Machine Learning Application To Loan Default Comparison And Prediction A Case Study In Usa And China
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Machine Learning Application To Loan Default Comparison And Prediction A Case Study In Usa And China
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Author : 詹鳳華
language : en
Publisher:
Release Date : 2017
Machine Learning Application To Loan Default Comparison And Prediction A Case Study In Usa And China written by 詹鳳華 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Proceedings Of The 2023 International Conference On Management Innovation And Economy Development Mied 2023
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Author : Luiz Moutinho
language : en
Publisher: Springer Nature
Release Date : 2023-10-29
Proceedings Of The 2023 International Conference On Management Innovation And Economy Development Mied 2023 written by Luiz Moutinho and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-29 with Business & Economics categories.
This is an open access book. Management innovation is the secret to success for companies and governments. Management breakthroughs can deliver a solid advantage for innovating organizations. On the other hand, Management Innovation is essential for society's economy growth. But what is management innovation? How to achieve economy development in many fields? The following international conference will answer and discuss those questions. The 2023 International Conference on Management Innovation and Economy Development(MIED 2023)will be held on July 28–30, 2023 in Qingdao, China. The conference mainly focused on research fields such as management innovation and economy development. MIED 2023 provides an open platform that brings worldwide scholars together to present current research and stimulate new growth in management and economy. MIED 2023 invites papers from all areas of management innovation and economy development. And We sincerely invite experts, scholars, business people, and other relevant people from universities and scientific research institutions from all over the world to attend the conference.
Operational Research In Business And Economics
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Author : Evangelos Grigoroudis
language : en
Publisher: Springer
Release Date : 2016-07-29
Operational Research In Business And Economics written by Evangelos Grigoroudis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-29 with Business & Economics categories.
This book gathers a selection of refereed papers presented at the 4th International Symposium and 26th National Conference of the Hellenic Operational Research Society. It highlights recent scientific advances in operational research and management science (OR/MS), with a focus on linking OR/MS with other areas of quantitative methods in a multidisciplinary framework. Topics covered include areas such as business process modeling, supply chain management, organization performance and strategy planning, revenue management, financial applications, production planning, metaheuristics, logistics, inventory systems, and energy systems.
Recent Trends In Information And Communication Technology
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Author : Faisal Saeed
language : en
Publisher: Springer
Release Date : 2017-05-24
Recent Trends In Information And Communication Technology written by Faisal Saeed and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-24 with Technology & Engineering categories.
This book presents 94 papers from the 2nd International Conference of Reliable Information and Communication Technology 2017 (IRICT 2017), held in Johor, Malaysia, on April 23–24, 2017. Focusing on the latest ICT innovations for data engineering, the book presents several hot research topics, including advances in big data analysis techniques and applications; mobile networks; applications and usability; reliable communication systems; advances in computer vision, artificial intelligence and soft computing; reliable health informatics and cloud computing environments, e-learning acceptance models, recent trends in knowledge management and software engineering; security issues in the cyber world; as well as society and information technology.
Signal Processing Techniques For Computational Health Informatics
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Author : Md Atiqur Rahman Ahad
language : en
Publisher: Springer Nature
Release Date : 2020-10-07
Signal Processing Techniques For Computational Health Informatics written by Md Atiqur Rahman Ahad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-07 with Technology & Engineering categories.
This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time–frequency and complexity domain, and image processing techniques in different image modalities. Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human–computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis. In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.
Smart Computing And Communication
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Author : Meikang Qiu
language : en
Publisher: Springer Nature
Release Date : 2022-03-14
Smart Computing And Communication written by Meikang Qiu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-14 with Computers categories.
This book constitutes the proceedings of the 6th International Conference on Smart Computing and Communication, SmartCom 2021, which took place in New York City, USA, during December 29–31, 2021.* The 44 papers included in this book were carefully reviewed and selected from 165 submissions. The scope of SmartCom 2021 was broad, from smart data to smart communications, from smart cloud computing to smart security. The conference gathered all high-quality research/industrial papers related to smart computing and communications and aimed at proposing a reference guideline for further research. * Conference was held online due to the COVID-19 pandemic.
Los Angeles Magazine
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Author :
language : en
Publisher:
Release Date : 2003-11
Los Angeles Magazine written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11 with categories.
Los Angeles magazine is a regional magazine of national stature. Our combination of award-winning feature writing, investigative reporting, service journalism, and design covers the people, lifestyle, culture, entertainment, fashion, art and architecture, and news that define Southern California. Started in the spring of 1961, Los Angeles magazine has been addressing the needs and interests of our region for 48 years. The magazine continues to be the definitive resource for an affluent population that is intensely interested in a lifestyle that is uniquely Southern Californian.
Business Analytics And Business Intelligence Machine Learning Model To Predict Bank Loan Defaults
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Author : dr. V.V.L.N. Sastry
language : en
Publisher: Idea Publishing
Release Date : 2020-05-29
Business Analytics And Business Intelligence Machine Learning Model To Predict Bank Loan Defaults written by dr. V.V.L.N. Sastry and has been published by Idea Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-29 with Computers categories.
Predictive Analytics offers a unique opportunity to identify future trends and allows organizations to act upon them. In this book we are dealing with ‘loan default’ which is always a threat to banks and financial institutions and should be predicted in advance based on various features of the borrowers or applicants. In this book we aim at applying machine learning models to classify the borrowers with and without loan default from a group of predicting variables and evaluate their performance. As a part of building a model to predict loan default, we have submitted in detail the introduction of the problem, exploratory data analysis (EDA), data cleaning and pre-processing, model building, interpretation, model tuning, model validation, and final interpretation & recommendations. Under the current project of loan default forming part of predictive analytics of business analytics and intelligence, we have studied research-based review parameters in detail which have also been annexed for ready reference as Annexure I. Data dictionary has been annexed as Annexure-2. R. Code for the same is provided at the URL which can be downloaded from www.drvvlnsastry.com/businessanalytics/data The study finds out that logistic regression is the best model to classify those applicants with loan default.
Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-13
Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-13 with Computers categories.
In this project, we aim to predict the risk of defaulting on a loan based on customer behavior using machine learning and deep learning techniques. We start by exploring the dataset and understanding its structure and contents. The dataset contains various features related to customer behavior, such as credit history, income, employment status, loan amount, and more. We analyze the distribution of these features to gain insights into their characteristics and potential impact on loan default. Next, we preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features. This ensures that the data is in a suitable format for training machine learning models. To predict the risk flag for loan default, we apply various machine learning models. We start with logistic regression, which models the relationship between the input features and the probability of loan default. We evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score. Next, we employ decision tree-based algorithms, such as random forest and gradient boosting, which can capture non-linear relationships and interactions among features. These models provide better predictive power and help identify important features that contribute to loan default. Additionally, we explore support vector machines (SVM), which aim to find an optimal hyperplane that separates the loan default and non-default instances in a high-dimensional feature space. SVMs can handle complex data distributions and can be tuned to optimize the classification performance. After evaluating the performance of these machine learning models, we turn our attention to deep learning techniques. We design and train an Artificial Neural Network (ANN) to predict the risk flag for loan default. The ANN consists of multiple layers of interconnected neurons that learn hierarchical representations of the input features. We configure the ANN with several hidden layers, each containing a varying number of neurons. We use the ReLU activation function to introduce non-linearity and ensure the model's ability to capture complex relationships. Dropout layers are incorporated to prevent overfitting and improve generalization. We compile the ANN using the Adam optimizer and the binary cross-entropy loss function. We train the model using the preprocessed dataset, splitting it into training and validation sets. The model is trained for a specific number of epochs, with a defined batch size. Throughout the training process, we monitor the model's performance using metrics such as loss and accuracy on both the training and validation sets. We make use of early stopping to prevent overfitting and save the best model based on the validation performance. Once the ANN is trained, we evaluate its performance on a separate test set. We calculate metrics such as accuracy, precision, recall, and F1-score to assess the model's predictive capabilities in identifying loan default risk. In conclusion, this project involves the exploration of a loan dataset, preprocessing of the data, and the application of various machine learning models and a deep learning ANN to predict the risk flag for loan default. The machine learning models, including logistic regression, decision trees, SVM, and ensemble methods, provide insights into feature importance and achieve reasonable predictive performance. The deep learning ANN, with its ability to capture complex relationships, offers the potential for improved accuracy in predicting loan default risk. By combining these approaches, we can assist financial institutions in making informed decisions and managing loan default risks more effectively.
The Use Of Artificial Intelligence In The Decision Making Process For Bank Loans
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Author : Zainab Noori Falih
language : en
Publisher:
Release Date : 2021
The Use Of Artificial Intelligence In The Decision Making Process For Bank Loans written by Zainab Noori Falih and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Despite the consumer loan defaults and the increased opposition in the banking industry, most commercial banks remain wary of using Artificial Intelligence (AI) software systems to help make loan resolutions. This study suggests a variety of Artificial Neural Network (ANN) and machine learning models that identify ANN as a useful method for analysing loan applications and assisting commercial banks in making personal loan decisions. The importance of this work is to help in application decision-making to get accurate results, in this case, a knowledge discovery approach is needed. The tests are carried out with the aid of tools, such as Python scripting. The main goal of this dissertation is to introduce a method that will speed up the advancement of banking work. ANN was found to be a promising technology that can be used to evaluate loan applications by commercial banks. Neural computing technology can also provide educational skills not detected in other systems due to the complexity of loan evaluation tools and implementation gaps. Next to typical prevision and classification procedures are neural networks because they can capture nonlinear and dynamic interactions. The major objectives of this research were to strengthen and compare the efficiency of the multi-layer perceptron (MLP) of ANN in loan applications for board machine models, namely the ensemble boosting and average Filtering (5,000 samples with 14 characteristics). This study created a dependable master and easy-to-use approach for discovering knowledge based on the neural network and machine learning model. This study will show the results of different algorithms to see which one of them is giving more accurate results. The results were analysed and the accuracy performance was compared. Among the 8 models that we have implemented, xgb classifier, random forest, decision tree, and logistic regression have the highest accuracy and best F1 Score with almost accuracy of 98% and F1-Score of 91%.