scholarly journals Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guangju Li
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Financial System ◽  
Warning System ◽  
Early Warning Systems ◽  
Group Method ◽  
Financial Risks ◽  
Semantic Hierarchy ◽  
Risk Detection ◽  
Increased Risk

Banks, financial, and credit institutions encountering the weakening financial system and increased risk factors cause high inflation and great losses for an economy. Detecting financial risks in advance could help financial institutions avoid losses, and the financial system could be eventually affected less. Early warning systems for banks could be helpful to identify financial risks and take measures to deal with hazardous situations. Various approaches have already been put forward. However, inaccuracy issues in risk detection are one of the main issues. Combining semantic hierarchy with the GMDH neural network to predict financial risks is proposed. A semantic hierarchy approach based on converting risk-related values and picking influential variables could be practical in risk detection. Besides, the GMDH algorithm utilizing neural networks based on available data has the capability of predicting possible risks that could occur in the future. The outcomes of the proposed method when compared to non-data mining methods suggest that it improves accuracy by almost 20%.

Disaster Early Warning Systems: The Potential Role and Limitations of Emerging Text and Data Messaging Mitigation Capabilities

2019 ◽  
Vol 13 (4) ◽  
pp. 709-712 ◽  
Author(s):  
Krzysztof Goniewicz ◽  
Frederick M. Burkle
Keyword(s):  
Mobile Networks ◽  
Early Warning ◽  
Text Messaging ◽  
Text Message ◽  
Warning System ◽  
Specific Area ◽  
Early Warning Systems ◽  
Text Messages ◽  
Increased Risk ◽  
Disaster Communications

ABSTRACTObjectiveThe increased risk of mass accidents or major catastrophes taking place necessitates the organization of remedial measures to help protect against these unusual events and adequate preparation in order to minimize their effects. One such initiative is the early notification of residents within a specific area about the risk of a particular calamity. Nowadays, the prevalence of mobile devices enables the installation of various mobile applications allowing for the communication and receiving of information about potential dangers. In many countries there are variously developed systems of notification in place based specifically on text messages.MethodsCurrently, new laws introduced in Poland establish that it is the obligation of operators of mobile networks to send text messages to all customers of these networks who are within the area where there is a serious risk of a catastrophe. Such messages are in the form of a short alert, to be sent only in extraordinary situations when there is an immediate threat to health or life. The alert is intended to help in the avoidance of danger or to mitigate its impact.ResultsThis article presents the potential implementation of the early warning system based on text message alerts in Poland, and in particular focuses on decreasing the risks associated with natural disasters.ConclusionsWhile early text messaging is essential to disaster communications and mitigation, the article further states that means must be found to ensure equal access to the most vulnerable populations and all those, vulnerable and not, who do not have immediate access to text messaging systems. (Disaster Med Public Health Preparedness. 2019;13:709–712)

scholarly journals Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Michael Brown ◽  
R. Matthew DeMonbrun ◽  
Stephanie Teasley
Keyword(s):  
Academic Success ◽  
Early Warning ◽  
Structural Complexity ◽  
Warning System ◽  
Early Warning Systems ◽  
Course Enrollment ◽  
Academic Difficulty ◽  
Post Secondary ◽  
Increased Risk ◽  
Course Difficulty

In this study, we develop and test four measures for conceptualizing the potential impact of co-enrollment in different courses on students’ changing risk for academic difficulty and recovery from academic difficulty in a focal course. We offer four predictors, two related to instructional complexity and two related to structural complexity (the organization of the curriculum) that highlight different trends in student experience of the focal course. Course difficulty, discipline of major, time in semester, and simultaneous difficulty across courses were all significantly related to entering a moderate and high-risk classification in the early warning system (EWS). Course difficulty, discipline of major, and time in semester were related to exiting academic difficulty classifications. We observe a snowball effect, whereby students who are experiencing difficulty in the focal course are at increased risk of experiencing difficulty in their other courses. Our findings suggest that different metrics may be needed to identify risk for academic difficulty and recovery from academic difficulty. Our results demonstrate what a more holistic assessment of academic functioning might look like in early warning systems and course recommender systems, and suggest that academic planners consider the relationship between course co-enrollment and student academic success.

scholarly journals Brief communication "Landslide Early Warning System: toolbox and general concepts"

2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
End Users ◽  
Flow Chart ◽  
Warning Systems ◽  
Limited Experience ◽  
Practical Guidelines ◽  
Landslide Early Warning

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

scholarly journals Communication architecture of an early warning system

2010 ◽  
Vol 10 (11) ◽  
pp. 2215-2228 ◽  
Author(s):  
M. Angermann ◽  
M. Guenther ◽  
K. Wendlandt
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Satellite Communication ◽  
Warning System ◽  
Early Warning Systems ◽  
Lessons Learned ◽  
Warning Systems ◽  
Communication Architecture ◽  
Design And Implementation ◽  
Communication Links

Abstract. This article discusses aspects of communication architecture for early warning systems (EWS) in general and gives details of the specific communication architecture of an early warning system against tsunamis. While its sensors are the "eyes and ears" of a warning system and enable the system to sense physical effects, its communication links and terminals are its "nerves and mouth" which transport measurements and estimates within the system and eventually warnings towards the affected population. Designing the communication architecture of an EWS against tsunamis is particularly challenging. Its sensors are typically very heterogeneous and spread several thousand kilometers apart. They are often located in remote areas and belong to different organizations. Similarly, the geographic spread of the potentially affected population is wide. Moreover, a failure to deliver a warning has fatal consequences. Yet, the communication infrastructure is likely to be affected by the disaster itself. Based on an analysis of the criticality, vulnerability and availability of communication means, we describe the design and implementation of a communication system that employs both terrestrial and satellite communication links. We believe that many of the issues we encountered during our work in the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) on the design and implementation communication architecture are also relevant for other types of warning systems. With this article, we intend to share our insights and lessons learned.

scholarly journals On building early-warning systems for preventing the deterioration of financial institutions’ performance

2019 ◽  
Vol 1 (1) ◽  
pp. 194-202
Author(s):  
Adrian Costea
Keyword(s):  
Financial Institutions ◽  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Capital Adequacy ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Performance Dimensions ◽  
Som Model ◽  
Self Organizing

Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would accurately forecast the NFIs future performance (whether they would stay or be eliminated from the NFI’s Special Register three quarters into the future). The results are promising: the model is able to correctly predict NFIs’ performance movements. Finally, we compared the results of our SOM-based model with those obtained by applying a multivariate logit-based model. The SOM model performed worse in discriminating the NFIs’ performance: the performance classes were not clearly defined and the model lacked the interpretability of the results. In the contrary, the multivariate logit coefficients have nice interpretability and an individual default probability estimate is obtained for each new observation. However, we can benefit from the results of both techniques: the visualization capabilities of the SOM model and the interpretability of multivariate logit-based model.

Toward a typology of weak-signal early alert systems: functional early warning systems in the post-COVID age

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jan Černý ◽  
Martin Potančok ◽  
Elias Castro Hernandez
Keyword(s):  
Peer Review ◽  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Weak Signal ◽  
Warning Systems ◽  
Weak Signals ◽  
Content Type ◽  
Human In The Loop ◽  
Early Alert Systems

PurposeThe study aims to expand on the concept of an early warning system (EWS) by introducing weak-signal detection, human-in-the-loop (HIL) verification and response tuning as integral parts of an EWS's design.Design/methodology/approachThe authors bibliographically highlight the evolution of EWS over the last 30+ years, discuss instances of EWSs in various types of organizations and industries and highlight limitations of current systems.FindingsProposed system to be used in the transforming of weak signals to early warnings and associated weak/strong responses.Originality/valueThe authors contribute to existing literature by presenting (1) novel approaches to dealing with some of the well-known issues associated with contemporary EWS and (2) an event-agnostic heuristic for dealing with weak signals.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0513.

Financial Early Warning System for Risk Detection and Prevention from Financial Crisis

Data Mining ◽  
2013 ◽  
pp. 1559-1590
Author(s):  
Nermin Ozgulbas ◽  
Ali Serhan Koyuncugil
Keyword(s):  
Financial Crisis ◽  
Early Warning ◽  
Financial Distress ◽  
Business Performance ◽  
Global Financial Crisis ◽  
Stock Exchange ◽  
Warning System ◽  
Chi Square ◽  
Risk Detection ◽  
The Global Financial Crisis

Risk management has become a vital topic for all enterprises especially in financial crisis periods. All enterprises need systems to warn against risks, detect signs and prevent from financial distress. Before the global financial crisis that began 2008, small and medium-sized enterprises (SMEs) have already fought with important financial issues. The global financial crisis and the ensuring flight away from risk have affected SMEs more than larger enterprises When we consider these effects, besides the issues of poor business performance, insufficient information and insufficiencies of managers in finance education, it is clear that early warning systems (EWS) are vital for SMEs for detection risk and prevention from financial crisis. The aim of this study is to develop and present a financial EWS for risk detection via data mining. For this purpose, data of SMEs listed in Istanbul Stock Exchange (ISE) and Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm were used. By using EWS, we determined the risk profiles and risk signals for risk detection and road maps for risk prevention from financial crisis.

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