An empirical study on credit evaluation of SMEs based on detailed loan data

Small and micro-sized Enterprises (SMEs) are an important part of Chinese economic system.The establishment of credit evaluating model of SMEs can effectively help financial intermediaries to reveal credit risk of enterprises and reduce the cost of enterprises information acquisition. Besides it can also serve as a guide to investors which also helps companies with good credit. This thesis conducts an empirical study based on loan data from a Chinese bank of loans granted to SMEs. The study aims to develop a data-driven model that can accurately predict if a given loan has an acceptable risk from the bank’s perspective, or not. Furthermore, we test different methods to deal with the problem of unbalanced class and uncredible sample. Lastly, the importance of variables is analyzed. Remaining Unpaid Principal, Floating Interest Rate, Time Until Maturity Date, Real Interest Rate, Amount of Loan all have significant effects on the final result of the prediction.The main contribution of this study is to build a credit evaluation model of small and micro enterprises, which not only helps commercial banks accurately identify the credit risk of small and micro enterprises, but also helps to overcome creditdifficulties of small and micro enterprises.As pequenas e microempresas constituem uma parte importante do sistema económico chinês. A definição de um modelo de avaliação de crédito para estas empresas pode ajudar os intermediários financeiros a revelarem o risco de crédito das empresas e a reduzirem o custo de aquisição de informação das empresas. Além disso, pode igualmente servir como guia para os investidores, auxiliando também empresas com bom crédito. Na presente tese apresenta-se um estudo empírico baseado em dados de um banco chinês relativos a empréstimos concedidos a pequenas e microempresas. O estudo visa desenvolver um modelo empírico que possa prever com precisão se um determinado empréstimo tem um risco aceitável do ponto de vista do banco, ou não. Além disso, são efetuados testes com diferentes métodos que permitem lidar com os problemas de classes de dados não balanceadas e de amostras que não refletem o problema real a modelar. Finalmente, é analisada a importância relativa das variáveis. O montante da dívida por pagar, a taxa de juro variável, o prazo até a data de vencimento, a taxa de juro real, o montante do empréstimo, todas têm efeitos significativos no resultado final da previsão. O principal contributo deste estudo é, assim, a construção de um modelo de avaliação de crédito que permite apoiar os bancos comerciais a identificarem com precisão o risco de crédito das pequenas e micro empresas e ajudar também estas empresas a superarem as suas dificuldades de crédito

The total assessment profile, volume 2

Appendices are presented which include discussions of interest formulas, factors in regionalization, parametric modeling of discounted benefit-sacrifice streams, engineering economic calculations, and product innovation. For Volume 1, see

SME default prediction: A systematic methodology-focused review

This study reviews the methodologies used in the literature to predict failure in small and medium-sized enterprises (SMEs). We identified 145 SMEs’ default prediction studies from 1972 to early 2023. We summarized the methods used in each study. The focus points are estimation methods, sample re-balancing methods, variable selection techniques, validation methods, and variables included in the literature. More than 1,200 factors used in failure prediction models have been identified, along with 54 unique feature selection techniques and 80 unique estimation methods. Over one-third of the studies do not use any feature selection method, and more than one-quarter use only in-sample validation. Our main recommendation for researchers is to use feature selection and validate results using hold-out samples or cross-validation. As an avenue for further research, we suggest in-depth empirical comparisons of estimation methods, feature selection techniques, and sample re-balancing methods based on some large and commonly used datasets.publishedVersio

Technical report of data mining

No abstract availabl

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

New Perspectives on Institutional Intermediaries and Small Business in Developing Countries:the Importance of Institutional Contexts and the Role of Dynamic Capabilities

Governments, non-governmental organizations, microfinance organizations, and UN development agencies provide programs to support small-scale entrepreneurship in developing countries. Small-scale entrepreneurship is considered one of the most prominent solutions to alleviate poverty and reduce unemployment among young people and women. In the academic literature, these organizations are referred to as institutional intermediaries (in short intermediaries). They support small enterprises with two types of programs aimed at improving: 1. firm business capabilities and 2. the institutional environments. Yet, the literature streams that emerged to study these programs report a limited impact regarding their effectiveness. This dissertation, beyond aspects explored in these literature streams, examines the conditions under which intermediaries’ programs can be effective in helping small enterprises improve their performance. The results of this thesis show that there is a complementarity effect between the two types of programs, in that they reinforce each other when combined. The research shows that the combined impact on firm performance is higher than the effects of the two programs provided individually. In addition, the effectiveness of intermediaries’ programs is contingent on firms’ ability to exploit the opportunities made available by the programs. This ability, or absorptive capacity, relates to acquiring and exploiting external knowledge. The development of such an ability goes beyond one intermediary’s intervention and requires that firms build on knowledge acquired from collaboration with different intermediaries. In sum, these findings have important implications for the design and implementation of intermediaries’ programs to better support small enterprises

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Mobile app recommendations using deep learning and big data

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMRecommender systems were first introduced to solve information overload problems in enterprises. Over the last decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media and mobile app stores. Several methods have been proposed over the years to build recommender systems. The most popular approaches are based on collaborative filtering techniques, which leverage the similarities between consumer tastes. But the current state of the art in recommender systems is deep-learning methods, which can leverage not only item consumption data but also content, context, and user attributes. Mobile app stores generate data with Big Data properties from app consumption data, behavioral, geographic, demographic, social network and user-generated content data, which includes reviews, comments and search queries. In this dissertation, we propose a deep-learning architecture for recommender systems in mobile app stores that leverage most of these data sources. We analyze three issues related to the impact of the data sources, the impact of embedding layer pretraining and the efficiency of using Kernel methods to improve app scoring at a Big Data scale. An experiment is conducted on a Portuguese Android app store. Results suggest that models can be improved by combining structured and unstructured data. The results also suggest that embedding layer pretraining is essential to obtain good results. Some evidence is provided showing that Kernel-based methods might not be efficient when deployed in Big Data contexts