scholarly journals Identification of Default Payments of Credit Card Clients using Boosting Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 4990-4994
Keyword(s):  
Credit Card ◽  
Credit Rating ◽  
Learning Algorithm ◽  
Credit Scoring ◽  
Screening Method ◽  
Main Idea ◽  
Customer Data ◽  
Huge Data ◽  
Credit Default ◽  
History Of

Understanding the history of clients will act as a valuable screening method for banks by providing information that can categorize clients as defaulters on a loan. Customer credit rating is a grade process where the consumer is categorized by the grade. Credit scoring model used to ascertain credit risk from new and existing customer. Credit rating is an assessment used to measure the creditworthiness of the customer. For the huge customers related dataset we can use various classification techniques used in the field of data mining. The main idea is by analyzing the customer data and by combining machine-learning algorithm to identify the default credit card user. Default is a keyword, used for predicting the customer who cant repay the amount on time. Predicting future credit default accounts in advance is highly tedious task. Modern statistical techniques are usually unable to manage huge data. The proposed work focus mainly on ensemble learning and other artificial intelligence technique.

Beyond Classification

2008 ◽  
pp. 1855-1876
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

Beyond Classification

2011 ◽  
pp. 139-161
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

Modelling Credit Default in Microfinance—An Indian Case Study

2017 ◽  
Vol 16 (3) ◽  
pp. 246-258 ◽  
Author(s):  
P. K. Viswanathan ◽  
S. K. Shanthi
Keyword(s):  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Relative Importance ◽  
Microfinance Institutions ◽  
Scoring Method ◽  
Credit Score ◽  
Credit Default ◽  
Key Drivers

Credit score models have been successfully applied in a traditional credit card industry and by mortgage firms to determine defaulting customer from the non-defaulting customer. In the light of growing competition in the microfinance industry, over-indebtedness and other factors, the industry has come under increased regulatory supervision. Our study provides evidence from a large microfinance institutions (MFI) in India, and we have applied both the credit scoring method and neural network (NN) method and compared the results. In this article, we demonstrate the capability of credit scoring models for an Indian-based microfinance firm in terms of predicting default probability as well the relative importance of each of its associated drivers. A logistic regression model and NN have been used as the predictive analytic tools for sifting the key drivers of default.

Data Mining for Credit Scoring

2008 ◽  
pp. 2449-2463
Author(s):  
Indranil Bose ◽  
Cheng Pui Kan ◽  
Chi King Tsz ◽  
Lau Wai Ki ◽  
Wong Cho Hung
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Probability Estimation ◽  
Financial Industry ◽  
Future Developments ◽  
History Of ◽  
The Relationship ◽  
Major Data

Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

Data Mining for Credit Scoring

2008 ◽  
pp. 309-323 ◽  
Author(s):  
Indranil Bose ◽  
Cheng Pui Kan ◽  
Chi King Tsz ◽  
Lau Wai Ki ◽  
Wong Cho Hung
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Scoring ◽  
Default Probability ◽  
Probability Estimation ◽  
Financial Industry ◽  
Future Developments ◽  
History Of ◽  
The Relationship ◽  
Major Data

Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

scholarly journals USING MACHINE LEARNING METHODS TO ASSESS RISKS WHEN IMPLEMENTING A NEW CREDIT PRODUCT

2020 ◽  
pp. 59-63
Author(s):  
Сергей Петрович Бобков ◽  
Станислав Вадимович Суворов ◽  
Артем Игоревич Орлов ◽  
Егор Алексеевич Пивнев
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Credit Scoring ◽  
Main Idea ◽  
Rating System ◽  
Credit Institution ◽  
Customer Data ◽  
Machine Learning Methods ◽  
Customer Segment ◽  
Mathematical Data Processing

The article discusses the issues of assessing the creditworthiness of individuals using credit scoring. This rating system is an effective approach to determining the level of risk for a specific customer segment. This is especially true of the situation when a credit institution launches a new credit product. The main idea proposed in the article is that new customer scoring cards are created on the basis of existing cards by mathematical data processing. The novelty of the method lies in the fact that the scoring is done based on a dedicated subset of customer data stored in the corporate storage. The approach helps to make a decision on granting a loan and can be recommended for use in lending 

scholarly journals Genetic Analysis of a Cohort of 275 Patients with Hyper-IgE Syndromes and/or Chronic Mucocutaneous Candidiasis

2021 ◽  
Author(s):  
Natalie Frede ◽  
Jessica Rojas-Restrepo ◽  
Andrés Caballero Garcia de Oteyza ◽  
Mary Buchta ◽  
Katrin Hübscher ◽  
...  
Keyword(s):  
Screening Method ◽  
Cost Effective ◽  
Illumina Miseq ◽  
Clinical Findings ◽  
Genetic Defects ◽  
Chronic Mucocutaneous Candidiasis ◽  
Mucocutaneous Candidiasis ◽  
Serious Infections ◽  
History Of ◽  
Panel Sequencing

AbstractHyper-IgE syndromes and chronic mucocutaneous candidiasis constitute rare primary immunodeficiency syndromes with an overlapping clinical phenotype. In recent years, a growing number of underlying genetic defects have been identified. To characterize the underlying genetic defects in a large international cohort of 275 patients, of whom 211 had been clinically diagnosed with hyper-IgE syndrome and 64 with chronic mucocutaneous candidiasis, targeted panel sequencing was performed, relying on Agilent HaloPlex and Illumina MiSeq technologies. The targeted panel sequencing approach allowed us to identify 87 (32 novel and 55 previously described) mutations in 78 patients, which generated a diagnostic success rate of 28.4%. Specifically, mutations in DOCK8 (26 patients), STAT3 (21), STAT1 (15), CARD9 (6), AIRE (3), IL17RA (2), SPINK5 (3), ZNF341 (2), CARMIL2/RLTPR (1), IL12RB1 (1), and WAS (1) have been detected. The most common clinical findings in this cohort were elevated IgE (81.5%), eczema (71.7%), and eosinophilia (62.9%). Regarding infections, 54.7% of patients had a history of radiologically proven pneumonia, and 28.3% have had other serious infections. History of fungal infection was noted in 53% of cases and skin abscesses in 52.9%. Skeletal or dental abnormalities were observed in 46.2% of patients with a characteristic face being the most commonly reported feature (23.1%), followed by retained primary teeth in 18.9% of patients. Targeted panel sequencing provides a cost-effective first-line genetic screening method which allows for the identification of mutations also in patients with atypical clinical presentations and should be routinely implemented in referral centers.

Why Did Credit Card Balances Decline so Much during the COVID-19 Pandemic

FEDS Notes ◽  
2021 ◽  
Vol 2021 (3025) ◽  
Author(s):  
Robert M. Adams ◽  
◽  
Vitaly M. Bord ◽  
Bradley Katcher ◽  
◽  
...  
Keyword(s):  
United States ◽  
Credit Card ◽  
Consumer Credit ◽  
The United States ◽  
History Of

Consumer credit card balances in the United States experienced unprecedented declines during the COVID-19 pandemic. According to the G.19 Consumer Credit statistical release, revolving consumer credit fell more than $120 billion (11 percent) in 2020, the largest decline in both nominal and percentage terms in the history of the series.

scholarly journals Credit Card Fraud Detection Using Autoencoder Model in Unbalanced Datasets

2019 ◽  
pp. 1-16 ◽  
Author(s):  
M. A. Al-Shabi
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Efficient Algorithm ◽  
Credit Card ◽  
Learning Algorithm ◽  
Credit Cards ◽  
Reconstruction Error ◽  
Insurance Companies ◽  
Deep Learning Algorithm ◽  
Unbalanced Dataset

Fraudulent credit card transaction is still one of problems that face the companies and banks sectors; it causes them to lose billions of dollars every year. The design of efficient algorithm is one of the most important challenges in this area. This paper aims to propose an efficient approach that automatic detects fraud credit card related to insurance companies using deep learning algorithm called Autoencoders. The effectiveness of the proposed method has been proved in identifying fraud in actual data from transactions made by credit cards in September 2013 by European cardholders. In addition, a solution for data unbalancing is provided in this paper, which affects most current algorithms. The suggested solution relies on training for the autoencoder for the reconstruction normal data. Anomalies are detected by defining a reconstruction error threshold and considering the cases with a superior threshold as anomalies. The algorithm's performance was able to detected fraudulent transactions between 64% at the threshold = 5, 79% at the threshold = 3 and 91% at threshold= 0.7, it is better in performance compare with logistic regression 57% in unbalanced dataset.

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