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.
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
A Survey of Online Credit Card Fraud Detection using Data Mining TechniquesIJSRD
Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.
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.
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
A Survey of Online Credit Card Fraud Detection using Data Mining TechniquesIJSRD
Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.
The credit card has become the most popular mode of payment for both online as well as
regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing
day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that
they generate new ways for committing fraudulent transactions each day which demands constant
innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy
logic, neural network, logistic regression, naïve Bayesian, Machine learning, Sequence Alignment,
decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in
detecting various credit card fraudulent transactions. This paper presents a survey of various techniques
used in credit card fraud detection mechanisms.
A rule-based machine learning model for financial fraud detectionIJECEIAES
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
MACHINE LEARNING ALGORITHMS FOR CREDIT CARD FRAUD DETECTIONmlaij
Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as ecommerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due
to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62
billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend
to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can
be generalized to other fraud types. This paper compares the performance of logistic regression, decision
trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines
(SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature
of the data and compared the performance of the supervised models on the oversampled data to the raw
data. From the results, the Random Forest classifier outperformed the other models with a higher AUC
score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change
the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score
but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest
performance.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Dive into the intricate world of fraud detection with this comprehensive presentation featuring an unique student project. Explore the project's objectives, methodologies, and innovative solutions developed to combat fraudulent activities within financial transactions. From data analysis to model implementation, witness the journey our student has undertaken to create a robust fraud detection system. Whether you're a fellow student, industry professional, or enthusiast, this showcase provides valuable insights into the challenges and advancements in fraud detection technology. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
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.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
The credit card has become the most popular mode of payment for both online as well as
regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing
day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that
they generate new ways for committing fraudulent transactions each day which demands constant
innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy
logic, neural network, logistic regression, naïve Bayesian, Machine learning, Sequence Alignment,
decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in
detecting various credit card fraudulent transactions. This paper presents a survey of various techniques
used in credit card fraud detection mechanisms.
A rule-based machine learning model for financial fraud detectionIJECEIAES
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
MACHINE LEARNING ALGORITHMS FOR CREDIT CARD FRAUD DETECTIONmlaij
Fraud is a critical issue in our society today. Losses due to payment fraud are on the increase as ecommerce keeps evolving. Organizations, governments, and individuals have experienced huge losses due
to payment. Merchant Savvy projects that global losses due to payment fraud will increase to about $40.62
billion in 2027 . Among all payment fraud, credit card fraud results in a higher loss. Therefore, we intend
to leverage the potential of machine learning to deal with the problem of fraud in credit cards which can
be generalized to other fraud types. This paper compares the performance of logistic regression, decision
trees, random forest classifier, isolation forest, local outlier factor, and one-class support vector machines
(SVM) based on their AUC and F1-score. We applied a smote technique to handle the imbalanced nature
of the data and compared the performance of the supervised models on the oversampled data to the raw
data. From the results, the Random Forest classifier outperformed the other models with a higher AUC
score and better f1-score on both the actual and oversampled data. Oversampling the data didn't change
the result of the decision trees. One-class SVM performs better than isolation forest in terms of AUC score
but has a very low f1-score compared to isolation forest. The local outlier factor had the poorest
performance.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal,
Dive into the intricate world of fraud detection with this comprehensive presentation featuring an unique student project. Explore the project's objectives, methodologies, and innovative solutions developed to combat fraudulent activities within financial transactions. From data analysis to model implementation, witness the journey our student has undertaken to create a robust fraud detection system. Whether you're a fellow student, industry professional, or enthusiast, this showcase provides valuable insights into the challenges and advancements in fraud detection technology. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
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.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
Eureka, I found it! - Special Libraries Association 2021 Presentation
Tanvi_Sharma_Shruti_Garg_pre.pdf.pdf
1. HARISH SHARMA SIR TANVI SHARMA (20/402)
SHRUTI GARG (20/399)
Financial Fraud Detection in Healthcare
Using Machine And Deep Learning
Submitted To :- Submitted by:-
Subject:- Recent Topics
4. Introduction
The system for detecting the fraud might be composed of
a manual process and the expertise algorithm for
detecting the fraud automatically. The automatic
operation can be based upon all previous ways of fraud
transactions happened.Detection of fraud is the process of
analyzing the behavior of card holders’ transactions to
know whether the conducted transaction is genuine.
5. Different frauds in a credit card can be categorized
as fraud in external card or inner card. Fraud in
inner card happens due to commitment of false
identity between the bank and the cardholders,
and fraud in external card includes the usage of a
stolen credit card to withdraw the cash by dubious
means.
Credit card fraud detection is associated with many
challenges, such as dynamic or the fraudulent
behavior of credit cardholders. Such kinds of activities
can be identified using the popular technology called
artificial intelligence through machine learning and
deep learning algorithms , such as KNN , random
forest , decision tree , Logistic Regressionj , netural
networks.
6. Credit Card Fraud
The most popular algorithm for detecting frauds in credit
is inspired by nature.
In behavior frauds, the criminal thieves the detail related to the
account and the password related to that account and uses that for
with-drawing the money. Credit card fraud is more accessible as
more money can be earned with less amount of risk in less
duration of time.
Application fraud
it relates to the criminal who owns a credit card from different
issuing companies by spreading false data related to the
cardholder .
Behavior fraud
7. The sequence pattern of credit card transactions
mainly relates to the Hidden Markov Model
(HMM), which identifies the effectiveness based
on credit card fraud.
They evaluate the data with various techniques
such as Random Forest, Logistic Regression, and
Support Vector Machine to predict the different
frauds related to the credit card with the
aggregation technique. However, this aggregation
method fails to detect the real-time fraud that
happens in the transaction with the credit card
8. Awoyemi et al. in 2017 [1] investigated severely distorted credit
card fraudulent information; this research analyzes the
efficiencies of various methodologies such as Naive Bayes, KNN,
and Logistic Regression. Credit card transaction information-
based data including 284,807 transactions were gathered from
European customers. On the distorted information, a combination
strategy of undersampling and oversampling is used. The original
and preprocessed data are subjected to three procedures. Python
is used to carry out the task. The findings reveal that Naive Bayes,
K-Nearest Neighbor, and Logistic Regression classifiers have an
optimum accuracy of 97.92%, 97.69%, and 54.86%, respectively.
KNN outperforms Naive Bayes and Logistic Regression methods,
according to the comparison findings.
Related Work
Research Paper 1:-
9. Dal Pozzolo et al. in 2017 [2] proposed three key
contributions. First, with the aid of their research
assistance, the authors offer a formalization of the fraud-
identification issue that accurately reflects the working
circumstances of FDSs that monitor enormous flows of
credit card transactions daily. The authors also showed how
to utilize the most relevant evaluation metrics for fraud
identification. Second, the authors devised and tested a
unique learning approach for dealing with class imbalance,
idea drift, and verification delay. Third, the authors
illustrated the influence of class imbalance and idea drift in
a real-world information stream with more than 75 million
transactions permitted over three years in their studies. To
train the behavior characteristics of regular and anomalous
transactions, two types of random forests are employed
Research Paper 2:-
10. Feature Selection
Payment via credit card has become more
common in both online and offline settings.
As result, the rate of fraud increases, resulting in massive losses
for financial and e-commerce companies. Traditional fraud
detection takes a long time; thus, some artificial intelligence
models were required for detecting and tracking down credit
card fraud.
These intelligence techniques include many techniques based on
computational intelligence. The supervised and unsupervised
learning methods are used in the fraud detection system
11. .
The supervised technique of fraud detection relies on the transaction based
on fraudulent and legitimate and then newly occurred transaction
classified based on the learned model, whereas in
SUPERVISED LEARNING
UNSUPERVISED LEARNING:-
an unsupervised model of fraud detection, the transactions that lie in
outliers are the mainly considered transactions related to the fraud.
For fraud detection, algorithms such
as backpropagation of error signals
with forward and backward passes are
used.
12. Experimental Setup and Methods
This section explains how a dataset and various deep learning and machine
learning classifiers, including Logistic Regression, Naive Bayes, Decision Tree,
KNN, and the Sequential Model, were used in the experiment.
Preprocessed data are fed into the classifier algorithm during the training phase.
The accuracy of identifying credit card fraud is later determined by evaluating the
test data. Finally, accuracy and best performance are evaluated for each of the
various models.
To determine which model performs better in the real-time scenario, a subset of
the legal ratio with the total number of fraud transactions is used.
Before creating the classifier, all of these algorithms go through various stages
such as data collection, data preprocessing, data analysis, data training with
various classifiers, and later data testing. Preprocessing involves converting all
of the data into a format that can be used. Using two different data distribution
sets, the hybrid undersampling (negative class) and oversampling (positive
class) techniques were applied.
13. The source of the dataset is the UCI Machine Learning Repository.
The dataset holds the information related transaction conducted
through credit cards as a default payment gateway of the different
customers in Taiwan.
Dataset :
The sequential model generates its sequential value by estimating the
input values for the series which can be time-series data. It is easier to
train the dataset through a sequential model as it requires minimum
computation complexity and generates a better result.
Naive Bayes is the statistical method that relies on Bayesian theory,
where the result is obtained based on the highest probability. It
estimates the probability of the unknown value based upon the known
value. The logic and prior knowledge can be applied to predict
unknown
probability.
Sequential
Model :
Naive Bayes
Classifier :
14. KNN classifier is an example of a learning approach
where classification is done based on the Manhattan
or Euclidean and Minkowski distance function-
calculated measure of similarity. While the
Minkowski function primarily deals with categorical
data, the Manhattan or Euclidean function primarily
deals with continuous variables.
.
Logistic
Regression
K-Nearest
Neighbou
r
A useful method for calculating the probability of binary
classes based on
one or more features is logistic regression. It generates
the sigmoid
nonlinear function's ideal parameter
17. RESULT:-
evaluating all these classifier models, training is conducted
using 70% of the entire dataset, while for testing and
validating, 30% of the dataset is used. Accuracy, specificity,
sensitivity, precision, and the Matthews correlation
coefficient (MCC) with the rate of balance classification are
applied for measuring the performance of all these
classifier models. The performance of all these classifier
models is evaluated. The sequential model visualizes the
better performance. (e technique of the sequential model
generates superior performance for the evaluation metrics
applied. It generates the highest value for precision and
specificity. The obtained performance metrics are
presented in Table 2.
18. The suggested methodology reveals that Sequential CNN performs
worse than Random Forest. The weakness of this methodology is that
Sequential CNN should outperform all other traditional ML approaches,
but this is not the case. It might occur because the dataset is insufficient
for training and identifying hidden patterns to predict upcoming or
future data, and the initialization of weights was extremely random,
which might have an impact on training.
Two additional improvements are possible. To improve the performance
of the proposed
methodology to identify fraudulent credit card transactions in the
healthcare sector, the
first method is to tune the hyperparameters through optimization, and
the second is to
use the transfer learning methodology.
Future Scope and Conclusion