrospectively, was identified as “high risk” most likely by AICVD risk prediction model rather than the traditional counterparts. The WHO/ISH risk prediction charts and FRS were the poorest predictors. Performance of QRISK3 score also remained less than satisfactory. These findings suggested that AICVD risk prediction model is a promising tool to assess for CV risk in Indian population. Funding Acknowledgement Type of funding sources: None. Predictability of risk prediction models

Digital Innovation Risk Management Model of Discrete Manufacturing Enterprise Based on Big Data Analysis

At present, most risk management work mainly relies on manpower, and manpower relies on the professional knowledge of relevant skilled workers to discover hidden safety risks in production activities. This article combines relevant big data theories and 4V characteristics to analyze and investigate safety production and big data, study data structure, data source and data type. Using 5W1H scientific big data and applications, this analysis method analyzes the theoretical basis, applications and beneficiaries of big data related to safety production, some of which are application links and important theoretical issues. Secondly, it studies the security risk management model based on big data, proposes a risk management model based on big data, the technical basis of big data and the idea of a three-dimensional model, and applies the systematic space method, which is reflected in three aspects of risk management. In the end, a risk identification model based on big data, a risk assessment classification model, and a risk early warning and pre-control model are defined.