An Artificial Intelligence Framework to Contractor Financial Prequalification

Financial distress in the construction industry always causes major disruptions that usually result in a rippling effect on the economy. Avoiding such defaults is a top priority for employers to meet their demands. Artificial Intelligence (AI) models have provided increased accuracy in predicting financial distress compared to statistical, fuzzy and logistic regression models, and other classification models. The main objective of this work is to support project employers in pre-qualifying contractors by predicting the status of construction contractors during a bid analysis to disqualify contractors with a high probability of experiencing financial distress during the project duration. Eight financial indicators & six macroeconomic variables were used in the analysis. The selected variables were proven to be highly correlated with the output values as provided in the literature while maintaining variables with diverse effects on the output. This work employs multiple models including artificial neural networks (ANN), support vector machines (SVM), and logistic regression using different tools (Python & NeuralTools) based on collected financial statements and macroeconomic indicators. The results show that the ANN model developed using python achieved higher performance measures than SVM (radial basis function & linear kernel functions), logistic regression & ANN developed using NeuralTools. The results also show that adding macroeconomic variables to financial ratios as input variables significantly enhance the accuracy and F-1 score of the model. Accordingly, the developed model is effective in predicting financial distress for construction companies

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future develop-ment becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice.Purpose of the article: This study aims to predict the bankruptcy of companies in the engineer-ing and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engi-neering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regres-sion to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bank-ruptcy using six of these indicators. Almost all sampled industries are privatised, and most com-panies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct com-parative analyses of their own model with ours to reveal areas of model improvements.KEGA [001PU-4/2022]; Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic; Slovak Academy Sciences [1/0590/22]1/0590/22; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR, KEGA: 001PU-4/202

Un modelo global de predicción de quiebra con redes neuronales

El capítulo 3 está dedicado al proceso de obtención de las muestras, a las variables utilizadas y a los criterios tenidos en cuenta para la selección de las mismas. Por su parte, en el capítulo 4 se presentan los resultados del análisis empírico, dejando constancia de los modelos de predicción de la quiebra desarrollados y de la robustez de los mismos. Finalmente, el trabajo concluye con una discusión sobre los resultados alcanzados, con la exposición de las principales conclusiones obtenidas y con el detalle de la bibliografía consultada. Fecha de lectura de Tesis Doctoral: 29 de enero 2019.El presente trabajo trata de responder a la cuestión de investigación de si es posible mejorar la precisión de los modelos globales de predicción de quiebra existentes en la literatura previa. Para responder a esta cuestión se ha tenido en cuenta los excelentes resultados de clasificación que proporcionan los métodos computacionales tales como las redes neuronales artificiales, y se han construidos tanto modelos regionales para Asia, Europa y Norte América, como modelos globales. En concreto, se ha utilizado el denominado Perceptrón Multicapa y los resultados obtenidos han permitido constatar una mayor precisión de los métodos computacionales frente a las técnicas estadísticas tradicionales. La estructura del presente trabajo de investigación es la siguiente. En el capítulo 1 se lleva a cabo un análisis de la literatura previa sobre predicción de quiebra. De este análisis se han obtenido conclusiones sobre los métodos aplicados y su perfeccionamiento, sobre las variables empleadas, y sobre la evolución de los resultados obtenidos por los distintos modelos. Además, y atendiendo al enfoque de estudio adoptado, se ha analizado la literatura diferenciando entre modelos globales y modelos regionales. Este primer capítulo concluye aportando una clasificación de los estudios previos en la que se pone de manifiesto los principales argumentos utilizados y la brecha existente acerca de la superioridad de los modelos globales frente a los modelos regionales. El capítulo 2 aborda los fundamentos del método de naturaleza computacional utilizado en el presente trabajo. Además, se presentan la técnica de validación cruzada y los principales criterios de selección de modelos, que han sido adicionalmente utilizados para el contraste de los resultados

A Hybrid intelligent system for diagnosing and solving financial problems

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnologico. Programa de Pós-Graduação em Engenharia de Produção2012-10-16T09:55:39

The effect of corporate entrepreneurship, innovation and strategic renewal on business performance, business failure or organizational decline moderated by industry and firm size

The largest and most successful companies in the world have not always been that way. The challenges they have faced in order to become what they are today may have been countless. In contrast, while many companies have achieved significant levels of growth, they have not all been able to maintain their success in the long term, even to the point of starting to fail. This Thesis provides a conceptual framework that tends to explain the effect of CE that encompass I and SR on BP in Colombian companies, so the lack of CE, I and SR moderated by size of and sector/industry, might lead the Colombian companies to BF or OD. The research was developed using the information provided by regulatory entities in Colombia. The information gathered included both financial and managerial information submitted to the Colombian Superintendency of Corporations by companies that have entered into the restructuring process during the last ten years, as supervised by the Colombian Superintendency of Corporations and based on the Colombian Law on Restructuring during the last decade. Out of the 131 companies included in this research, 53 (40 %) belonged to the manufacturing sector and 78 (60%) belonged to the service sector. Regarding company size, 60 (45,80%) were small, 27 (20,61%) were medium and 44 (33,59%) were big. The information gathered included both financial and managerial information submitted to the Colombian Superintendency of Corporations by the companies that have entered into the restructuring process during the last ten years, as supervised by the Colombian Superintendency of Corporations and based on the Colombian Law on Restructuring during the last decade. For this purpose, this study used the data base information provided by the Colombian Superintendency of Corporations. This research excluded information obtained from agencies other than the Colombian Superintendence of Corporations. This research did begin with a pilot study developed with a case study that took into consideration a survey and interviews with representatives of companies that have entered into a restructuring process. The Superintendency’s website provided the list of the companies in the restructuring process. This information includes the approved financial services companies and detailed information on the legal representation of those companies. A two-stage sampling process was followed in identifying the subjects for the sampling. In stage one, this research used a probability sampling that is based on stratified random sampling. In order to determine the size of the sampling, the following formula was taken into consideration: n=(N*Z2a*p*q)/((e2*(N-1)+(Z2a*p*q)), where n is the size of the sample, N the size of the universe, Z the level of confidence, e the margin of error p and q the probability of occurrence. A level of confidence of 80% and error margin of 5% was determined by the researcher. In stage 2, the selection of participants from the organizations represented in the now-established sampling frame will be selected in terms of the criteria that the selected participant be registered in the restructuration process data base provided by the Superintendency of Corporations. A single contact person will be identified within each organization, with this person furnishing the contact information of employees who, meeting qualifying selection criteria, will be selected to be the research participants. The identified participants will then be called on their telephones, and a follow up e-mail will detail the research objectives). The participants will be considered “front facing” – in other words, they are part of, or were part of the decision-making process in their companies. For the quantitative part, the paths of the hypotheses among the main latent constructs will be then assessed using a structural equation modeling (SEM) procedure. For the quantitative part the research will use Atlas TI. As with standard regression, the basic measure of association between variables is covariance, and the dynamics of actually fitting SEM models involve covariance structure modeling (Iacobucci, 2009). The model shows that CE has a direct influence on BP. It is important to highlight that CE is influenced only by I. SR does not have a direct impact in CE. It could be inferred that the Colombian companies do not perceive that CE is a crucial part of the strategic planning. Furthermore, CE does not impact OD. It can be inferred that the more CE activities the Colombian companies put in place the better the outcomes from the business and financial performance. Thus, the Colombian companies that are facing OD could have avoided it by implementing CE activities.Tesi

Investigating the neural substrates of gambling disorder using multiple neuromodulation and neuroimaging approaches

Introduction : Le trouble du jeu de hasard et d'argent (GD) est caractérisé par un comportement de jeu inadapté qui interfère avec les activités personnelles ou professionnelles. Ce trouble psychiatrique est difficile à traiter avec les thérapies actuelles et les rechutes sont fréquentes. Les symptômes dépressifs et cognitifs (e.g., l'impulsivité), ainsi que le "craving" (désir intense de jouer) sont des facteurs prédictifs de rechutes. Une meilleure compréhension des substrats neuronaux et leurs significations cliniques pourraient mener au développement de nouveaux traitements. La stimulation transcrânienne à courant direct (tDCS) pourrait être l'un de ceux-ci car elle permet de cibler des circuits neuronaux spécifiques. De plus, la tDCS ciblant le cortex dorsolatéral préfrontal (DLPFC) pourrait améliorer les symptômes dépressifs et cognitifs et réduire le craving. Cependant, les effets précis de la tDCS sur la fonction cérébrale, ainsi que leurs significations cliniques, demeurent à être élucidés. Par ailleurs, étant donné que les patients avec GD présentent souvent des différences morphométriques par rapport aux individus en santé, il est possible de faire l'hypothèse que la morphométrie cérébrale influence les effets de la tDCS. Objectifs : Ce travail avait trois objectifs principaux. Le premier objectif était d'explorer s'il y avait des associations entre les substrats neuronaux et les symptômes cliniques et cognitifs. Le deuxième objectif était d'examiner les effets de la tDCS sur la fonction cérébrale. Le troisième objectif était d'explorer si la morphométrie du site de stimulation (DLPFC) pouvait influencer les effets de la tDCS sur les substrats neuronaux. Méthode : Nous avons réalisé quatre études différentes. Dans la première étude, nous avons mesuré la morphométrie cérébrale en utilisant l'imagerie par résonance magnétique (IRM) structurelle. Nous avons mesuré les corrélations entre la morphométrie et les symptômes cliniques (dépression, sévérité et durée du GD) et cognitifs (impulsivité). De plus, nous avons comparé la morphométrie des patients à celui d'une base de données normative (individus en santé) en contrôlant pour plusieurs facteurs comme l'âge. Dans la deuxième étude, nous avons mesuré la fonction cérébrale (connectivité fonctionnelle) des patients avec l'IRM fonctionnelle. Nous avons examiné s'il y avait des liens entre la connectivité fonctionnelle et les symptômes cognitifs (impulsivité et prise de risque) et cliniques (sévérité et durée du GD). Dans la troisième étude, nous avons étudié les effets de la tDCS sur la connectivité fonctionnelle et si la morphométrie du DLPFC pouvait influencer ces effets. Dernièrement, dans la quatrième étude, nous avons examiné si la morphométrie du DLPFC pouvait influencer les effets de la tDCS sur la neurochimie (avec la spectroscopie par résonance magnétique). Résultats : Nous avons démontré deux corrélations positives entre la superficie du cortex occipital et les symptômes dépressifs (étude I). Nous avons également mis en évidence une corrélation positive entre la connectivité fonctionnelle d'un réseau occipital et l'impulsivité (étude II). De plus, il y avait une corrélation positive entre la connectivité fonctionnelle de ce réseau et la sévérité du GD. Par ailleurs, il y avait des corrélations positives entre la connectivité fonctionnelle de l'opercule frontal droit et la prise de risque (étude II). En outre, la connectivité fonctionnelle d'un réseau cérébelleux était corrélée avec les symptômes dépressifs (étude II). Les patients avaient aussi plusieurs différences morphométriques par rapport aux individus en santé (cortex occipital, préfrontal, etc.). Nous avons démontré également que la tDCS appliquée sur le DLPFC a augmenté la connectivité fonctionnelle d'un réseau fronto-pariétal (étude III). Finalement, cette thèse a montré que la morphométrie du DLPFC influence les augmentations induites par la tDCS sur la connectivité fonctionnelle du réseau fronto-pariétal (étude III) et le niveau de GABA frontal (étude IV). Conclusions : Cette thèse démontre une importance clinique potentielle pour les régions occipitales, frontales et cérébelleuses, particulièrement pour les patients ayant des symptômes dépressifs ou cognitifs. De plus, elle montre que la tDCS peut renforcer le fonctionnement d'un réseau fronto-pariétal connu pour son rôle dans les fonctions exécutives. Il reste à déterminer si un plus grand nombre de sessions pourrait apporter des bénéfices cliniques additionnels afin d'aider les patients à résister le jeu. Finalement, les résultats de cette thèse suggèrent que la morphométrie des régions sous les électrodes pourrait aider à identifier les meilleurs candidats pour la tDCS et pourrait être considéré pour la sélection des cibles de stimulation.Introduction: Gambling disorder (GD) is characterised by maladaptive gambling behaviour that interferes with personal or professional activities. This psychiatric disorder is difficult to treat with currently available treatments and relapse rates are high. Several factors can predict relapse, including depressive and cognitive (e.g., impulsivity, risk taking) symptoms, in addition to craving (strong desire to gamble). A better understanding of neural substrates and their clinical significance could help develop new treatments. Transcranial direct current stimulation (tDCS) might be one of these since it can target specific neural circuits. In addition, tDCS targeting the dorsolateral prefrontal cortex (DLPFC) could improve depressive and cognitive symptoms as well as reduce craving. However, the precise effects of tDCS on brain function, as well as their clinical significance, remain to be elucidated. Furthermore, considering that patients with GD often display morphometric differences as compared to healthy individuals, it may be worth investigating whether brain morphometry influences the effects of tDCS. Objectives: This work had three main objectives. The first objective was to explore whether there were associations between neural substrates and clinical and cognitive symptoms. The second objective was to examine the effects of tDCS on brain function. The third objective was to explore whether morphometry of the stimulation site (DLPFC) influenced the effects of tDCS on neural substrates. Methods: We carried out four different studies. In the first study, we investigated brain morphometry using structural magnetic resonance imaging (MRI). We tested for correlations between morphometry and clinical symptoms (depression, GD severity, GD duration) and cognitive symptoms (impulsivity). In addition, we compared the morphometry of patients with GD to that of a normative database (healthy individuals) while controlling for several factors such as age. In a second study, we assessed brain function (functional connectivity) in patients with functional MRI (fMRI). We examined whether there were associations between brain function and cognitive symptoms (impulsivity and risk taking) as well as clinical symptoms (GD severity and duration). In the third study, we examined tDCS-induced effects on brain function and whether morphometry of the DLPFC influenced these effects. Lastly, in the fourth study, we examined whether DLPFC morphometry influenced tDCS-induced effects on neurochemistry (using magnetic resonance spectroscopy imaging). Results: Firstly, we found two positive correlations between surface area of the occipital cortex and depressive symptoms (study I). We also showed a positive correlation between functional connectivity of an occipital network and impulsivity (study II). In addition, there was a positive correlation between functional connectivity of this network and GD severity (study II). In addition, there were positive correlations between functional connectivity of the right frontal operculum and risk-taking (study II). Also, functional connectivity of a cerebellar network was positively correlated with depressive symptoms (study II). Moreover, patients with GD had several morphometric differences as compared to healthy individuals (occipital and prefrontal cortices, etc.). Furthermore, we observed that tDCS over the DLPFC increased functional connectivity of a fronto-parietal circuit during stimulation (study III). Lastly, this thesis indicated that DLPFC morphometry influenced tDCS-induced elevations on fronto-parietal functional connectivity (study III) and frontal GABA levels (study IV). Conclusions: This thesis suggests the potential clinical relevance of occipital, frontal, and cerebellar regions, particularly for those with depressive and cognitive symptoms. It also indicates that tDCS can strengthen the functioning of a fronto-parietal network known to be implicated in executive functions. It remains to be seen whether a greater number of tDCS sessions could lead to clinical benefits to help patients resist gambling. Finally, the results of this thesis suggest that morphometry of the regions under the electrodes might help predict better candidates for tDCS and could be considered to select stimulation targets