A new selection operator for genetic algorithms that balances between premature convergence and population diversity

The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics

Machine learning for personal credit evaluation: A systematic review

The importance of information in today's world as it is a key asset for business growth and innovation. The problem that arises is the lack of understanding of knowledge quality properties, which leads to the development of inefficient knowledge-intensive systems. But knowledge cannot be shared effectively without effective knowledge-intensive systems. Given this situation, the authors must analyze the benefits and believe that machine learning can benefit knowledge management and that machine learning algorithms can further improve knowledge-intensive systems. It also shows that machine learning is very helpful from a practical point of view. Machine learning not only improves knowledge-intensive systems but has powerful theoretical and practical implementations that can open up new areas of research. The objective set out is the comprehensive and systematic literature review of research published between 2018 and 2022, these studies were extracted from several critically important academic sources, with a total of 73 short articles selected. The findings also open up possible research areas for machine learning in knowledge management to generate a competitive advantage in financial institutions.Campus Lima Centr

Are incumbent banks bygones in the face of digital transformation?

Digital transformation has received considerable scholarly attention in areas of management, business, information systems, information technology, and marketing. In particular, retail banks have been at the forefront of technological revolution characterized by rapid deployment and innovation of digital services, exponential pace of change and innovative breakthroughs that alter conventional banking practice. However, the term digital transformation is often misunderstood as a straightforward deployment of latest information communication technologies. In practice, technological investments entail not only risk but also require an understanding of the relationship between technological, organizational culture and institutional change within certain boundaries of regulatory framework. Digital transformation is far from simple, certain or predictable and likely to be disruptive or transformative with immutable impacts upon associated organizational outcomes related to technical capabilities and behaviors. The present study attempts to explore and develop a framework for understanding digital transformation by examining the development, deployment and use of digital technologies in retail banking. Within a social informatics perspective, this study examines the effects of digital technologies on retail banks operations, structure and capabilities of those who deploy, implement and use it. Using a grounded theory approach the study explores theoretical constructs by reviewing the literature and analysing primary field data including data from retail banks and interviews with senior professionals. The findings provide the pitfalls and successful approaches towards the digital transformation journey. This includes the ordinary dilemmas that the managers face in order to deliver the projects at hand

A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING

During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results

Análise dos Determinantes no Grau de Evidenciação do Risco de Crédito em Centrais de Cooperativas de Crédito

TCC (Graduação) - Universidade Federal de Santa Catarina. Centro Socioeconômico. Ciências Contábeis.A transparência Organizacional dos dados contábeis e financeiros amplia a possibilidade de o usuário da informação realizar tomadas de decisões com eficiência, e consequentemente, minimizar as chances de possíveis perdas. Por este motivo, o presente estudo, buscou analisar o nível de aderência aos indicadores de divulgação de riscos de crédito e os determinantes que impactam no grau de evidenciação do gerenciamento de risco de crédito praticado pelas cooperativas de crédito. A amostra é constituída por Centrais de Cooperativas de Crédito entre o período de 2015 a 2018, sendo que as observações para a variável dependente disclosure do risco de crédito (DRC) foram selecionadas com base na metodologia aplicada por Dantas et al. (2010). As demais variáveis: ativo (TAM), índice da basileia (IB), índice de imobilização (II) e rentabilidade (RENT) foram selecionadas a partir das bases de dados do Banco Central do Brasil. Para verificação das variáveis foram formuladas três hipóteses, analisados pela estatística descritiva e por dados em painel. Os resultados evidenciaram que o nível de divulgação dos riscos de crédito é inferior ao esperado, e no que que tange à explicação do nível de divulgação, as regressões demonstraram que o tamanho e o índice de imobilização são significativos para explicar a variação do nível de divulgação. Todavia, diferente do esperado, a rentabilidade não se demonstrou relevante para determinar o nível de divulgação, ou seja, melhores níveis de gestão administrativa e financeira não estão relacionados ao maior desempenho econômico-financeiro, nas cooperativas de crédito

Using deep learning and explainable AI to predict and explain loan defaults

The use of machine learning in finance is increasing, and while deep learning models are becoming the state of the art to make predictions, the difficulty of interpreting them is a drawback. This is especially so in finance, where each result that a model outputs must be explainable and justifiable. In recent years, novel explainable AI methods have been researched and developed to explain deep learning models and their decisions. The aim of this bachelor thesis was to analyze a use case in credit scoring, specifically in loan defaulting, with deep learning and explainable AI. It also aimed to show that deep learning can be used to predict loan defaults in finance, that explainable AI methods offer insights for interpreting the black box’s internal decisions, and furthermore, that it is possible to improve models with insights from explainable AI. A peer-to-peer loan dataset from Bondora with 164,547 instances and 112 features was analyzed, pre-processed, and prepared for deep learning. Multiple neural networks with different parameters were fitted and evaluated to find the best hyperparameters for loan default predicting with the dataset. A post hoc analysis with SHAP was applied to the best model to retrieve insights from it. These insights were then used to explain the model’s decisions and to adjust it. The results show that the model has an AUC of 0.72 and can therefore differentiate between a defaulted and a not defaulted loan with a probability of 72%. In addition, a recall of 0.88 was reached, meaning the model predicts 88% of defaulted loans correctly. Furthermore, the insights gained from explainable AI enabled the creation of a second, adjusted model that reached equally good performance with only half of the features. Moreover, the explainable AI insights were used to determine and analyze the fifteen features which influence the model the most. The three most influential were debt-to-income, applied amount and loan duration. Additionally, two loan instances from the dataset were analyzed in detail with SHAP. In conclusion, using deep learning and explainable AI we were able to predict loan defaults, and interpret as well as explain the model’s decisions. Moreover, the explainable AI insights could be used to adjust and improve the model. A complete use case in credit scoring is shown in this thesis, highlighting that deep learning and explainable AI can be used in finance. However, the gained insights from the explainable AI methods were very specific to the used dataset and therefore further research with different datasets would be interesting

Entropy maximizing evolutionary design optimization of water distribution networks under multiple operating conditions

The informational entropy model for flow networks was formulated over 30 years ago by Tanyimboh and Templeman (University of Liverpool, UK) for a single discrete operating condition that typically comprises the maximum daily demands and was undefined for water distribution networks (WDNs) under multiple operating conditions. Its extension to include multiple independent discrete operating conditions was investigated experimentally herein considering the relationships between flow entropy and hydraulic capacity reliability and redundancy. A novel penalty-free multi-objective genetic algorithm was developed to minimize the initial construction cost and maximize the flow entropy subject to the design constraints. Furthermore, optimized designs derived from the maximum daily demands as a single discrete operating condition were compared to those derived from a combination of discrete operating conditions. Optimized designs from a combination of discrete operating conditions outperformed those from a single operating condition in terms of performance and initial construction cost. The best results overall were achieved by maximizing the sum of the flow entropies of the discrete operating conditions. The logical inference from the results is that the flow entropy of multiple discrete operating conditions is the sum of their respective entropies. In addition, a crucial property of the resulting flow entropy model is that it is bias free with respect to the individual operating conditions; hitherto a fundamental weakness concerning the practical application of the flow entropy model to WDNs is thus addressed

Establishment of risk control mechanisms for farmers' microcredit in China's rural areas: the case of HN Province

This thesis aims to address the practical and institutional problems in the development of farmers' microcredit to ultimately contribute to achieve sustainable development. The author, based on his long-term work experience in rural finance, analyzes the current situation of microcredit in rural areas and presents a set of mechanisms for risk management and scientific pricing of farmers’ in the framework of the construction of an iterative model with the support of statistical analysis. To be specific, based on improving farmers' scheduled repayment rate and a study of farmers' microcredit factors, criteria are formulated for selecting credible borrowers among farmers; Making full use of the decisive role of the market, an effective risk management model is gradually formulated that helps to foster a credit-based financial service environment in rural areas. Besides, software engineering is utilized to optimize and control farmers' microcredit risk management and scientific pricing of credit products. The study suggests that the farmers' microcredit risk control mechanisms established can effectively enable farmers' microcredit financial institutions to function as a main market player, while overcoming theoretical and practical difficulties in microcredit for farmers. Four suggestions are also presented. First, ensure that farmers enjoy the "right of approval" when applying for loans; second, transfer the "right of pricing" loan interests to farmers; third, transfer the "right of salary distribution" and risk control of loans to loan officers; and fourth, entrust the "management right" of loans to the system.Esta tese tem como objetivo abordar os problemas práticos e institucionais no desenvolvimento do microcrédito dos agricultores para, finalmente, contribuir para alcançar o desenvolvimento sustentável. O autor, com base em sua experiência do trabalho de longo prazo em finanças rurais, analisa a situação atual do microcrédito em áreas rurais e apresenta um conjunto de mecanismos para a gestão do risco e a determinação científica de preços para o microcrédito dos agricultores no quadro da construção de um modelo interativo com o apoio da análise estatística. Mais especificamente, com base na melhoria da previsão da taxa de reembolso do microcrédito pelos agricultores e em um estudo dos fatores condicionantes desse reembolso, são formulados critérios para selecionar mutuários confiáveis entre os agricultores; Fazendo pleno uso do papel decisivo do mercado, é formulado gradualmente um modelo eficaz de gestão de risco que ajuda a promover nas áreas rurais um ambiente favorável de serviços financeiros baseados no crédito. Além disso, a engenharia de software é utilizada para otimizar e controlar a gestão de riscos de microcrédito dos agricultores e a determinação científica dos preços dos produtos de crédito. O estudo demonstra que o mecanismo de controle de risco de microcrédito dos agricultores assim estabelecido pode efetivamente permitir que as instituições financeiras de microcrédito funcionem como um participante principal do mercado, ultrapassando dificuldades que a teoria e a prática têm revelado no microcrédito para os agricultores. Quatro sugestões são ainda apresentadas. Em primeiro lugar, garantir que os agricultores desfrutem do "direito de aprovação" quando solicitam empréstimos. Em segundo lugar, transferir para os agricultores "direito de determinação do preço" da taxa de juro do empréstimo. Em terceiro lugar, passar o "direito de distribuição salarial" e o controle de risco dos empréstimos para os agentes de crédito. Em quarto lugar, confiar ao sistema o "direito de gestão" dos empréstimos

Obtención de reglas de clasificación difusas utilizando técnicas de optimización : Caso de estudio Riesgo Crediticio

El aporte central de esta tesis es la definición de un nuevo método capaz de generar un conjunto de reglas de clasificación difusas de fácil interpretación, baja cardinalidad y una buena precisión. Estas características ayudan a identificar y comprender las relaciones presentes en los datos facilitando de esta forma la toma de decisiones. El nuevo método propuesto se denomina FRvarPSO (Fuzzy Rules variable Particle Swarm Oprmization) y combina una red neuronal competitiva con una técnica de optimización basada en cúmulo de partículas de población variable para la obtención de reglas de clasificación difusas, capaces de operar sobre atributos nominales y numéricos. Los antecedentes de las reglas están formados por atributos nominales y/o condiciones difusas. La conformación de estas últimas requiere conocer el grado de pertenencia a los conjuntos difusos que definen a cada variable lingüística. Esta tesis propone tres alternativas distintas para resolver este punto. Uno de los aportes de esta tesis radica en la definición de la función de aptitud o fitness de cada partícula basada en un ”Criterio de Votación” que pondera de manera difusa la participación de las condiciones difusas en la conformación del antecedente. Su valor se obtiene a partir de los grados de pertenencia de los ejemplos que cumplen con la regla y se utiliza para reforzar el movimiento de la partícula en la dirección donde se encuentra el valor más alto. Con la utilización de PSO las partículas compiten entre ellas para encontrar a la mejor regla de la clase seleccionada. La medición se realizó sobre doce bases de datos del repositorio UCI (Machine Learning Repository) y tres casos reales en el área de crédito del Sistema Financiero del Ecuador asociadas al riesgo crediticio considerando un conjunto de variables micro y macroeconómicas. Otro de los aportes de esta tesis fue haber realizado una consideración especial en la morosidad del cliente teniendo en cuenta los días de vencimiento de la cartera otorgada; esto fue posible debido a que se tenía información del cliente en un horizonte de tiempo, una vez que el crédito se había concedido Se verificó que con este análisis las reglas difusas obtenidas a través de FRvarPSO permiten que el oficial de crédito de respuesta al cliente en menor tiempo, y principalmente disminuya el riesgo que representa el otorgamiento de crédito para las instituciones financieras. Lo anterior fue posible, debido a que al aplicar una regla difusa se toma el menor grado de pertenencia promedio de las condiciones difusas que forman el antecedente de la regla, con lo que se tiene una métrica proporcional al riesgo de su aplicación.Tesis en cotutela con la Universitat Rovira i Virgili (URV) (España).Facultad de InformáticaUniversitat Rovira i Virgil