Interpretable Binary and Multiclass Prediction Models for Insolvencies and Credit Ratings

Insolvenzprognosen und Ratings sind wichtige Aufgaben der Finanzbranche und dienen der Kreditwürdigkeitsprüfung von Unternehmen. Eine Möglichkeit dieses Aufgabenfeld anzugehen, ist maschinelles Lernen. Dabei werden Vorhersagemodelle aufgrund von Beispieldaten aufgestellt. Methoden aus diesem Bereich sind aufgrund Ihrer Automatisierbarkeit vorteilhaft. Dies macht menschliche Expertise in den meisten Fällen überflüssig und bietet dadurch einen höheren Grad an Objektivität. Allerdings sind auch diese Ansätze nicht perfekt und können deshalb menschliche Expertise nicht gänzlich ersetzen. Sie bieten sich aber als Entscheidungshilfen an und können als solche von Experten genutzt werden, weshalb interpretierbare Modelle wünschenswert sind. Leider bieten nur wenige Lernalgorithmen interpretierbare Modelle. Darüber hinaus sind einige Aufgaben wie z.B. Rating häufig Mehrklassenprobleme. Mehrklassenklassifikationen werden häufig durch Meta-Algorithmen erreicht, welche mehrere binäre Algorithmen trainieren. Die meisten der üblicherweise verwendeten Meta-Algorithmen eliminieren jedoch eine gegebenenfalls vorhandene Interpretierbarkeit. In dieser Dissertation untersuchen wir die Vorhersagegenauigkeit von interpretierbaren Modellen im Vergleich zu nicht interpretierbaren Modellen für Insolvenzprognosen und Ratings. Wir verwenden disjunktive Normalformen und Entscheidungsbäume mit Schwellwerten von Finanzkennzahlen als interpretierbare Modelle. Als nicht interpretierbare Modelle werden Random Forests, künstliche Neuronale Netze und Support Vector Machines verwendet. Darüber hinaus haben wir einen eigenen Lernalgorithmus Thresholder entwickelt, welcher disjunktive Normalformen und interpretierbare Mehrklassenmodelle generiert. Für die Aufgabe der Insolvenzprognose zeigen wir, dass interpretierbare Modelle den nicht interpretierbaren Modellen nicht unterlegen sind. Dazu wird in einer ersten Fallstudie eine in der Praxis verwendete Datenbank mit Jahresabschlüssen von 5152 Unternehmen verwendet, um die Vorhersagegenauigkeit aller oben genannter Modelle zu messen. In einer zweiten Fallstudie zur Vorhersage von Ratings demonstrieren wir, dass interpretierbare Modelle den nicht interpretierbaren Modellen sogar überlegen sind. Die Vorhersagegenauigkeit aller Modelle wird anhand von drei in der Praxis verwendeten Datensätzen bestimmt, welche jeweils drei Ratingklassen aufweisen. In den Fallstudien vergleichen wir verschiedene interpretierbare Ansätze bezüglich deren Modellgrößen und der Form der Interpretierbarkeit. Wir präsentieren exemplarische Modelle, welche auf den entsprechenden Datensätzen basieren und bieten dafür Interpretationsansätze an. Unsere Ergebnisse zeigen, dass interpretierbare, schwellwertbasierte Modelle den Klassifikationsproblemen in der Finanzbranche angemessen sind. In diesem Bereich sind sie komplexeren Modellen, wie z.B. den Support Vector Machines, nicht unterlegen. Unser Algorithmus Thresholder erzeugt die kleinsten Modelle während seine Vorhersagegenauigkeit vergleichbar mit den anderen interpretierbaren Modellen bleibt. In unserer Fallstudie zu Rating liefern die interpretierbaren Modelle deutlich bessere Ergebnisse als bei der zur Insolvenzprognose (s. o.). Eine mögliche Erklärung dieser Ergebnisse bietet die Tatsache, dass Ratings im Gegensatz zu Insolvenzen menschengemacht sind. Das bedeutet, dass Ratings auf Entscheidungen von Menschen beruhen, welche in interpretierbaren Regeln, z.B. logischen Verknüpfungen von Schwellwerten, denken. Daher gehen wir davon aus, dass interpretierbare Modelle zu den Problemstellungen passen und diese interpretierbaren Regeln erkennen und abbilden

Interpretable Credit Application Predictions With Counterfactual Explanations

International audienceWe predict credit applications with off-the-shelf, interchangeable black-box clas-sifiers and we explain single predictions with counterfactual explanations. Coun-terfactual explanations expose the minimal changes required on the input data to obtain a different result e.g., approved vs rejected application. Despite their effectiveness , counterfactuals are mainly designed for changing an undesired outcome of a prediction i.e. loan rejected. Counterfactuals, however, can be difficult to interpret , especially when a high number of features are involved in the explanation. Our contribution is twofold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

The most important part in software engineering is a software defect prediction. Software defect prediction is defined as a software prediction process from errors, failures, and system errors. Machine learning methods are used by researchers to predict software defects including estimation, association, classification, clustering, and datasets analysis. Datasets of NASA Metrics Data Program (NASA MDP) is one of the metric software that researchers use to predict software defects. NASA MDP datasets contain unbalanced classes and high dimensional data, so they will affect the classification evaluation results to be low. In this research, data with unbalanced classes will be solved by the AdaCost method and high dimensional data will be handled with the Average Weight Information Gain (AWEIG) method, while the classification method that will be used is the Naïve Bayes algorithm. The proposed method is named AWEIG + AdaCost Bayesian. In this experiment, the AWEIG + AdaCost Bayesian algorithm is compared to the Naïve Bayesian algorithm. The results showed the mean of Area Under the Curve (AUC) algorithm AWEIG + AdaCost Bayesian yields better than just a Naïve Bayes algorithm with respectively mean of AUC values are 0.752 and 0.696

Calculando el riesgo de insolvencia, de los métodos tradicionales a las redes neuronales artificiales. Una revisión de literatura

In the financial management of any organization, the calculation of the risk of insolvency has become an important parameter, seeking to anticipate the eventuality of having an economic problem and generating insolvency. The objective of this work is to compare the classical methodologies and the artificial neural networks applied to calculate the risk of insolvency, looking for the main characteristics within the applications carried out by different authors over time. In this way, the main variables that can show that the application of the neural network methodology facilitates the calculation of the risk of insolvency are observed. Through the bibliographic review, between the years 1992-2021, with the use of the analytical-synthetic method, it can be seen that the exposed model is considered efficient according to its results, with adjustments that, in most of the exposed cases, exceed 80% efficiency. The results found allowed us to conclude that the basic structure of a neural network is given by three layers: an input layer, an output layer and a hidden layer. However, the number of nodes varies in each of the applications carried out by the different authors, since they represent the variables, in this case the most relevant financial indicators according to the proposed application. Finally, it was possible to show which are the most used financial indicators in the different neural network applications.En la administración financiera de toda organización el cálculo del riesgo de insolvencia se ha convertido en un parámetro importante, buscando anticiparse a la eventualidad de llegar a tener un problema económico y generar insolvencia. El objetivo de este trabajo es determinar si en el cálculo del riesgo de insolvencia, el uso de redes neuronales artificiales genera mejores resultados que las metodologías tradicionales, buscando las principales características dentro de las aplicaciones realizadas por distintos autores a través del tiempo. De esta manera se observan las principales variables que pueden evidenciar que la aplicación de la metodología de redes neuronales facilita el cálculo del riesgo de insolvencia.  A través de la revisión bibliográfica, en el período 1992-2021, con el uso del método analítico-sintético se puede evidenciar que el modelo expuesto es considerado como eficiente según sus resultados, con ajustes que, en la mayoría de los casos expuestos, superan el 80% de eficacia. Los resultados encontrados permitieron concluir que la estructura básica de una red neuronal viene dada por tres capas: una de entrada, una de salida y una oculta. Sin embargo, el número de nodos es el que varía en cada una de las aplicaciones realizadas por los distintos autores, dado que los mismos representan a las variables, en este caso indicadores financieros más relevantes según la aplicación planteada. Finalmente se logró evidenciar cuáles son los indicadores financieros más usados en las distintas aplicaciones de redes neuronales. Todo indica que las redes neuronales generan resultados más efectivos que los métodos tradicionales.&nbsp

Insolvency statutory rules and contractual freedom: a study on the limits of corporate insolvency law in the Anglo/American tradition

Back in 1986 Thomas H. Jackson published a ground breaking work for insolvency literature: The Logic and Limits of Bankruptcy Law. Since then, much ink has been spilled in the investigation of the foundations of insolvency law. Many scholars have argued for the implementation of rescue- or debtor-oriented practices, in order to give a “second chance” to the honest but unfortunate debtor, or to save a distressed but viable business. Others have advocated for maximising the return to creditors to protect the rights bargained for by the parties in solvent times. In other words, the debate has focused on the “logic” of this area of law and it has been primarily either principle or purpose-oriented. This study, on the other hand, focuses on the other pillar of Jackson’s literary work: the “limits” of insolvency law. Insolvency remedies can be considered as a statutory endorsement of breaches of contracts. Insolvency law is not a synonym of rescue law. As a result, insolvency rules should apply only when contractual or general law remedies are no longer appropriate to deal with the ailing debtor. The main purpose of this research is therefore to investigate when, to what extent and if at all statutory insolvency rules should depart from the law of contract in market-driven jurisdictions. As a result, this study focuses on selected common law jurisdictions - the United States and the United Kingdom (rectius, England and Wales) - to investigate the circumstances that should justify the enforcement of insolvency (rectius, corporate distress) rules. This thesis complements problem and principle-informed theories to suggest a novel mechanism to determine the limit of insolvency law. To date, practitioners, scholars and judges have not fully acknowledged this issue and its implications for business practice. Legal systems fail to provide comprehensive guidance on the matter of the co-ordination between insolvency rules and the enforceability of the rights negotiated by the parties in their contracts. The aim of this thesis is to acknowledge, critically analyse, investigate 10 and suggest solutions to this issue and, in doing so, make an original and significant contribution to the field. This work will propose an innovative solution to determine when companies should rely on corporate distress remedies. Nevertheless, it does not claim that the proposed approach is the best or optimal one. It highlights the benefit that the proposed conceptualisation is expected to achieve over existing statutory and theoretical approaches and it identifies the factors that are most likely to promote (and to thwart) this new conceptualisation of the law of corporate distress

A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text

Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies

Sustainable development under the conditions of European integration. Part I

This collective monograph offers the description of sustainable development in the condition of European integration. The authors of individual chapters have chosen such point of view for the topic which they considered as the most important and specific for their field of study using the methods of logical and semantic analysis of concepts, the method of reflection, textual reconstruction and comparative analysis. The theoretical and applied problems of sustainable development in the condition of European integration are investigated in the context of economics, education, cultural, politics and law