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This document presents a genetic algorithm approach for detecting credit card fraud in real time. It aims to reduce the relation between loss and available credit limit. The proposed genetic algorithm system overcomes issues with existing database-dependent systems by optimizing interval-valued parameters to detect fraud based on customer behavior, minimizing false alerts and producing optimized results. Hardware requirements include a Pentium IV 2.4 GHz system with 40GB hard disk and 256MB RAM, while the software used are Windows XP, Java, and NetBeans IDE.
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Credit Card Fraud Detection
Suraj Patro and M. Binayak Kumar Reddy presented their B.Tech major project on credit card fraud detection. They aimed to build an ensemble classifier using machine learning algorithms like decision trees, logistic regression, neural networks and gradient boosting to detect fraudulent transactions. They discussed challenges in fraud detection, implemented the project in Python using various libraries, and evaluated the performance using metrics like precision, recall and F1 score. The outcome would be an ensemble classifier model for credit card fraud detection.
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In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
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Rahul Sharma's curriculum vitae provides information about his education, skills, projects, and activities. He has a B.E. in Computer Science and Engineering from PIET in 2014 and knows languages like C, C#, ASP.NET, and SQL. His project involved using a Hidden Markov Model to detect credit card fraud by analyzing spending patterns. He has participated in workshops and activities related to software development.
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Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
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Similar to Creditcard
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Share Credit_Card_Fraud_Detection_ML_MP (1).pptx
Fraud detection is a topic which is applicable to many industries including banking and financial sectors, insurances, government agencies, and low enforcement and more.Through the use of sophisticeted use of data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
Its a process of identifying fraudulent transaction.
This technique used to recognize fraudulent creddit card transactions so that customers are not charged for items that they did not purchases
shyampresentaaaaaaaaaaaaaaaaaaaaaaa.pptx
ghtyfvgyhuohikbjgcfgvhkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkAir pollution is the act of mixing pollutants into air which is not ideal because it decreases the quality of life of human-beings and affects the overall planet’s habitat. Air pollution occurs when dangerous particles, gases, and chemicals are released into the air. The pollutants of air can be found in vehicle exhausts, fumes from factories and power plants, and construction sites. Respiratory problems, skin diseases, irritation of the eyes are some of the major health issues caused by air pollution. To combat this, many governments have created and enforced policies to reduce air pollution, such as shutting down coal power plants or requiring car owners to switch over to electric cars. Air purifiers are being installed at points of high vehicular movement. Rain seeding is another step to clean up the air. We should also plant more trees and care for them as trees filter pollutants and absorb carbon dioxide. Air Pollution is a challenge that humankind needs to overcome to see a better tomorrow.
(166 words)
Example 2: Importance of Trees
Trees are very important, valuable and necessary to our existence as they have furnished us with two important life essentials; food and oxygen. Trees intake Carbon dioxide from air and breathe out fresh oxygen. Carbon dioxide breathed in by the trees is one of the greenhouse gases. So planting more trees will clean the air and reduce the ill – effects of global warming.
Trees provide food to man and all herbivorous animals. Animals, insects, birds, and fungi make their home in the trees and make a diverse ecosystem. Trees also help in binding the soil. When trees are cut off, the most fertile top soil layer gets washed away easily in rains or floods. Trees provide us with medicinal herbs, timber, shelter too.
Hence, We should encourage planting more and more trees. It is for our own betterment and the sooner we understand this, the better it is for us.
(150 words)
Example 3: India of my Dreams
India is a country where people of all cultures and religions coexist. As Indian citizens, we are continuously looking for ways to improve our country and see a better India.
In the India of my dreams, women would be safe and be able to travel freely. Additionally, it will be a place where everyone may experience freedom and equality in its truest form. It would also be a place without caste, colour, gender, creed, social or economic standing, or race prejudice. India of my dreams should be a place where poor people get empowerment, face no poverty, do not starve, and get the proper roof to live. Additionally, I think of it as a place that experiences a lot of technological growth and development. I wish our wonderful nation nothing ggggggggg
CREDIT CARD FRAUD DETECTION
In Short : Aim of the project is to find whether the credit card used for transaction is by legitimate user or not
IRJET- Credit Card Fraud Detection using Machine Learning
This document discusses using machine learning algorithms to detect credit card fraud. It begins with an introduction to machine learning and its applications. A literature review covers previous research using algorithms like AdaBoost, decision trees, random forests, SVM and logistic regression for fraud detection. The proposed system architecture has five layers of control, including security checks, transaction blocking rules, scoring rules, and human investigators. Dimensionality reduction using PCA and classification with SVM are applied to transaction data. Data visualization with heatmaps is also discussed. The document concludes machine learning proves accurate for fraud detection and future work could explore additional algorithms.
CREDIT_CARD.ppt
The document describes using a random forest algorithm to detect credit card fraud. It begins with an abstract that outlines analyzing a credit card dataset, applying random forest, and identifying fraud transactions with 98% accuracy. Existing methods are discussed that achieve 60-70% accuracy. The proposed system uses random forest classification to analyze the dataset, which can process large amounts of data quickly and achieve 98% accuracy. Literature on the topic is surveyed. Random forest and the system architecture are described in more detail, including modules for data collection, preprocessing, feature extraction, model evaluation and visualization of results. The random forest model achieves 98.6% accuracy, outperforming other methods. Conclusions discuss potential improvements like using more data and preprocessing techniques.
A Review of deep learning techniques in detection of anomaly incredit card tr...
This document summarizes a review of deep learning techniques for detecting anomalies in credit card transactions. It discusses how credit card fraud causes major financial losses and how machine learning can help identify fraudulent transactions. The document outlines the objectives of comparing support vector machines and random forests for credit card fraud detection and discusses challenges like class imbalance in the data. It presents the system architecture for credit card fraud detection and analyzes results on a dataset of European credit card transactions, finding random forests outperform decision trees. Future work to improve accuracy is also discussed.
ROI of Fraud Detection by Nucleus Research
Customer interviews of Guardian Analytics by Rebecca Wetteman of Nucleus Research. Behavioral Analytics and Machined Learning for Real-Time Fraud detection for Wire and ACH.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
FRAUD DETECTION IN CREDIT CARD TRANSACTIONS
This document summarizes a research paper on detecting credit card fraud using machine learning algorithms. It begins by introducing the challenges of credit card fraud detection and how traditional methods are insufficient. Then it discusses how machine learning algorithms can be applied to transaction data to identify complex fraud patterns in real-time. The document outlines the methodology, including data collection, preprocessing, feature extraction, model selection and training, and model evaluation. Finally, it presents the results and performance of logistic regression, support vector machines, and random forest algorithms on the fraud detection task and concludes that machine learning is a promising approach.
A Comparative Study on Online Transaction Fraud Detection by using Machine Le...
This document summarizes a study that used machine learning and Python to detect online transaction fraud. It describes how online transactions and fraud are increasing. The study used a real credit card dataset to train models like KNN, NB, and SVM to detect fraudulent transactions based on user behavior patterns and restrict fraudulent users after three failed attempts. The goal was to develop a system that can detect fraud in real-time and prevent losses for banks and credit card users.
IRJET- Survey on Credit Card Fraud Detection
This document discusses various techniques for credit card fraud detection. It begins with an introduction to fraud detection and challenges in detecting credit card fraud. It then summarizes 6 research papers on different fraud detection techniques, including cost-sensitive decision trees, hidden Markov models, self-organizing maps, cortical learning algorithms, a fusion approach using Dempster-Shafer theory and Bayesian learning, and modified Fisher discriminant analysis. The document concludes that while various machine learning techniques have been applied to fraud detection, loss from credit card fraud continues to increase due to evolving fraud tactics, and improved dynamic systems are still needed.
Tanvi_Sharma_Shruti_Garg_pre.pdf.pdf
This document summarizes research on detecting financial fraud in healthcare using machine and deep learning. It discusses how algorithms like random forest, decision trees, logistic regression, and neural networks can be used to identify fraudulent credit card transactions. The document outlines several research papers that experimented with different models on real transaction data. It then describes how the experiments were set up, evaluating models like logistic regression, naive Bayes, KNN and sequential models on a dataset from credit card transactions in Taiwan. The sequential model achieved the best performance based on evaluation metrics like accuracy, sensitivity and precision. There is potential to improve results further by optimizing hyperparameters and using transfer learning.
Online Transaction Fraud Detection System Based on Machine Learning
This document discusses machine learning methods for detecting online transaction fraud. It proposes a system that uses behavior and location analysis to detect unusual patterns in a user's payment history and geographic location. If an unexpected pattern is identified, the user is re-verified. The system detects fraud by analyzing users' previous spending habits and locations. It can ban users after three unsuccessful verification attempts. The document also reviews several machine learning techniques for fraud detection, including nearest neighbor, naive Bayes, and support vector machines. It proposes using a bidirectional graph and transition probability matrix to model users' transaction behaviors and assess transaction validity.
ATM fraud detection system using machine learning algorithms
This document presents a machine learning-based system for detecting ATM fraud. It examines current types of ATM fraud and proposes using machine learning algorithms to build a detection system. The study evaluates supervised, unsupervised and semi-supervised machine learning approaches for this task. It specifically explores hidden Markov models, support vector machines and decision trees for fraud detection. The document outlines the methodology, including data collection, model training and testing. Charts and figures show sample outputs from the system, including detected fraud over time. The system is found to effectively detect fraud while minimizing false positives. Machine learning is concluded to be well-suited for ATM fraud detection.
Fraud Meetup
Next Jump is a company that provides an online marketplace and rewards program called WOWPoints to help employees save money on purchases from various retailers. The document discusses lessons learned from $1 million in fraud over the past 5 years. Key lessons include that fraudsters will exploit authorization systems, may target holidays when monitoring is reduced, and that a balanced approach of limiting high-risk users and products provides a better user experience than complete disabling of features. The company addresses fraud by focusing on preventing transactional fraud first through an inline risk scoring system optimized for speed, agility, and scalability using decision trees generated from historical fraud data.
A Survey of Online Credit Card Fraud Detection using Data Mining Techniques
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.
credit card.pptx
This document discusses using machine learning algorithms to detect credit card fraud. It introduces the growing problem of credit card fraud and how machine learning can help by detecting patterns in transactions. The proposed model uses a hybrid BiLSTM-MaxPooling-BiGRU architecture combined with classifiers like Naive Base, Voting, Ada Boosting and Random Forest. Experimental results on real credit card data show the majority voting method achieves good accuracy at detecting fraud cases.
Similar to Creditcard (20)
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...
IRJET- Credit Card Fraud Detection using Random Forest
IRJET- Credit Card Fraud Detection using Random Forest
IRJET- Credit Card Fraud Detection using Machine Learning
IRJET- Credit Card Fraud Detection using Machine Learning
A Review of deep learning techniques in detection of anomaly incredit card tr...
A Review of deep learning techniques in detection of anomaly incredit card tr...
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...
A Comparative Study on Online Transaction Fraud Detection by using Machine Le...
A Comparative Study on Online Transaction Fraud Detection by using Machine Le...
Online Transaction Fraud Detection System Based on Machine Learning
Online Transaction Fraud Detection System Based on Machine Learning
ATM fraud detection system using machine learning algorithms
ATM fraud detection system using machine learning algorithms
A Survey of Online Credit Card Fraud Detection using Data Mining Techniques
A Survey of Online Credit Card Fraud Detection using Data Mining Techniques
Creditcard
- 1. H.O.D: MR.P.SRINIVASULU M.Tech PROJECT GUIDE: MR.P.SRINIVASULU M.Tech PRESENTED BY: E.Bhargavi 082H1A0510 B.Srilakshmi Ch.Rajyalakshmi 082H1A0544 082H1A0533
- 2. ABSTRACTABSTRACT The rise and rapid growth of E- Commerce payment for both online as well as regular purchase
- 3. INTRODUCTIONINTRODUCTION Many modern techniques are evolved. Efficient credit card fraud detection system. In this project we propose a technique using Genetic algorithm
- 4. EXISTING SYSTEMEXISTING SYSTEM Depends on database system. Detect fraud by Credit rate. For a large amount of data it is not efficient.
- 5. PROBLEMPROBLEM Relation between loss and the available limit have to be reduced. The fraud has to be deducted in real time
- 6. PROPOSED SYSTEMPROPOSED SYSTEM Overcomes the problems in an efficient way. Genetic algorithm. Interval valued parameters are optimized.
- 7. ADVANTAGES •The fraud is detected based on the customers behavior. •The false alert is minimized and it produces an optimized result.
- 8. HARDWARE REQUIREMENTSHARDWARE REQUIREMENTS SYSTEM : Pentium IV 2.4 GHz HARD DISK : 40 GB MONITOR : 15 VGA colour MOUSE : Logitech. RAM : 256 MB KEYBOARD : 110 keys enhanced.
- 9. SOFTWARE REQUIREMENTSSOFTWARE REQUIREMENTS Operating system : Windows XP Professional Front End : JAVA Tool : NetBeans IDE
- 10. REFERENCEREFERENCE S.Benson Edwin Raj, A. Annie Portia, “Analysis on Credit Card Fraud Detection Methods”, IEEE International Conference on Computer, Communication and Electrical Technology, IEEE March 2011.