Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques (See5 and Rough Set) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies, upon the basis of a set of financial ratios. We also compare these methods with three classical and well-known techniques: one of them belonging to the field of Machine Learning (Multilayer Perceptron) and two statistical ones (Linear Discriminant Analysis and Logistic Regression). Results indicate a higher performance of the machine learning techniques. Furthermore, See5 and Rough Set provide easily understandable and interpretable decision models, which shows that these methods can be a useful tool to evaluate insolvency of insurance firms.El pronóstico sobre la insolvencia de las compañías de seguro ha surgido como un problema importante en el ámbito de investigación financiera. La mayoría de los métodos aplicados en el pasado para abordar este problema, son técnicas estadísticas tradicionales que usan los ratios financieros como variables explicativas. Aunque, estas variables a menudo no satisfacen las suposiciones estadísticas, lo que complica la aplicación de dichos métodos. En este artículo, se lleva a cabo un estudio comparativo sobre la actuación de dos técnicas de aprendizaje automático no paramétrico (See5 y Rough Set). Hemos aplicado ambos métodos al problema del pronóstico sobre la insolvencia de compañías españolas de seguros no de vida, sobre la base de un conjunto de ratios financieros. Además, hemos comparado estos métodos con tres técnicas clásicas y muy conocidas: una de ellas perteneciente al área del Aprendizaje Automático (Perceptrón Multicapa), y dos estadísticos (Análisis Discriminante Lineal y Regresión Logística). Los resultados indican un desempeño más elevado en las técnicas de aprendizaje automático. Es más, See5 y Rough Set aportan unos modelos de decisión fácilmente entendibles, e interpretables, lo que demuestra que estos métodos pueden ser útiles para evaluar la insolvencia de empresas de seguros

Minimal Decision Rules Based on the A Priori Algorithm

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensi- ble (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our ap- proach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values

Pre-earthquake fuzzy logic-based rapid hazard assessment of reinforced concrete buildings

The main purpose of this paper is to present a rapid building assessment fuzzy logic (FL) modelling for risk assessment based on expert construction engineering verbal informatics. Before an earthquake, a set of input expert assessment variables are transformed into five types of hazard categorization as "no damage", "slight damage", "moderate damage", "severe damage", and "collapse". Main variables are reported by expert engineers based on visual inspection of structural components in addition to the building location's peak ground velocity (PGV) micro zonation numerical value, soil type and building's material information. Each input variable and output hazard class is fuzzified. A valid set of fuzzy rule base components is written based on input variables, each of which has an appropriate output hazard class. The fuzzy hazard assessment model has input and output variables in terms of fuzzy sets. Thus, the overall model output is in the form of a fuzzy set and then defuzzified to find the percentage of each hazard class for a single building. The application of this fuzzy logic model is presented for twenty existing reinforced concrete buildings, and the final hazard categories of these buildings are presented with interpretations and recommendations.Istanbul Medipol Universit

SoResilere—A Social Resilience Index Applied to Portuguese Flood Disaster-Affected Municipalities

Decades of academic discussion on social resilience have led to the development of indicators, indexes, and different approaches to assessing it at national and local levels. The need to show real-world applications of such assessments is evident since resilience became a political and disaster risk reduction governance component. This article gives a full description of the methodology used to develop SoResilere, a new social resilience index applied to flood disaster-affected Portuguese municipalities. Study cases were selected according to historical databases, academic sources and governmental entities. Statistical methods for data dimension reduction, such as Factor Analysis (through Principal Component Analysis), were applied to the quantitative data and Optimal Scaling to the categorical data. SoResilere results were analyzed. Since SoResilere is a new tool, component weighting was applied to compare results with no weighting, although it did not affect the SoResilere status in 55.5% of the study cases. There is a tendency to look at the improvement of SoResilere results with component weighting due mainly to the quantitative subindex. There is no evidence of the benefits of component weighting, as no logical association or spatial pattern was found to support SoResilere status improvement in 22.22% of the study cases.info:eu-repo/semantics/publishedVersio

Supporting metropolitan Venice coastline climate adaptation. A multi-vulnerability and exposure assessment approach

Urban planning for adaptation to climate change privileges the construction of cognitive frameworks developed through the use of new spatial technologies and open-source databases. The significant and most highly innovative aspect concerns how resilience to CC under conditions of vulnerability and risk is defined, monitored and assessed. Based on these premises, this paper aims to explore a new methodology of climate vulnerability, exposure and risk analysis through multicriteria assessment techniques by activating a case study in the coastal municipality of Jesolo (Italy). Taking into consideration three main weather-climate impacts (Urban Flooding, Coastal Flooding and Urban Heat Island) the methodology searches for the best geo-referenced data that can best describe the recognizing impact of the cumulative impact condition through testing a GIS-based multi-attribute exploratory procedure. Intersectoral and multilevel vulnerability conditions at different spatial scales are configured. The analysis methodology continues using open source data (from Open Street Map) to construct local exposure information layers. Exposure combined with spatial vulnerability conditions allows the generation of multi-hazard mapping. Experimentation with multi-hazard climate-oriented spatial assessment can guide planning and public decision-making in new policy domains and target mitigation and adaptation actions in land planning, management and regulation practices. Finally, the proposed methodology can activate stakeholder engagement processes within municipalities to discuss the actual perceived risk and begin a collaborative journey with citizens to identify best practices and solutions to adopt in the areas indicated by the risk mapping

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

The APC was funded by their authors.Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information.This research was funded by the University of Granada, grant number PP2016-PIP0

Internet-based solutions to support distributed manufacturing

With the globalisation and constant changes in the marketplace, enterprises are adapting themselves to face new challenges. Therefore, strategic corporate alliances to share knowledge, expertise and resources represent an advantage in an increasing competitive world. This has led the integration of companies, customers, suppliers and partners using networked environments. This thesis presents three novel solutions in the tooling area, developed for Seco tools Ltd, UK. These approaches implement a proposed distributed computing architecture using Internet technologies to assist geographically dispersed tooling engineers in process planning tasks. The systems are summarised as follows. TTS is a Web-based system to support engineers and technical staff in the task of providing technical advice to clients. Seco sales engineers access the system from remote machining sites and submit/retrieve/update the required tooling data located in databases at the company headquarters. The communication platform used for this system provides an effective mechanism to share information nationwide. This system implements efficient methods, such as data relaxation techniques, confidence score and importance levels of attributes, to help the user in finding the closest solutions when specific requirements are not fully matched In the database. Cluster-F has been developed to assist engineers and clients in the assessment of cutting parameters for the tooling process. In this approach the Internet acts as a vehicle to transport the data between users and the database. Cluster-F is a KD approach that makes use of clustering and fuzzy set techniques. The novel proposal In this system is the implementation of fuzzy set concepts to obtain the proximity matrix that will lead the classification of the data. Then hierarchical clustering methods are applied on these data to link the closest objects. A general KD methodology applying rough set concepts Is proposed In this research. This covers aspects of data redundancy, Identification of relevant attributes, detection of data inconsistency, and generation of knowledge rules. R-sets, the third proposed solution, has been developed using this KD methodology. This system evaluates the variables of the tooling database to analyse known and unknown relationships in the data generated after the execution of technical trials. The aim is to discover cause-effect patterns from selected attributes contained In the database. A fourth system was also developed. It is called DBManager and was conceived to administrate the systems users accounts, sales engineers’ accounts and tool trial monitoring process of the data. This supports the implementation of the proposed distributed architecture and the maintenance of the users' accounts for the access restrictions to the system running under this architecture

A spatial decision support system for multifunctional landscape assessment: a transformative resilience perspective for vulnerable inland areas

The concept of transformative resilience has emerged from the recent literature and represents a way to interpret the potential opportunities for change in vulnerable territories, where a socioeconomic change is required. This article extends the perspective of transformative resilience to an assessment of the landscape multifunctionality of inland areas, exploring the potential of identifying a network of synergies among the different municipalities that is able to trigger a process of territorial resilience. A spatial decision support system (SDSS) for multifunctionality landscape assessment aims to help local actors understand local resources and multifunctional values of the Partenio Regional Park (PRP) and surrounding municipalities, in the South of Italy, stimulating their cooperation in the management of environmental and cultural sites and the codesign of new strategies of enhancement. The elaboration of spatial indicators according to Landscape Services classification and the interaction between the “Analytic Network Process” (ANP) method, spatial weighted overly and geographic information system (GIS) support the identification of a preferable scenario able to activate a transformative resilience strategy in selected vulnerable inland areas, which can be scaled up in other similar contexts