The document presents a study comparing various machine learning algorithms for personal credit scoring, including logistic regression, multilayer perceptron, support vector machines, AdaBoostM1, and Hidden Layer Learning Vector Quantization (HLVQ-C). The models were tested on datasets from a Portuguese bank. HLVQ-C achieved the highest accuracy on both datasets and the best performance on a proposed "usefulness" metric, making it the most effective model for credit scoring applications according to this research.
The document proposes using several machine learning algorithms, including multilayer perceptron neural networks, logistic regression, support vector machines, AdaBoostM1, and Hidden Layer Learning Vector Quantization (HLVQ-C), to improve personal credit scoring accuracy. The algorithms were tested on a large dataset from a Portuguese bank containing over 400,000 entries. HLVQ-C achieved the most accurate results, outperforming traditional linear methods. The document introduces a "usefulness" measure to evaluate classifiers based on earnings from correctly denying credit to risky applicants and losses from misclassifications.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
A Review on Credit Card Default Modelling using Data ScienceYogeshIJTSRD
In the last few years, credit card issuers have become one of the major consumer lending products in the U.S. as well as several other developed nations of the world, representing roughly 30 of total consumer lending USD 3.6 tn in 2016 . Credit cards issued by banks hold the majority of the market share with approximately 70 of the total outstanding balance. Bank’s credit card charge offs have stabilized after the financial crisis to around 3 of the outstanding total balance. However, there are still differences in the credit card charge off levels between different competitors. Harsh Nautiyal | Ayush Jyala | Dishank Bhandari "A Review on Credit Card Default Modelling using Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42461.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42461/a-review-on-credit-card-default-modelling-using-data-science/harsh-nautiyal
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
IRJET- Prediction of Credit Risks in Lending Bank LoansIRJET Journal
This document discusses machine learning models for predicting credit risk in bank loan applications. It begins with an introduction to credit risk assessment and types of loans. Then, it describes how machine learning can be used to more accurately evaluate borrowers' ability to repay loans based on important variables like borrower characteristics, loan details, and repayment status. The document proposes using artificial neural network and support vector machine models to classify borrowers as good or bad credit risks based on these variables. It evaluates the accuracy of support vector machines and boosted decision tree models for the task of credit risk prediction.
Default Probability Prediction using Artificial Neural Networks in R ProgrammingVineet Ojha
The objective of the project is to analyze the ability of the Artificial Neural Network Model
developed to forecast the credit risk profile of retails banking loan consumers and credit card
customers.
From a theoretical point of view, this project introduces a literature review on the detailed
working and the application of Artificial Neural Networks for credit risk management.
Practically, the aim of this project is presenting a model for estimating the Probability of Default
using Artificial Neural Network to accrue benefit non-linear models.
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone project—part of the UVA Data Science Institute Master of Science in Data Science program—that addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
The document proposes using several machine learning algorithms, including multilayer perceptron neural networks, logistic regression, support vector machines, AdaBoostM1, and Hidden Layer Learning Vector Quantization (HLVQ-C), to improve personal credit scoring accuracy. The algorithms were tested on a large dataset from a Portuguese bank containing over 400,000 entries. HLVQ-C achieved the most accurate results, outperforming traditional linear methods. The document introduces a "usefulness" measure to evaluate classifiers based on earnings from correctly denying credit to risky applicants and losses from misclassifications.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
A Review on Credit Card Default Modelling using Data ScienceYogeshIJTSRD
In the last few years, credit card issuers have become one of the major consumer lending products in the U.S. as well as several other developed nations of the world, representing roughly 30 of total consumer lending USD 3.6 tn in 2016 . Credit cards issued by banks hold the majority of the market share with approximately 70 of the total outstanding balance. Bank’s credit card charge offs have stabilized after the financial crisis to around 3 of the outstanding total balance. However, there are still differences in the credit card charge off levels between different competitors. Harsh Nautiyal | Ayush Jyala | Dishank Bhandari "A Review on Credit Card Default Modelling using Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42461.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42461/a-review-on-credit-card-default-modelling-using-data-science/harsh-nautiyal
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
IRJET- Prediction of Credit Risks in Lending Bank LoansIRJET Journal
This document discusses machine learning models for predicting credit risk in bank loan applications. It begins with an introduction to credit risk assessment and types of loans. Then, it describes how machine learning can be used to more accurately evaluate borrowers' ability to repay loans based on important variables like borrower characteristics, loan details, and repayment status. The document proposes using artificial neural network and support vector machine models to classify borrowers as good or bad credit risks based on these variables. It evaluates the accuracy of support vector machines and boosted decision tree models for the task of credit risk prediction.
Default Probability Prediction using Artificial Neural Networks in R ProgrammingVineet Ojha
The objective of the project is to analyze the ability of the Artificial Neural Network Model
developed to forecast the credit risk profile of retails banking loan consumers and credit card
customers.
From a theoretical point of view, this project introduces a literature review on the detailed
working and the application of Artificial Neural Networks for credit risk management.
Practically, the aim of this project is presenting a model for estimating the Probability of Default
using Artificial Neural Network to accrue benefit non-linear models.
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone project—part of the UVA Data Science Institute Master of Science in Data Science program—that addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
IRJET- Credit Card Fraud Detection using Random ForestIRJET Journal
This document discusses using random forest machine learning algorithms to detect credit card fraud. It begins with an abstract that outlines using random forest classification on transaction data to improve fraud detection accuracy. The introduction then provides background on credit card fraud and how machine learning has been used for detection. It describes random forest as an advanced decision tree algorithm that can improve efficiency and accuracy over other methods. The paper proposes building a fraud detection model using random forest classification to analyze a transaction dataset and optimize result accuracy. Key performance metrics like accuracy, sensitivity and precision are evaluated.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
This document discusses the application of machine learning algorithms for fraud detection in the banking sector. It proposes using Classification and Regression Tree (CART), AdaBoost, LogitBoost, and Bagging algorithms to classify banking data and detect fraud. An experiment is conducted to analyze the performance of these algorithms on a banking data set. The results show that the Bagging algorithm has the lowest misclassification rate, indicating it performs better than the other algorithms at classifying banking data for fraud detection. In conclusion, the Bagging algorithm is deemed the best performing of the meta-learning algorithms analyzed for fraud detection in banking data.
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
The document analyzes variables associated with the interest rate of loans. It uses a dataset of peer-to-peer loans to build a multiple linear regression model relating interest rate to applicant characteristics. These include FICO score, amount requested, amount loaned, debt-to-income ratio, number of open credit lines, outstanding credit balances, credit inquiries, and loan length. The model fits the data well (R-squared = 0.7942) and finds a strong association between interest rate and FICO score in particular. The analysis identifies key variables for determining loan interest rates.
The document analyzes loan data from Lending Club to determine the relationship between interest rate and FICO score. It finds a significant negative association, with interest rate decreasing by 1% for every increase of 0.08 in FICO score. Other variables like loan length, amount borrowed, and amount funded also influence interest rate and FICO score. Including these in the analysis improves the model but does not remove the negative relationship between interest rate and FICO score.
Credit Card Fraud Detection Using Unsupervised Machine Learning AlgorithmsHariteja Bodepudi
This document summarizes a research paper that uses unsupervised machine learning algorithms to detect credit card fraud. It describes how credit card fraud has increased with the rise of online shopping and payments. Unsupervised algorithms are well-suited for this task since labeled fraud data can be difficult to obtain. The paper tests Isolation Forest, Local Outlier Factor, and One Class SVM on a credit card transaction dataset to find anomalies (fraudulent transactions). Isolation Forest achieved the highest accuracy at 99.74%, slightly outperforming Local Outlier Factor, while One Class SVM had much lower accuracy. The paper concludes unsupervised algorithms are effective for anomaly detection tasks like credit card fraud detection.
Fairness-aware Classifier with Prejudice Remover RegularizerToshihiro Kamishima
The document describes a method for fairness-aware classification that aims to remove prejudice from machine learning models. It introduces three types of potential prejudice - direct, indirect, and latent - that could result from correlations between the model's predictions and sensitive features like gender or race. The proposed method uses a prejudice remover regularizer added to logistic regression that constrains the mutual information between the model's predictions and sensitive features. Experimental results on census income data show the method achieves similar accuracy as baselines while improving fairness measures like normalized mutual information between predictions and sensitive features.
This document proposes a new approach to reduce computational time for credit scoring models based on support vector machines (SVM) and stratified sampling. The approach uses SVM incorporated with feature selection using F-scores and takes a sample of the dataset instead of the whole dataset to create the credit scoring model. The new method is shown to have competitive accuracy to other methods while significantly reducing computational time by optimizing features and reducing dataset size.
Correcting Popularity Bias by Enhancing Recommendation NeutralityToshihiro Kamishima
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
IRJET- Fraud Detection Algorithms for a Credit CardIRJET Journal
This document discusses algorithms for detecting credit card fraud. It compares the performance of two algorithms: random forest and K-nearest neighbors (KNN). Random forest uses decision trees to classify transactions as normal or fraudulent based on attributes of past transactions. KNN compares new transactions to historical ones based on attributes. The document tests these algorithms on a real-world credit card transaction dataset. It finds that random forest obtains good results on smaller datasets but has issues with imbalanced data. The authors' future work will focus on addressing these issues and improving the random forest algorithm.
Many customers often switch or unsubscribe (churn) from their telecom providers for a variety of reasons. These could range from unsatisfactory service, better pricing from competitors, customers moving to different cities etc. Therefore, telecom companies are interested in analyzing the patterns for customers who churn from their services and use the resultant analysis to determine in the future which customers are more likely to unsubscribe from their services. One such company is Telco Systems. Telco Systems is interested in identifying the precise patterns for their churning customers and have provided the customer data for this project.
This document summarizes a proposed model for a microcredit institution. It discusses performing a break-even analysis to understand the loan size and interest rates needed. It also proposes a mathematical model to analyze the creditworthiness of borrowing groups and measure the associated risks. The document provides background on microcredit and discusses several previous studies on topics like trust in lender-borrower relationships, experimental economics studies of microfinance mechanisms, the effects of microcredit on long-run development, and using insurance products to enhance access to microcredit for farmers.
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
This document provides a marking scheme for a database design and development exam. It specifies that alternative valid answers should be credited and instructs markers to round marks awarded for partial answers up to whole numbers. It also lists four questions to be answered, with guidance on marking each part.
An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
This document discusses using a multi-objective evolutionary algorithm (MOEA) for feature selection in bankruptcy prediction models. The goal is to maximize classifier accuracy while minimizing the number of features. A two-objective problem of minimizing features and maximizing accuracy is analyzed using logistic regression and support vector machines classifiers. The methodology is tested on financial data from 1200 French companies and shown to be an efficient feature selection approach, obtaining best results when optimizing both accuracy and classifier parameters simultaneously.
This document describes a multi-objective evolutionary algorithm that uses artificial neural networks to approximate fitness functions in order to reduce the number of exact function evaluations. The algorithm runs the evolutionary algorithm for an initial number of generations to collect a training dataset. It then trains a neural network on this dataset. The evolutionary algorithm continues running for additional generations, using the neural network to approximate some or all of the fitness function evaluations. The neural network approximation error is monitored, and the evolutionary algorithm switches back to using exact function evaluations when the error becomes too high. This process repeats until an acceptable Pareto front is found. The method was tested on benchmark multi-objective test functions and showed a 20-40% reduction in the number of exact function evaluations needed
1) O documento apresenta um estudo sobre a cisão de agregados metálicos.
2) A dissertação aplica vários modelos teóricos para descrever a física dos agregados, incluindo a Teoria dos Funcionais da Densidade, o Modelo da Geleia Estabilizada e o Modelo da Gota Líquida.
3) A cisão de agregados carregados é estudada usando o Modelo da Gota Líquida e o Método de Correção de Camadas para calcular barreiras de cisão e números críticos.
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
IRJET- Credit Card Fraud Detection using Random ForestIRJET Journal
This document discusses using random forest machine learning algorithms to detect credit card fraud. It begins with an abstract that outlines using random forest classification on transaction data to improve fraud detection accuracy. The introduction then provides background on credit card fraud and how machine learning has been used for detection. It describes random forest as an advanced decision tree algorithm that can improve efficiency and accuracy over other methods. The paper proposes building a fraud detection model using random forest classification to analyze a transaction dataset and optimize result accuracy. Key performance metrics like accuracy, sensitivity and precision are evaluated.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
This document discusses the application of machine learning algorithms for fraud detection in the banking sector. It proposes using Classification and Regression Tree (CART), AdaBoost, LogitBoost, and Bagging algorithms to classify banking data and detect fraud. An experiment is conducted to analyze the performance of these algorithms on a banking data set. The results show that the Bagging algorithm has the lowest misclassification rate, indicating it performs better than the other algorithms at classifying banking data for fraud detection. In conclusion, the Bagging algorithm is deemed the best performing of the meta-learning algorithms analyzed for fraud detection in banking data.
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
The document analyzes variables associated with the interest rate of loans. It uses a dataset of peer-to-peer loans to build a multiple linear regression model relating interest rate to applicant characteristics. These include FICO score, amount requested, amount loaned, debt-to-income ratio, number of open credit lines, outstanding credit balances, credit inquiries, and loan length. The model fits the data well (R-squared = 0.7942) and finds a strong association between interest rate and FICO score in particular. The analysis identifies key variables for determining loan interest rates.
The document analyzes loan data from Lending Club to determine the relationship between interest rate and FICO score. It finds a significant negative association, with interest rate decreasing by 1% for every increase of 0.08 in FICO score. Other variables like loan length, amount borrowed, and amount funded also influence interest rate and FICO score. Including these in the analysis improves the model but does not remove the negative relationship between interest rate and FICO score.
Credit Card Fraud Detection Using Unsupervised Machine Learning AlgorithmsHariteja Bodepudi
This document summarizes a research paper that uses unsupervised machine learning algorithms to detect credit card fraud. It describes how credit card fraud has increased with the rise of online shopping and payments. Unsupervised algorithms are well-suited for this task since labeled fraud data can be difficult to obtain. The paper tests Isolation Forest, Local Outlier Factor, and One Class SVM on a credit card transaction dataset to find anomalies (fraudulent transactions). Isolation Forest achieved the highest accuracy at 99.74%, slightly outperforming Local Outlier Factor, while One Class SVM had much lower accuracy. The paper concludes unsupervised algorithms are effective for anomaly detection tasks like credit card fraud detection.
Fairness-aware Classifier with Prejudice Remover RegularizerToshihiro Kamishima
The document describes a method for fairness-aware classification that aims to remove prejudice from machine learning models. It introduces three types of potential prejudice - direct, indirect, and latent - that could result from correlations between the model's predictions and sensitive features like gender or race. The proposed method uses a prejudice remover regularizer added to logistic regression that constrains the mutual information between the model's predictions and sensitive features. Experimental results on census income data show the method achieves similar accuracy as baselines while improving fairness measures like normalized mutual information between predictions and sensitive features.
This document proposes a new approach to reduce computational time for credit scoring models based on support vector machines (SVM) and stratified sampling. The approach uses SVM incorporated with feature selection using F-scores and takes a sample of the dataset instead of the whole dataset to create the credit scoring model. The new method is shown to have competitive accuracy to other methods while significantly reducing computational time by optimizing features and reducing dataset size.
Correcting Popularity Bias by Enhancing Recommendation NeutralityToshihiro Kamishima
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
IRJET- Fraud Detection Algorithms for a Credit CardIRJET Journal
This document discusses algorithms for detecting credit card fraud. It compares the performance of two algorithms: random forest and K-nearest neighbors (KNN). Random forest uses decision trees to classify transactions as normal or fraudulent based on attributes of past transactions. KNN compares new transactions to historical ones based on attributes. The document tests these algorithms on a real-world credit card transaction dataset. It finds that random forest obtains good results on smaller datasets but has issues with imbalanced data. The authors' future work will focus on addressing these issues and improving the random forest algorithm.
Many customers often switch or unsubscribe (churn) from their telecom providers for a variety of reasons. These could range from unsatisfactory service, better pricing from competitors, customers moving to different cities etc. Therefore, telecom companies are interested in analyzing the patterns for customers who churn from their services and use the resultant analysis to determine in the future which customers are more likely to unsubscribe from their services. One such company is Telco Systems. Telco Systems is interested in identifying the precise patterns for their churning customers and have provided the customer data for this project.
This document summarizes a proposed model for a microcredit institution. It discusses performing a break-even analysis to understand the loan size and interest rates needed. It also proposes a mathematical model to analyze the creditworthiness of borrowing groups and measure the associated risks. The document provides background on microcredit and discusses several previous studies on topics like trust in lender-borrower relationships, experimental economics studies of microfinance mechanisms, the effects of microcredit on long-run development, and using insurance products to enhance access to microcredit for farmers.
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
This document provides a marking scheme for a database design and development exam. It specifies that alternative valid answers should be credited and instructs markers to round marks awarded for partial answers up to whole numbers. It also lists four questions to be answered, with guidance on marking each part.
An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
This document discusses using a multi-objective evolutionary algorithm (MOEA) for feature selection in bankruptcy prediction models. The goal is to maximize classifier accuracy while minimizing the number of features. A two-objective problem of minimizing features and maximizing accuracy is analyzed using logistic regression and support vector machines classifiers. The methodology is tested on financial data from 1200 French companies and shown to be an efficient feature selection approach, obtaining best results when optimizing both accuracy and classifier parameters simultaneously.
This document describes a multi-objective evolutionary algorithm that uses artificial neural networks to approximate fitness functions in order to reduce the number of exact function evaluations. The algorithm runs the evolutionary algorithm for an initial number of generations to collect a training dataset. It then trains a neural network on this dataset. The evolutionary algorithm continues running for additional generations, using the neural network to approximate some or all of the fitness function evaluations. The neural network approximation error is monitored, and the evolutionary algorithm switches back to using exact function evaluations when the error becomes too high. This process repeats until an acceptable Pareto front is found. The method was tested on benchmark multi-objective test functions and showed a 20-40% reduction in the number of exact function evaluations needed
1) O documento apresenta um estudo sobre a cisão de agregados metálicos.
2) A dissertação aplica vários modelos teóricos para descrever a física dos agregados, incluindo a Teoria dos Funcionais da Densidade, o Modelo da Geleia Estabilizada e o Modelo da Gota Líquida.
3) A cisão de agregados carregados é estudada usando o Modelo da Gota Líquida e o Método de Correção de Camadas para calcular barreiras de cisão e números críticos.
Pt redistributes inhomogeneously during Ni(Pt)Si formation. Real-time RBS shows Pt initially accumulates at the Ni2Si/Ni interface as a diffusion barrier, slowing silicidation kinetics. As NiSi seeds form, Pt incorporates in high concentrations, exceeding the initial Pt/Ni ratio. This influences NiSi texture development and stress behavior. Extended annealing shows Pt mobility in NiSi is low at temperatures up to 600°C, leaving the inhomogeneous distribution stable.
This document presents a study comparing several machine learning models for personal credit scoring: logistic regression, multilayer perceptron, support vector machine, AdaBoostM1, and Hidden Layer Learning Vector Quantization (HLVQ-C). The models were tested on datasets from a Portuguese bank. HLVQ-C achieved the highest accuracy and was the most useful model according to a proposed measure that considers earnings from denying bad credits and losses from denying good credits. While other models had higher error rates for good credits, HLVQ-C balanced accuracy and usefulness the best, making it suitable for commercial credit scoring applications.
Einstein passa tardes inteiras em cafés rabiscar papeis e pensar em física, ignorando as aulas. Um dia, discutindo a constância da velocidade da luz, Einstein tem uma ideia revolucionária: o tempo é relativo e espaço-tempo estão interligados. Anos mais tarde, Einstein finaliza sua teoria da relatividade, que liga espaço e tempo e revoluciona a física, porém poucos a compreendem devido à sua natureza revolucionária.
Manifold learning for bankruptcy predictionArmando Vieira
This document presents a method for bankruptcy prediction and analysis using manifold learning. Specifically, it applies the Isomap algorithm with class label information incorporated into the dissimilarity matrix (S-Isomap) on a real dataset of French companies. S-Isomap is shown to have comparable testing accuracy to other classifiers like SVM and better than KNN and RVM, while providing excellent lower-dimensional visualization with only 3 dimensions. The S-Isomap approach achieves separability of patterns from healthy to bankrupt firms in the embedded space. This preprocessing technique using manifold learning is a promising approach for bankruptcy prediction and analysis on high-dimensional financial data.
O autor descreve como a curiosidade natural das crianças é inibida pelo sistema educativo, transformando o ensino da ciência em algo abstrato e livresco em vez de prático e exploratório. Isto leva ao desinteresse dos alunos pela ciência e ao pequeno papel de Portugal na investigação científica. Defende que a educação deve estimular a curiosidade das crianças em vez de a reprimir.
Nesta carta, Forjaz expressa seu ceticismo sobre a natureza humana, acreditando que todos escondem instintos cruéis. Ele também critica a hipocrisia da sociedade, onde ninguém é verdadeiramente bom ou justo. Por fim, Forjaz prega que o dinheiro é o verdadeiro deus do mundo, capaz de comprar qualquer coisa ou pessoa.
This document outlines a proposal called "Democracy 2" which aims to define a new democratic model that is more citizen-centric and suited to today's society. It proposes moving beyond representative democracy by giving citizens a more direct role in important political decisions through information technology. The initiative will define the new model through contributions from citizens and experts across three streams focusing on political, social, and technology issues. It will also conduct a proof of concept trial of the new model at the local/regional level in multiple countries. The overall goal is to create a more open and representative democratic system.
Sairmais.com is a new tourism web portal that uses a recommendation system to provide personalized recommendations to users. It analyzes a user's social connections and preferences to filter vast amounts of tourism information and provide the most relevant options. The portal aims to be a one-stop platform for comprehensive geo-referenced tourism data. It incorporates review sharing and social networking features commonly seen on sites like Amazon, Facebook and TripAdvisor. Sairmais.com's recommendation system analyzes the relationships between users, items, and ratings to provide customized recommendations tailored to each individual user's interests. The system seeks to simplify the travel planning process and provide a more personal touch than other major tourism websites.
Sairmais.com is a new tourism web portal that uses a recommendation system to provide personalized recommendations to users. It analyzes a user's social connections and ratings of tourism items like hotels and restaurants to filter vast amounts of online tourism information and provide the most relevant options. The portal aims to be a one-stop platform for comprehensive geo-referenced tourism data. It incorporates social networking features allowing users to share experiences and opinions to improve recommendations for others. The system utilizes collaborative tagging and ratings within a user's social network to build profiles and predict their preferences, helping users more easily plan trips by finding the best options tailored specifically for them.
Seasonality effects on second hand cars salesArmando Vieira
This document analyzes seasonality effects on car sales using weekly aggregated car deal data from October 2012 to November 2014. It finds that:
1) A sudden drop in the last week's sales can be explained by statistical fluctuations based on the normal distribution of weekly deals over the period.
2) Months with the lowest deals (November and December) still show that last week's sales of 154 were a normal occurrence based on the mean and standard deviation for those months.
3) Google trends data for the keyword "used cars" shows a clear seasonality pattern of decreasing searches before the end of the year and increasing searches at the start and middle of the year.
1) A análise dimensional é uma ferramenta útil para estudar física sem necessidade de teorias complexas, fornecendo informações sobre problemas físicos.
2) O documento usa exemplos da física e biologia para mostrar como a análise dimensional pode revelar relações entre grandezas físicas.
3) A análise dimensional pode prever equações sem conhecimento completo da física subjacente aos fenômenos.
Optimization of digital marketing campaignsArmando Vieira
This document discusses using machine learning techniques to optimize digital marketing campaigns. Specifically, it analyzes data from campaigns using clustering, visualization and predictive models. Unsupervised learning methods like k-means clustering, PCA, MDS and SOM are used to identify patterns in large digital data. Supervised models like SVMs and random forests predict conversions. The goal is to extract actionable insights to improve ROI, engagement and sales through optimization of parameters like ad design, keywords, bids, channels and budget allocation.
We propose an algorithm for training Multi Layer Preceptrons for classification problems, that we named Hidden Layer Learning Vector Quantization (H-LVQ). It consists of applying Learning Vector Quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rurtherford
backscattering spectra and on a benchmark problem of image recognition. It may also be used for efficient feature extraction.
Boosting conversion rates on ecommerce using deep learning algorithmsArmando Vieira
This document summarizes an approach to use deep learning algorithms to predict the probability that online shoppers will purchase a product based on their website interactions. The approach involves using stacked auto-encoders to reduce the high dimensionality of the product interaction data before applying classification algorithms. Testing on various datasets showed that random forest outperformed logistic regression and that incorporating time data and more training examples improved prediction performance. Further work proposed applying stacked auto-encoders and deep belief networks to fully leverage the large amount of product interaction data.
Unfolding the Credit Card Fraud Detection Technique by Implementing SVM Algor...IRJET Journal
This document discusses using machine learning algorithms to detect credit card fraud. Specifically, it analyzes using a Support Vector Machine (SVM) algorithm. The document begins by introducing the authors and defining credit card fraud. It then provides background on challenges with detecting fraud and introduces the SVM technique. The remainder of the document discusses applying SVM to a credit card transaction dataset, comparing its performance to other algorithms like decision trees and random forests, and summarizing several related research papers on using machine learning for fraud detection.
Instance Selection and Optimization of Neural NetworksITIIIndustries
Credit scoring is an important tool in financial institutions, which can be used in credit granting decision. Credit applications are marked by credit scoring models and those with high marks will be treated as “good”, while those with low marks will be regarded as “bad”. As data mining technique develops, automatic credit scoring systems are warmly welcomed for their high efficiency and objective judgments. Many machine learning algorithms have been applied in training credit scoring models, and ANN is one of them with good performance. This paper presents a higher accuracy credit scoring model based on MLP neural networks trained with back propagation algorithm. Our work focuses on enhancing credit scoring models in three aspects: optimize data distribution in datasets using a new method called Average Random Choosing; compare effects of training-validation-test instances numbers; and find the most suitable number of hidden units. Another contribution of this paper is summarizing the tendency of scoring accuracy of models when the number of hidden units increases. The experiment results show that our methods can achieve high credit scoring accuracy with imbalanced datasets. Thus, credit granting decision can be made by data mining methods using MLP neural networks.
Synthetic feature generation to improve accuracy in prediction of credit limitsBOHRInternationalJou1
Financial institutions use various data mining algorithms to determine the credit limits for individuals using features
like age, education, employment, gender, income, and marital status. But, there is still a question of accurate
predictability, that is, how accurate can an institution be in predicting risk and granting credit levels. If an institution
grants too low of a credit limit/loan for an individual, then the institution may lose business to competitors, but
if the institution grants too high of a credit limit/loan, then the institution may lose money if that individual does
not repay the credit/loan. The novelty of this work is that it shows how to improve the accuracy in predicting
credit limits/loan amounts using synthetic feature generation. By creating secondary groupings and including
both the original binning and the synthetic bins, the classification accuracy and other statistical measures like
precision and ROC improved substantially. Hence, our research showed that without synthetic feature generation,
the classification rates were low, and the use of synthetic features greatly improved the classification accuracy and
other statistical measures.
The document discusses using machine learning techniques to predict loan repayment risk by analyzing loan applicant data. It aims to classify applicants as either high or low credit risk. Several machine learning algorithms are considered, including logistic regression, decision trees, random forests, SVM, naive Bayes and KNN. Python libraries like NumPy, Pandas, Scikit-learn and Matplotlib are used. The model could help banks and financial institutions better assess loan applications and reduce defaults. Future work involves comparing different algorithm predictions to identify the most accurate model.
This document discusses predicting loan defaults through machine learning models. It begins by introducing the business problem of banks suffering losses from customer loan defaults. It then describes preprocessing the loan dataset, which includes handling missing data, label encoding categorical variables, and balancing the dataset using SMOTE and SMOTEENN techniques. Logistic regression, decision trees, AdaBoost and random forest algorithms are applied to both the original and balanced datasets. The random forest model on the balanced data using SMOTEENN achieved the best accuracy of 92%. The model is then pickled and integrated into a web application using Flask for users to predict loan defaults.
This document discusses the application of meta-learning algorithms in banking sector data mining for fraud detection. It proposes using Classification and Regression Tree (CART), AdaBoost, LogitBoost, Bagging and Dagging algorithms for classification of banking transaction data. The experimental results show that Bagging algorithm has the best performance with the lowest misclassification rate, making it effective for banking fraud detection through data mining. Data mining can help banks detect patterns for applications like credit scoring, payment default prediction, fraud detection and risk management by analyzing customer transaction history and loan details.
Credit risk assessment with imbalanced data sets using SVMsIRJET Journal
This document discusses using support vector machines (SVMs) to assess credit risk with imbalanced data sets. SVMs have limited performance with imbalanced credit data where unpaid loans are less frequent than paid loans. The author develops an SVM model using two data resampling techniques - random oversampling and SMOTE - to address class imbalance. Performance is evaluated using various criteria like accuracy, sensitivity, specificity, and AUC. The results suggest resampling data can improve SVM performance for accurate credit risk prediction with imbalanced data.
LOAN APPROVAL PRDICTION SYSTEM USING MACHINE LEARNING.Souma Maiti
This document describes a machine learning model for predicting loan approvals. It discusses collecting loan application data and preprocessing the data which includes cleaning, feature selection, and scaling. Various machine learning algorithms are trained on the data including logistic regression, decision trees, random forest, support vector machines, and gradient boosting. Their accuracies are compared and random forest is found to perform best. The optimal model is deployed with a user interface created using Streamlit. The system aims to automate and improve the loan approval process for banks.
Credit risks are calculated based on the borrowers’ overall ability to repay. Our objective was to use optimization in order to create a tool that approves or rejects loans to borrowers. We also used optimization to establish how much interest rate/credit will be extended to borrowers who were approved for a loan.
IRJET- Improving Prediction of Potential Clients for Bank Term Deposits using...IRJET Journal
This document summarizes research on improving predictions of potential clients for bank term deposits using machine learning approaches. The researchers analyzed bank customer data using logistic regression, support vector machines, random forests, and XGBoost models. They found that XGBoost performed best with an area under the ROC curve of 0.7368, an F1 score of 0.9291, and test accuracy of 0.8351. The study aimed to identify the most effective predictive model that can be used in bank telemarketing campaigns to target potential clients.
The objective was to develop a neural network model to predict loan defaults using variables like number of derogatory reports, number of delinquent credit lines, debt-to-income ratio, age of oldest credit line, and loan divided by value. The best model had a training profit of $25.48 million and testing profit of $27.48 million, outperforming logistic regression. Key changes were reducing variables and lowering the probability cutoff, which increased profits and lift. The neural network model was simpler, more consistent and predictable than complex models, with training and testing profits varying less than $150,000.
Quant Foundry Labs - Low Probability DefaultsDavidkerrkelly
The Quant Foundry Labs division was approached to improve models for predicting low probability sovereign defaults. They developed a machine learning model that uses a large dataset of economic, financial, and governance indicators to predict sovereign credit ratings. The model was trained and tested on historical data, demonstrating improved accuracy over traditional statistical techniques. Explanatory tools also provide transparency into the model's predictions. The results represent an improvement in predicting low probability default events, which can help with regulatory requirements and risk management.
# Project 03 - Data Mining on Financial Data
In this project, various models like Logistic Regression, KNN, SVM, and Random Forest have been applied to three finance-related datasets in order to discover the insights from the datasets. The methods will be applied to the datasets in R studio and corresponding outputs will be shown in this paper. Then the applied methods will be compared with each other to identify how well the method is performing on each of the datasets and then finally the better method will be chosen for each of the datasets. The methods were explained in detail along with their advantages with the help of the information from the relevant papers. Every method which has been applied to the datasets has been confirmed whether it follows the data mining methodologies. Data mining methodologies like CRISP-DM, KDD and SEMMA will also be explained in detail via this paper. The process flow of the data mining methodologies will be explained and also will be made sure that whether the process flow has been followed while applying each method on the datasets. Data mining is now considered as a major factor in the risk management process of financial institutions. Even though various data mining tools are existing in the market, this paper allows readers to understand how the algorithm works on the dataset and how to justify whether an algorithm’s prediction.
This document summarizes and compares various machine learning models for credit scoring and investment decisions using explainable AI techniques. It finds that ensemble classifiers like random forests and neural networks outperform individual classifiers. LIME and SHAP techniques are used to explain ML credit scoring models. The study also develops new investment models using ML algorithms to maximize profit while minimizing risk. A variety of ML algorithms are tested, including logistic regression, decision trees, LDA, QDA, AdaBoost, random forests, and neural networks. The random forest and AdaBoost models are tuned with hyperparameters. Model performance is evaluated using metrics like accuracy, derived from a confusion matrix.
This document summarizes a student research project that developed a machine learning model to predict loan approval. The researchers tested various algorithms on a dataset of 615 loan applications and found that logistic regression performed best with an accuracy of 88.7%. They created a web application where users can enter loan application details and the model will predict approval. While the model considers many attributes, in reality a single strong attribute could also determine approval, which the system cannot account for.
This document describes an ensemble-based credit risk assessment system that uses multiple machine learning models to improve accuracy. It proposes a three-level architecture using unsupervised clustering, supervised classification with algorithms like logistic regression and random forests, and semi-supervised consensus voting. Testing on real data showed 93% accuracy, better predicting defaulters compared to current systems. The system aims to reduce credit risks and losses for financial institutions.
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
This document discusses using machine learning algorithms to predict household poverty levels. The goals are to build classification models to predict a household's poverty level as either "poor" or "non-poor" based on household attributes. Linear regression is proposed as the modeling algorithm. The document outlines collecting and preprocessing a household dataset, feature selection, model training and evaluation using metrics like MSE, RMSE and R-squared. References are provided on related work applying machine learning to poverty prediction using household surveys and satellite imagery.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A predictive system for detection of bankruptcy using machine learning techni...IJDKP
Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to
ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different
soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy
prediction system to categorize the companies based on extent of risk. The prediction system acts as a
decision support tool for detection of bankruptcy
Improving Insurance Risk Prediction with Generative Adversarial Networks (GANs)Armando Vieira
Generative adversarial networks (GANs) show promise for addressing data imbalance issues in insurance modeling. GANs were originally developed for computer vision tasks but have also been applied to tabular data. Conditional GANs and CycleGANs can generate synthetic minority class examples to balance datasets. In a case study on insurance fraud detection, GANs outperformed traditional resampling techniques like SMOTE in improving precision, recall, and F1-score. However, GANs require dense feature representations and consistency over time to be effective for tabular data imbalance problems.
Predicting online user behaviour using deep learning algorithmsArmando Vieira
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during the pre-train phase. They prove also to be more convenient to deal with severe class imbalance.
Visualizations of high dimensional data using R and ShinyArmando Vieira
This document discusses building interactive visualizations with Shiny and R to explore social and health care data from the UK. It describes using inputs like demographics, economic deprivation, and health metrics to create outputs like a health score and stress score. Visualizations were created with Shiny and Google Motion Charts to compare districts. The document concludes discussing using machine learning techniques like embeddings and exploring causality.
The document discusses GPU computing for machine learning. It notes that machine learning algorithms are computationally expensive and their requirements increase with data size. GPUs provide significant performance gains over CPUs for parallel problems like machine learning. Many machine learning algorithms have been implemented on GPUs, achieving speedups of 1-2 orders of magnitude. However, most GPU implementations are closed-source. Open-source implementations provide advantages like reproducibility and fair algorithm comparisons.
This document provides an overview of deep learning algorithms, including deep neural networks, convolutional neural networks, deep belief networks, and restricted Boltzmann machines. It discusses key concepts such as learning in deep neural networks, the evolution timeline of deep learning approaches, deep architectures, and restricted Boltzmann machines. It also covers training restricted Boltzmann machines using contrastive divergence, constructing deep belief networks by stacking restricted Boltzmann machines, and practical considerations for pre-training and fine-tuning deep belief networks.
Extracting Knowledge from Pydata London 2015Armando Vieira
The document discusses using deep learning techniques like word embeddings to jointly embed text and knowledge graphs for information extraction purposes. Word embeddings represent words as vectors in a way that captures semantic meaning, allowing related words to have similar embeddings. Knowledge graphs explicitly represent entities and relations. The document proposes combining text corpora with knowledge graphs by training a model on both to generate embeddings that incorporate information from both sources. This allows extracting knowledge expressed in text and transforming it into a machine-readable format.
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
Neural Networks and Genetic Algorithms Multiobjective accelerationArmando Vieira
by Neural
Network
- The document proposes a hybrid multi-objective evolutionary algorithm that uses an artificial neural network to reduce the number of objective function evaluations needed. It combines a multi-objective evolutionary algorithm (MOEA) with an artificial neural network (ANN) to approximate solutions. The ANN is trained on solutions evaluated by the MOEA and then used to estimate fitness for unevaluated solutions to further guide the search. This approach aims to improve optimization efficiency over existing MOEAs for problems with computationally expensive objective functions.
Credit risk with neural networks bankruptcy prediction machine learningArmando Vieira
The document discusses credit risk management with AI tools. It summarizes that credit scoring is used to statistically quantify risk by converting applicant information into numbers and a score. The objective is to forecast future performance based on past client behavior. It then discusses using various machine learning models like HLVQ-C and neural networks to predict financial distress, classify companies, and improve bankruptcy prediction. The models were tested on real world credit and financial datasets.
Artificial neural networks for ion beam analysisArmando Vieira
The document discusses using artificial neural networks (ANNs) for ion beam analysis. Specifically, it discusses:
1) Using ANNs to analyze Rutherford backscattering spectroscopy (RBS) data in an automated way, by recognizing patterns in the data related to sample properties without explicit knowledge of causes.
2) Training ANNs on datasets of RBS spectra with known sample parameters to allow the ANNs to relate spectral features to things like layer thickness, composition, and depth.
3) The potential for ANNs to enable real-time automated analysis and optimization of ion beam experiments.
The document discusses artificial intelligence and pattern recognition. It introduces various pattern recognition concepts including defining a pattern, examples of patterns in different domains, and approaches to pattern recognition. It also provides an example of using discriminative methods to classify fish into salmon and sea bass using optical sensing and extracted features.
This document provides summaries of key financial ratios for analyzing operational performance, value creation, financial liquidity, and risk-return tradeoffs. It defines ratios such as ROCE, margin, capital turnover, WACC, WC, WCR, D/E, debt-pay-years, and TIE. It explains how these ratios are used to evaluate a company's pricing strategy, capital intensity, value creation, liquidity position, and risk-return profile. Graphs illustrate the relationships between these metrics and identify zones of balanced/unbalanced liquidity or value creation/destruction.
The document discusses models for influence maximization on social networks when negative opinions may emerge and spread. It presents an extension of the independent cascade model to account for both positive and negative influences. When a node is activated, it can activate neighbors positively with some probability or negatively with certainty. The objective is to maximize the expected number of positively activated nodes. While this model leads to a submodular objective function, more general models that account for node-specific quality factors or propagation delays are not necessarily submodular.
This document describes MyRec, a recommendation system developed by Armando Vieira and Tiago Simas. MyRec uses proximity networks to provide personalized recommendations for tourism planning, online shopping, and targeted advertising. It can recommend less known, more specific products and scale to large datasets. MyRec has already been implemented on Addega.com for wine recommendations and at Los Alamos National Laboratory. The founders are looking to license MyRec as a software as a service and have potential clients in targeted advertising, tourism, and marketing.
Manifold learning for credit risk assessment Armando Vieira
The document outlines a paper on using manifold learning techniques for credit risk analysis. It discusses motivations for dimensionality reduction and bankruptcy prediction. The proposed approach uses Isomap and Supervised Isomap for nonlinear dimensionality reduction to better analyze financial data and predict credit risk. Experimental results are presented on a data set to evaluate the methodology. Conclusions and potential future work are discussed.
This document discusses using artificial neural networks to classify protein loops based on amino acid sequence. It provides background on protein structure, outlines challenges in protein structure prediction, and describes how neural networks like Hidden Layer Vector Quantization can be used to classify different types of protein loops from sequence alone with reasonable accuracy. The document concludes by discussing future work, including improved amino acid coding schemes and exploring protein structure information beyond multiple sequence alignments.
Credit risk management tools are evolving with AI. Traditional methods focused on factors like customer payment history, market changes, and regulatory compliance. Newer AI tools analyze large datasets with thousands of applicants and financial ratios to more accurately predict future customer performance and risk of default. One system analyzed a French company bankruptcy database with 30 financial ratios to classify companies as high or low risk of bankruptcy with over 84% accuracy.
1. Improving Personal Credit Scoring with HLVQ-C
A. S. Vieira1, João Duarte1, B. Ribeiro2 and J. C. Neves3
1
ISEP, Rua de S. Tomé, 4200 Porto, Portugal
2
Department of Informatics Engineering, University of Coimbra, P-3030-290
Coimbra, Portugal
3
ISEG - School of Economics, Rua Miguel Lupi 20, 1249-078 Lisboa, Portugal
{asv@isep.ipp.pt, bribeiro@dei.uc.pt, jcneves@iseg.utl.pt}
Abstract. In this paper we study personal credit scoring using several machine
learning algorithms: Multilayer Perceptron, Logistic Regression, Support
Vector Machines, AddaboostM1 and Hidden Layer Learning Vector
Quantization. The scoring models were tested on a large dataset from a
Portuguese bank. Results are benchmarked against traditional methods under
consideration for commercial applications. A measure of the usefulness of a
scoring model is presented and we show that HLVQ-C is the most accurate
model.
Keywords: Credit Scoring, Neural Networks, Classification, Hidden Layer
Learning Vector Quantization.
1 Introduction
Quantitative credit scoring models have been developed for the credit granting
decision in order to classify applications as ‘good’ or `bad’, the latest being loosely
defined as a group with a high likelihood of defaulting on the financial obligation.
It is very important to have accurate models to identify bad performers. Even a
small fraction increase in credit scoring accuracy is important. Linear discriminant
analysis still is the model traditionally used for credit scoring. However, with the
growth of the credit industry and the large loan portfolios under management, more
accurate credit scoring models are being actively investigated [1]. This effort is
mainly oriented towards nonparametric statistical methods, classification trees, and
neural network technology for credit scoring applications [1-5].
The purpose of this work is to investigate the accuracy of several machine learning
models for the credit scoring applications and to benchmark their performance against
the models currently under investigation.
The credit industry has experienced a rapid growth with significant increases in
instalment credit, single-family mortgages, auto-financing, and credit card debt.
Credit scoring models, i.e, rating of the client ability to pay the loans, are widely used
by the financial industry to improve cashflow and credit collections. The advantages
of credit scoring include reducing the cost of credit analysis, enabling faster credit
decisions, closer monitoring of existing accounts, and prioritizing collections [4].
2. Personal credit scoring is used by banks for approval of home loans, to set credit
limits on credit cards and for other personal expenses. However, with the growth in
financial services there have been mounting losses from delinquent loans. For
instance, the recent crises in the financial system triggered by sub-prime mortgages
have caused losses of several billion dollars.
In response, many organizations in the credit industry are developing new models
to support the personal credit decision. The objective of these new credit scoring
models is increasing accuracy, which means more creditworthy applicants are granted
credit thereby increasing profits; non-creditworthy applicants are denied credit thus
decreasing losses.
The main research focuses on two areas: prediction of firm insolvency and
prediction of individual credit risk. However, due to the proprietary nature of credit
scoring, there is few research reporting the performance of commercial credit scoring
applications.
Salchenberger et al. investigate the use of a multilayer perceptron neural network
to predict the financial health of savings and loans [6]. The authors compare a
multilayer perceptron neural network with a logistic regression model for a data set of
3429 S&L's from January 1986 to December 1987. They find that the neural network
model performs as well as or better than the logistic regression model for each data
set examined.
The use of decision trees and multilayer perceptrons neural network for personal
credit scoring were studied by several authors. West tested several neural networks
architectures on two personal credit datasets, German and Australian. Results
indicates that multilayer perceptron neural network and the decision tree model both
have a comparable level of accuracy while being only marginally superior to tradition
parametric methods [7].
Jensen [5] develops a multilayer perceptron neural network for credit scoring with
three outcomes: obligation charged of (11.2%), obligation delinquent (9.6%), and
obligation paid-of. Jensen reports a correct classification result of 76 - 80% with a
false positive rate (bad credit risk classified as good credit) of 16% and a false
negative rate (good credit risk classified as bad credit) of 4%. Jensen concludes that
the neural network has potential for credit scoring applications, but its results were
obtained on only 50 examples.
The research available on predicting financial distress, whether conducted at the
firm or individual level suggests that recent non-parametric models show potential yet
lack an overwhelming advantage over classical statistical techniques. Recently we
have successfully applied new data mining models like Hidden Layer Learning
Vector Quantization (HLVQ-C) [8] and Support Vector Machines (SVM) [9] for
bankruptcy prediction where they clear outperformed linear methods. However, the
major drawback for using these models is that they are difficult to understand and the
decisions cannot be explicitly discriminated.
This paper is organized as follows. Section 2 discusses the dataset used, the pre-
processing of the data and feature selection. Section 3 presents the models and the
usefulness measure. In Section 4 the results are discussed and finally section 5
presents the conclusions.
3. 2 Dataset
The database contains about 400 000 entries of costumers who have solicited a
personal credit to the bank. The valued solicited ranges from 5 to 40 kEuros and the
payment period varies between 12 to 72 months.
Table 1 presents the definitions of the eighteen attributes used by the bank. Eight
of these attributes are categorical (1, 2, 3, 4, 5, 8, 9 and 10) and the remaining
continuous. Most of the entries in the database have missing values for several
attributes. To create a useful training set we select only entries without missing
values.
The database also contains the number of days that each client is in default to the
bank concerning the payment of the monthly mortgage – in most cases this number is
zero. We consider a client with bad credit when this number is greater than 30 days.
We found 953 examples in the database within this category. To create a balanced
dataset an equal number of randomly selected non-default examples were selected,
reaching a total of 1906 training cases. We call this dataset 1.
We also created a second dataset where the definition of bad credit was set to 45
days of delay. This dataset is therefore more unbalanced containing 18% of defaults
and 82% non-defaults. This is called dataset 2.
Table 1: Attributes used for credit scoring. Marked bold are the selected attributes.
# Designation # Designation
1 Professional activity 10 Nationality
2 Previous professional activity 11 Debt capacity
3 Zip code 12 Annual costs
4 Zip code – first two digits 13 Total income
5 Marital status 14 Other income
6 Age 15 Effort ratio
7 Number of dependents 16 Future effort ratio
8 Have home phone 17 Number of instalments
9 Residential type 18 Loan solicited
2.1 Feature selection
Several feature selection algorithms were used to exclude useless attributes and
reduce the complexity of the classifier. Due to the presence of many categorical
attributes, feature selection is difficult. Several methods were used to test the
consistency of the selection: SVM Attribute Evaluation, Chisquared and GainRatio.
Each method selected slightly different sets of attributes. We choose the following set
of six attributes with the highest consensus among all rankers: 1, 3, 4, 11, 17 and 18.
4. 3. Models used
The data was analysed with five machine learning algorithms: Logistic, Multilayer
Perceptron (MLP), Support Vector Machine (SVM), AdaBoostM1 and Hidden Layer
Learning Vector Quantization (HLVQ-C).
For MLP, we used a neural network with a single hidden layer with 4 neurons. The
learning rate was set to 0.3 and the momentum to 0.2. The SVM algorithm used was
the LibSVM [12] library with a radial basis function as kernel with the cost parameter
C = 1 and the shrinking heuristic. For AdaBoostM1 algorithm we used a Decision
Stump as weak-learner and set the number of iterations to 100. No resampling was
used. The HLVQ-C algorithm implementation is described elsewhere [8].
3.1 Usefulness of a classifier
Accuracy is a good indicator, but not the only criteria, to choose the apropriate
classifier. We introduce a measure of the usefulness of a classifier, defined by:
η = E − L,
where E is the earnings obtained by the use of the classifier and L the losses incurred
due to the inevitable misclassifications.
Earning, for the bank point of view, results from refusing credit to defaults clients,
and can be expressed as:
E = NV (1 − eI ) x
where N is the number of loans applicants, V the average value of a loan, e I the type
I error and x the typical percentage of defaults in the real sample. For simplicity we
are assuming a Loss Given Default (LGD) of 100%.
Losses results from excluding clients that were incorrectly classified as defaults. In
a simplified way they can be calculated as:
L = mNV (1 − x)eII
where m is the average margin typically obtained by the bank in a loan. The net gain
in using a classifier is:
η = NV [x(1 − eI ) − (1 − x)eII m] .
To have η > 0 we need:
x
> mG ,
1− x
5. eII
where G= , is a measure of the efficiency of the classifier. This quantity
1 − eI
should be the lowest possible. Assuming x small and eI = 0.5 , we should have
x > 2meII .
4. Results
In table 2 we compare the efficiency of the classifiers on two datasets using 10-fold
cross validation. For dataset 1, most classifiers achieve a good accuracy in detecting
defaults but at the cost of large type II errors. Since real data is highly unbalanced,
most cases being non-defaults, this means that more than half of clients will be
rejected. SVM is the most balanced classifier while HLVQ-C achieved the highest
accuracy on both datasets.
Since dataset 2 is more unbalanced and the default definition more strict error type
II decreased considerably while error type I increased. More important, the usefulness
of the classifier, measured by G, improved substantially. The HLVQ-C is again the
best performer, either on accuracy and usefulness, and AdaboostM1 the second best.
Logistic is the worst performer.
Following our definition, for the classifier to be useful the dataset has to have
about 6% defaults, considering the best model (HLVQ-C), and as much as 11% for
the Logistic case (setting m = 0.5).
To increase the usefulness, i.e. lower G, error type II should decrease without
deteriorating error type I. This can be done either by using a more unbalanced dataset
or applying different weights for each class. The exact proportion of instances in each
class in the dataset can be adjusted in order to minimize G.
Table 2. Accuracy, error types and usefulness of different models in the two datasets
considered.
Classifier Accuracy Type I Type II G
Logistic 66.3 27.3 40.1 54.8
MLP 67.5 8.1 57.1 61.1
Dataset 1 SVM 64.9 35.6 34.6 52.3
AdaboostM1 69.0 12.6 49.4 55.7
HLVQ-C 72.6 5.3 49.5 52.3
Logistic 81.2 48.2 11.0 21.2
MLP 82.3 57.4 9.1 20.1
Dataset 2 SVM 83.3 38.1 12.4 19.3
AdaboostM1 84.1 45.7 8.0 14.7
HLVQ-C 86.5 48.3 6.2 11.9
6. 5 Conclusions
In this work we compared the efficiency of several machine learning algorithms for
credit scoring. Feature selection was used to reduce the complexity and eliminate
useless attributes. From the initial set of 18 features only 6 have been selected.
While MLP slightly improves the accuracy of Logistic regression, other methods
show considerable gains. AdaboostM1 boosts its accuracy by 3% and HLVQ-C up to
5%.
The price to be paid for the accurate detection of defaults is a high rate of false
positives. To circumvent this situation an unbalanced dataset was used with a more
strict definition of default. A measure of the usefulness of the classifier was
introduced and we showed that it improves considerably on this second dataset.
References
1. Brill J. The importance of credit scoring models in improving cashflow and collections.
Business Credit 7, 1 (1998)
2. Tam KY, Kiang MY. Managerial applications of neural networks: the case of bank failure
predictions. Management Science 47, 926 (1992).
3. Davis RH, Edelman DB, Gammerman AJ. Machine learning algorithms for credit-card
applications. IMA Journal of Mathematics Applied in Business and Industry 51, 43 (1992).
4. Desai VS, Crook JN, Overstreet GA. A comparison of neural networks and linear scoring
models in the credit union environment. European Journal of Operational Research 37, 24
(1996).
5. Jensen HL. Using neural networks for credit scoring. Managerial Finance 26, 18 (1992).
6. Salchenberger LM, Cinar EM, Lash NA. Neural networks: a new tool for predicting thrift
failures. Decision Sciences 23, 899 (1992).
7. West D, Neural Network credit scoring models, Computers & Operations Research 27, 1131
(2000).
8. A. S. Vieira and J. C. Neves, “Improving Bankruptcy Prediction with Hidden Layer Learning
Vector Quantization”, Eur. Account. Rev. 15 (2), 253-275. (2006)
9. B. Ribeiro, A. Vieira and J. C. Neves: Sparse Bayesian Models: Bankruptcy-Predictors of
Choice?, Int. J. Conf. Neural Networks, Vancouver, Canada 3377-3381 (2006).