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.
Credit Card Fraud Detection Using ML In DatabricksDatabricks
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.
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.
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
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
This slides shares some tips on how to identify credit card fraud - brought to you by FraudLabs Pro.com
Read the full article at https://www.fraudlabspro.com/resources/tutorials/how-to-identify-credit-card-fraud/#slideshare
Loan Prediction system is a system which provides you a interface for loan approval to the applicants application of loan. Applicants provides the system about their personal information and according to their information system gives his status of availability of loan.
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
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
Credit Card Fraud Detection Using ML In DatabricksDatabricks
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.
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.
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
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
This slides shares some tips on how to identify credit card fraud - brought to you by FraudLabs Pro.com
Read the full article at https://www.fraudlabspro.com/resources/tutorials/how-to-identify-credit-card-fraud/#slideshare
Loan Prediction system is a system which provides you a interface for loan approval to the applicants application of loan. Applicants provides the system about their personal information and according to their information system gives his status of availability of loan.
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
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
AlgoCharge offers a web-based fraud management system that assists in credit card fraud detection & prevention with Geo-based filters. The system provides various levels of fraud protection to enhance acceptance rate & reduce the risk of charge-backs.
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
Hortizontal Aggregation in SQL for Data Mining Analysis to Prepare Data SetsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Survey on Credit Card Fraud Detection Using Different Data Mining Techniquesijsrd.com
In today's world of e-commerce, credit card payment is the most popular and most important mean of payment due to fast technology. As the usage of credit card has increased the number of fraud transaction is also increasing. Credit card fraud is very serious and growing problem throughout the world. This paper represents the survey of various fraud detection techniques through which fraud can be detected. Although there are serious fraud detection technology exits based on data mining, knowledge discovery but they are not capable to detect the fraud at a time when fraudulent transaction are in progress so two techniques Neural Network and Hidden Markov Model(HMM) are capable to detect the fraudulent transaction is in progress. HMM categorizes card holder profile as low, medium, and high spending on their spending behavior. A set of probability is assigned to each cardholder for amount of transaction. The amount of incoming transaction is matched with cardholder previous transaction, if it is justified a predefined threshold value then a transaction is considered as a legitimate else it is considered as a fraud.
Advantages and disadvantages of hidden markov modeljoshiblog
Strong statistical foundation
Efficient learning algorithms-learning can take place directly from raw sequence data.
Allow consistent treatment of insertion and deletion penalties
in the form of locally learnable
Can handle inputs of variable length-they are the most
flexible generalization of sequence profiles.
Wide variety of applications including multiple alignment,
data mining and classification, structural analysis, and pattern
discovery.
Can be combined into libraries
Biometrics technology is rapidly progressing and offers attractive opportunities. In recent years, biometric authentication has grown in popularity as a means of personal identification in ATM authentication systems. The prominent biometric methods that may be used for authentication include fingerprint, palmprint, handprint, face recognition, speech recognition, dental and eye biometrics. In this paper, a microcontroller based prototype of ATM cashbox access system using fingerprint sensor module is implemented. An 8-bit PIC16F877A microcontroller developed by Microchip Technology is used in the system. The necessary software is written in Embedded 'C' and the system is tested.
Credit Cards Frauds and Cybersecurity Threats Machine Learning Detection Algo...ijtsrd
Credit and Debit cards have become the choice mode of payment online as a result of the proliferation of electronic transactions and advancement in Information and Communication Technology ICT . Because of the increased use of credit cards for payment online, the number of fraud cases associated with it has also increased scammers and fraudsters are stealing credit card information of victims online and thereby stealing their monies. There is the need therefore to stop or abate these frauds using very powerful fraud detection system that detects patterns of credit card frauds in order to prevent it from occurring. In this paper we x rayed the concept of credit card frauds and how they are carried out by fraudsters. Python 3.7.6 programming language, Jupyter Notebook 6.0.3 and Anaconda Navigator 1.9.12 were used as experimental test bed. Also, we implemented two different supervised machine learning algorithms on an imbalanced dataset such as Decision Tree and Random forest techniques. A comparative analysis of the credit card detection capabilities of these machine learning algorithms were carried out to ascertain the best detection algorithm using different performance evaluation metrics such as accuracy, precision, recall, f1 score, confusion matrix. Experimental results showed that Random Forest outperformed Decision Tree algorithm slightly in performance metrics used for performance evaluation. Obodoeze Fidelis C. | Oliver Ifeoma Catherine | Onyemachi George Olisamaka | Udeh Ifeanyi Frank Gideon | Obiokafor, Ifeyinwa Nkemdilim "Credit Cards Frauds and Cybersecurity Threats: Machine Learning Detection Algorithms as Countermeasures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52440.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-security/52440/credit-cards-frauds-and-cybersecurity-threats-machine-learning-detection-algorithms-as-countermeasures/obodoeze-fidelis-c
Online Transaction Fraud Detection using Hidden Markov Model & Behavior AnalysisCSCJournals
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Problem Reduction in Online Payment System Using Hybrid ModelIJMIT JOURNAL
Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so, chances of detecting legitimate user as a fraud will reduce.
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
Explore our students' project on detecting credit card fraud using advanced analytics techniques. This project utilizes machine learning algorithms to analyze transaction data and identify fraudulent patterns, offering valuable insights for financial institutions. Gain insights into fraud detection strategies and the impact of technology on financial security. 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.
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of particle size, particle content and operating temperature except for slight variation observed for
varying particle content. Functionally Graded Materials (FGMs) emerged and led to the development
of superior heat resistant materials.
Energy Audit is the systematic process for finding out the energy conservation
opportunities in industrial processes. The project carried out studies on various energy conservation
measures application in areas like lighting, motors, compressors, transformer, ventilation system etc.
In this investigation, studied the technical aspects of the various measures along with its cost benefit
analysis.
Investigation found that major areas of energy conservation are-
1. Energy efficient lighting schemes.
2. Use of electronic ballast instead of copper ballast.
3. Use of wind ventilators for ventilation.
4. Use of VFD for compressor.
5. Transparent roofing sheets to reduce energy consumption.
So Energy Audit is the only perfect & analyzed way of meeting the Industrial Energy Conservation.
An Implementation of I2C Slave Interface using Verilog HDLIJMER
The focus of this paper is on implementation of Inter Integrated Circuit (I2C) protocol
following slave module for no data loss. In this paper, the principle and the operation of I2C bus protocol
will be introduced. It follows the I2C specification to provide device addressing, read/write operation and
an acknowledgement. The programmable nature of device provide users with the flexibility of configuring
the I2C slave device to any legal slave address to avoid the slave address collision on an I2C bus with
multiple slave devices. This paper demonstrates how I2C Master controller transmits and receives data to
and from the Slave with proper synchronization.
The module is designed in Verilog and simulated in ModelSim. The design is also synthesized in Xilinx
XST 14.1. This module acts as a slave for the microprocessor which can be customized for no data loss.
Discrete Model of Two Predators competing for One PreyIJMER
This paper investigates the dynamical behavior of a discrete model of one prey two
predator systems. The equilibrium points and their stability are analyzed. Time series plots are obtained
for different sets of parameter values. Also bifurcation diagrams are plotted to show dynamical behavior
of the system in selected range of growth parameter
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
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• Remote control system for accessing CCR and allied system over serial or TCP.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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Credit Card Fraud Detection System: A Survey
1. International
OPEN ACCESS Journal
Of Modern Engineering Research (IJMER)
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss.9| Sept. 2014 | 24|
Credit Card Fraud Detection System: A Survey Dinesh L. Talekar 1, K. P. Adhiya 2 1,2 Department of Computer Engineering, SSBT COET, Bambhori, Jalgaon (M.S.), India
I. INTRODUCTION
While performing online transaction using a credit card issued by bank, the transaction may be either Online Purchase or transfer .The online purchase can be done using the credit or debit card issued by the bank or the card based purchase can be categorized into two types Physical Card and Virtual Card. In both the cases if the card or card details are stolen the fraudster can easily carry out fraud transactions which will result in substantial loss to card holder or bank. In the case of Online Fund Transfer a user makes use of details such as Login Id, Password and transaction password. Again here if the details of the account is wrong then, as a result, it will give rise to fraud transaction. Credit card fraud is a wide-ranging term for theft and fraud committed using a credit Card or any similar Payment mechanism as a fraudulent source of funds in a transaction. The target may be to obtain goods Without paying money, or to obtain unauthorized funds from an account. The fraud begins with either the theft of the physical card or the compromise of data associated with the account, it include the card account number or other information that would routinely and necessarily be available to a merchant during a legal transaction. The compromise can occur by many common routes and can usually be conducted without tipping off the card holder or the merchant at least until the account is ultimately used for fraud. A store clerk copying sales receipts for later use is a simple example. The speedy growth of credit card use on the Internet has made database security lapses particularly costly; in some cases, millions of accounts have been determined. Stolen cards can be reported emergently by cardholders, but a determined account can be cached by a thief for weeks or months before any miss use, making it difficult to identify the source of the determined. Popularity of online shopping is growing day to day. Credit card is the easy way to do online shopping. According to an ACNielsen study conducted in 2005 one-tenth of the world’s population is shopping online in same study it is also mentioned that credit cards are most popular mode of online payment. In US it is found that total number of credit cards from the four credit card network(Master Card, VISA, Discover, and American Express) is 609 million and 1.28 billion credit cards from above four primary credit card networks plus some other networks (Store, Oil Company and other). If consider the statistics of credit cards in India , it is found that total number of credit cards In India at the end of December-31-2012 is about 18 to 18.9 million [1]. In case of multinational banks, the usage or average balance, per borrower for credit card holder has rise up from Rs. 61,758 in 2011 to Rs. 82,455 in 2012. in the same period, private bank customers' usage rise from Rs 39,368 to Rs. 47,370[1]. As the number of credit card users increases world-wide, the opportunities for fraudster to steal credit card details and, subsequently, commit fraud are also grew up.
II. MOTIVATION
Now a day the customers prefer the most accepted payment mode via credit card for the convenient way of paying bills, online shopping is easiest way. At the same time the fraud transaction risks using credit card is a main problem which should be avoided. So There are many data mining techniques available to avoid
Abstract: 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.
Keywords: Credit Card Fraud, Hidden Markov Model (HMM), Fraud Detection, Password, Security question.
2. Credit Card Fraud Detection System: A Survey
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these risks effectively. In existing research they modeled the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and shown how it can be used for the detection of frauds. To avoid computational complexity and to provide better accuracy in fraud detection in proposed work.
III. LITERATURE SURVEY
Abhinav Srivastava et al describe the “Credit card fraud detection method by using Hidden Markov Model (HMM)” [2]. In this method, they model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of fraud Transaction. An HMM is initially trained with the normal behavior of a cardholder. S. Ghosh and Douglas L. Reilly et al describes the “Credit card fraud detection With Neural Network (NN)” [3]. In this method author use data from a credit card issuer, a neural network based credit card fraud detection system was trained on a large sample of labeled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month of time. The neural network was trained on examples of fraud due to stolen cards, lost cards, application fraud, mail-order fraud, counterfeit fraud. The network detected significantly more fraud accounts (an order of magnitude more) with significantly fewer false positives (reduced by a factor of 20) over rule based fraud detection procedures.
IV. VARIOUS TECHNIQUES FOR CREDIT CARD FRAUD DETECTION SYSTEM
In Credit Card Fraud Detection there are many methods, here we present survey of some most powerful method. Credit Card Fraud Detection Methods
Decision Tree
Genetic Algorithm
Meta Learning Strategy
Neural Network
Hidden Markov Model (HMM)
Support Vector Machine
Biological immune system
A) Decision Tree
Decision Tree algorithm is a data mining induction Techniques that recursively partitions a data set of records using depth-first greedy approach (Hunts et al, 1966) or breadth-first approach (Shafer et al, 1996) until all the data items belongs to a special class. A decision tree structure is made of root, leaf and internal nodes. The tree Structure is used in classifying unknown data records. So at each internal node of the tree, a decision of best split is made using impureness measures (Quinlan, 1993). The tree leaves are made up of the class labels which the data items have been group [5]. In this method a Credit Card Fraud Detection using algorithm for Decision Tree Learning. Although focus on the Information Gain based Decision Tree Learning in this technique estimating the best split of Purity Measures of Gini, Entropy and Information Gain Ratio to test the best classifier attribute. In this Technique simply find out the Fraudulent Customer/Merchant through Tracing Fake Mail and IP Address. Customer /merchant are suspicious if the mail is fake they are traced all information about the owner/sender through IP Address. It can find out the Location of the customer and Trace all details. Decision Tree is Powerful Technique in Data Mining Decision Tree is vital part of Credit card Fraud Detection [5].
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B) Genetic Algorithm
In this Technique fraud detected and fraud transactions are generated with the given sample data set. If this algorithm is applied into bank credit card fraud detection, the chance of fraud transactions can be predicted soon after credit card transactions is in process, and a series of anti-fraud strategies can be adopted to prevent banks from great losses before and reduce risks [6]. The Experiment process has four steps: STEP1: Input group of data credit card transactions, every transaction record with n attributes, and standardize the data, get the sample finally, which includes the confidential information about the card holder. STEP2: Compute the critical values, Calculate the Credit Card usage frequency count, Credit Card overdraft, current bank balance, Credit Card usage location, average daily spending. STEP3: Generate critical values found after limited number of generations. Critical Fraud Detected, Monitor able Fraud Detected, Ordinary Fraud Detected etc. using Genetic algorithm. STEP4: Generate fraud transactions using this algorithm. This is to analyze the feasibility of credit card fraud detection based on technique, then applies detection mining based on critical values into credit card fraud detection and proposes this detection procedures and its process [7]. The initial population is selected randomly from the sample space which has many populations. The fitness value is calculated in each population and is sorted out. In selection process is selected through tournament method. The Crossover is calculated using single point probability. Mutation mutates the new offspring using uniform probability measure. In elitism selection the best solution are passed to the further generation. The new population is generated and undergoes the same process it maximum number of generation is reached.
C) Meta Learning Strategy
The meta-learning aims to filter the legitimate transactions from the fraudulent ones, and by quickly and accurately identifying the fraudulent transactions, fraud losses can be reduced. “Meta-learning” techniques introduced by Chan and Stolfo. There are two methods of combing algorithms that were introduced by Chan and Stolfo, the arbiter and the combiner strategies. Chan and Stolfo found that the combiner strategy performs more effectively than the arbiter strategy. Therefore, the combiner strategy is used. In the combiner strategy the attributes and correct classifications of credit card transaction instances are used to train multiple base classifiers. The predictions of the base classifiers are used as new attributes for the meta-level classifier. By combining the original attributes, the base classifier predictions, and the correct classification for each instance, a new “combined” dataset is created [8] which are used as the training data to generate the meta-level classifier. The predictions from the meta-level classifier are then used as the final predictions in the combiner strategy. There are four main stages in the meta-learning process: STAGE 1: Establishes the base classifiers using a training dataset that consists of 50% fraudulent transactions and 50% legitimate transactions [8]. This was done on a month by month basis for the first 8 months where all of the fraudulent transactions for the given month were matched with an equal number of randomly chosen legitimate transactions. STAGE 2: The base classifiers are applied to a validation dataset to generate base predictions. The validation set consisted of all of the transactions. The predictions from the second stage are then combined with the validation dataset. STAGE 3: Meta-algorithm is applied to this combined dataset to produce a meta-classifier. STAGE 4: The forward predicting test stage, the meta- classifier is applied to the testing dataset to produce forward looking predictions [8].
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D) Neural Network
Fraud detection using Neural network is totally based on the human brain working principal. Neural network technology has made a computer capable of think. As human brain learn through past experience and use its knowledge or experience in making the decision in daily life problem the same technique is applied with the credit card fraud detection technology. When a particular consumer uses its credit card, There is a fix pattern of credit card use, made by the way consumer uses its credit card. When credit card is being used by unauthorized user the neural network based fraud detection system check for the pattern used by the fraudster and matches with the pattern of the original card holder on which the neural network has been trained, if the pattern matches the neural network declare the authorize transaction. When a transaction arrives for authorization, it is characterized by a stream of authorization data fields that carry information identifying the cardholder (account number) and characteristics of the transaction (e.g., amount, merchant code). There are additional data fields that can be taken in a feed from the authorization system (e.g., time of day) [9]. The neural network is design to produce output in real value between 0 and 1 .If the neural network produce output that is below .6 or .7 then the transaction is ok and if the output is above .7 then the chance of being a transaction illegal increase [9]. In the design of neural network-based pattern recognition Systems, there is always a process of business History descriptors contain features characterizing the use of the card For transactions, the payments made to the account over Some immediately prior time interval. Other some descriptors can Include such factors as the date of issue (or most recent issue) of the credit card. This is important for the detection of NRI (non-receipt of issue) fraud [9].
E) Hidden Markov Model (HMM)
An HMM is a double embedded stochastic process with two hierarchy levels. It can be used to model complicated stochastic processes as compared to a traditional Markov model. An Hidden Markov Model has a finite set of states governed by a set of transition probabilities. In a particular state, observation or an outcome can be generated according to an associated probability distribution. So It is only the outcome and not the state that is visible to an external observer. HMM uses cardholder’s spending behavior to detect fraud. In Implementation, three behavior of cardholder are taken into consideration. 1) Low spending behavior 2) Medium spending behavior 3) High spending behavior Different cardholders has their different spending behavior (low, medium, high).Low spending behavior of any cardholder means cardholder spend low amount (L), medium spending behavior of any cardholder means cardholder spend medium amount (M), high spending behavior of any cardholder means cardholder spend high amount (H). These profiles are observation symbols [10]. Algorithm Steps: Training Phase: Cluster creation STEP 1: To Identify the profile of cardholder from their purchasing STEP 2: The probability calculation depends on the amount of time that has elapsed since entry into the current state. STEP 3: To construct the training sequence for training model Detection Phase: Fraud detection STEP 1: To Generate the observation symbol STEP 2: To form new sequence by adding in existing sequence STEP 3: To Calculate the probability difference and test the result with training phase
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STEP 4: Finaly, If both are same it will be a normal customer else there will be fraud signal will be provided.
Fig: Flow chart for Credit Card Fraud Detection In this Technique Clustering algorithm are used for creating three clusters and clusters represent observation symbols. Then calculate clustering probability of each cluster, which is percentage of number of transaction in each cluster to total number of transactions. Then calculate fraudulent Transaction. But in this Proposed system no need to check the original user as we Maintain a log. We can find the most accurate detection using this technique. This reduces the tedious work of an employee in the bank. A one-time password (OTP) is a password that is valid for only one login session or transaction.
F) Support Vector Machine
Support Vector Machines (SVMs) have developed from Statistical Learning Theory. It have been widely applied to fields such as handwriting digit, character and text recognition, and more recently to satellite image classification. SVMs, like ANN and other nonparametric classifiers have a reputation for being robust. SVMs function by nonlinearly projecting the training data in the input space to a feature space of higher dimension by use of a kernel function. This results in a linearly separable dataset that can be separated by a linear classifier. This process enables the classification of datasets which are usually nonlinearly separable in the input space. The functions used to project the data from input space to feature space are called kernels (or kernel machines) examples of which include polynomial, Gaussian (more commonly referred to as radial basis functions) and quadratic functions. Each function has unique parameters which have to be checked prior to classification and it also usually determined through a cross validation process [11]. The choice of a Kernel depends on the problem at hand because it depends on what we are trying to model. A polynomial kernel, allows us to model feature up to the order of the polynomial. And Radial functions allows to pick out circles (or hyper spheres) in contrast with the Linear kernel it allows only to pick out lines (or hyper planes). Linear Kernel: The Linear kernel is the simplest kernel function. It is given by the inner product <x,y> plus and constant c as optional. Kernel algorithms using a linear kernel are often equivalent to their non-kernel counterparts, that means. KPCA[11] with linear kernel is the same as standard PCA.
Start
Login
Purchase
Credit Card Information
Verification
Transaction
Stop
Fraud Check
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K(X,Y) = XT Y + C Polynomial Kernel: The Polynomial kernel means it is a non-stationary kernel. Polynomial kernels are good for problems where as all the training data is normalized. K(X,Y) = (αXT Y + C)d The adjustable parameters are the constant term c, the slope alpha and the polynomial degree d. Here detail the proposed algorithm for classification of Fraud Transactions. Step 1: Read the given data. Step 2: Re-categorize the data in five groups as transaction month, date, day, amount of transaction & difference between successive transaction amounts. Step 3: Make each transaction in the form of data as vector of five fields. Step 4: Then make two separate groups of data named True & False transaction group (if false transaction data is not available add randomly generate data in this group). Step 5: Select one of three kernels (Linear, Quadratic, and RBF)[11]. Step 6: Train SVM. Step 7: Save the classifier. Step 8: Read the current Transaction. Step 9: Restart the process from step1 to step3 for current transaction data only. Step 10: Replaced the saved classifier & currently generated vector in classifier. Step 11: Admit the generated decision from the classifier. Since there is no real data is available because of privacy maintained by respective banks. so for testing of implementation of algorithm author generated the data of true & false Transaction using different mean & variance & then mixed them with different probability. And used the MATLAB for the execution of the algorithm because of its rich sets of mathematical functions and also supporting the inbuilt functions for SVM. Finally author said these technique give near about 90 to 97 % accuracy but future improvement is needed[11].
G) Biological immune system (BIS)
BIS is, a multilayered defense system comprising of cells and molecules which interact in various ways to detect and eliminate infectious agents (pathogens) from our body. BIS differentiates between self, (S), and (ii) nonself (NS) peptides and then assigns the right effectors to eliminate each pathogen. Similarly, detection system which sets apart fraudulent credit card transactions from genuine ones. The input for the system is financial transactions (i.e., source, destination and amount) in the form of a of e-commerce binary string.BIS in turn can be equated to a parallel adaptive information-system (IS) which works on the principle of simple and, localized rules. BIS interacts with pathogens in a localized fashion. Surfaces of BIS cells are covered with receptors, which chemically bind to (i) pathogens, and (ii) other immune system cells or molecules. Also BIS cells circulate around the body via the blood and lymph systems, to form a dynamic system of distributed detection and response. BIS has no centralized control, and hierarchical organization. Similarly, FDSCC detectors can be mobile agents that migrate across networks linking banks, financial institutions, etc.
7. Credit Card Fraud Detection System: A Survey
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss.9| Sept. 2014 | 30|
BIS will comprise of two steps (i) detection and (ii) response. In step 1, detectors will be trained to discriminate between true and fraudulent transactions. In step 2, based on the training the FDSCC will classify a given transaction. It will help to memorize the rule for subsequent detection[12]. Detectors The mobile detectors of System are analogous to the receptors on lymphocytes (B-cell receptor i.e. antibody or T-cell receptor). The receptors on lymphocytes bind to antigenic determinants (epitopes) on pathogens. Non-self detection results in the activation of the lymphocytes which trigger a series of reactions that can lead to elimination of the pathogens. A lymphocyte only activated when the number of its receptors binding to epitopes exceeds a threshold. Similarly, System detector matches the binary string inputs by using r- contiguous bit algorithm and confirms whether it is genuine or fraud transaction. The specificity of the detector is governed by the length of r-contiguous bits. Response BIS has a variety of response mechanism to eliminate different pathogens that attack the human body. One very important response (effector function) is mediated by soluble receptors called antibodies secreted by plasma cell (matured B lymphocytes). Antibody molecule has 2 parts variable region and constant region.Variable region binds to the pathogen and the constant region is responsible for the effector response. This is analogous to the System detector. Selection of effectors in System is determined by mathematical models[12]. BIS based anomaly detection and response system, which augments its performance through self learning. System will be an effective mechanism to detect and eliminate online credit card fraud transactions. This will help promote e-commerce as it will effectively minimize losses and other online credit card frauds. V. Result Comparison of Existing Methods
Authors
Year
Techniques / Algorithms
Results
Dr. R. Dhanapal
2012
Decision Tree/ Hunts Algorithm
Fraud detect by using Tracing Email and IP
Rinky D. Patel & Dheeraj Kumar Singh
2013
Genetic Algorithm
Optimizing the parametric fraud detection solution
Joseph Pun, Yuri Lawryshyn
2012
Meta Learning Strategy/ Meta Algorithm
Improvement in catch fraud than Neural Network
Raghavendra Patidar, Lokesh Sharma
2011
Neural Network/ Back Propagation Algorithm
Neural network-based pattern recognition.
Avinash Ingole, Dr. R. C. Thool
2013
HMM/ Clustering Algorithm
Fraud Detect using spending profile
Gajendra Singh, Ravindra Gupta
2012
Support Vector Machine
True Positive rate and false positive rate using MATLAB
Arunabha Mukhopadhyay,Sayali Mukherjee
2011
Artificial Immune System
By Matching Binary string Using detector and response
8. Credit Card Fraud Detection System: A Survey
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss.9| Sept. 2014 | 31|
V. CONCLUSION
Credit card fraud has become more and more rampant in recent years. To improve merchants’ risk management level in an automatic and efficient way and building an accurate and easy handling credit card risk monitoring system is one of the key tasks for the merchant banks. One aim of this study is to identify the user model that best identifies fraud cases. There are many ways of detection of credit card fraud. If one of these or combination of algorithm is applied into bank credit card fraud detection system, Then the probability of fraud transactions can be predicted soon after credit card transactions by the banks. This paper gives contribution towards the effective ways of credit card fraudulent detection. In our paper we survey on seven existing Techniques for credit card fraud detection with comparing their results hence we conclude that out of these method HMM model is one of the best model because in HMM model fraud detect using Card holders spending behavior, but we need to improvement HMM in future.
REFERENCES
[1] Avinash Ingole, Dr. R. C. Thool, “ Credit Card Fraud Detection Using Hidden Markov Model and Its Performance," International Journal of Advanced Research In Computer Science and Software Engineering (IJARCSSE), vol. 3, 6 June 2013. [2] Srivastava, Abhinav, Kundu, Amlan, Sural, Shamik and Majumdar, Arun K., (2008) “Credit Card Fraud Detection Using Hidden Markov Model”, IEEE Transactions on Dependable and Secure Computing, Vol. 5, No. 1, pp. 37-48. [3] S. Ghosh and D.L. Reilly, “Credit Card Fraud Detection with a Neural-Network,” Proc. 27th Hawaii Int’l Conf. System Sciences: Information Systems: Decision Support and Knowledge Based Systems, vol. 3, pp. 621-630, 1994. [4] Pankaj Richhariya et al “A Survey on Financial Fraud Detection Methodologies” BITS, Bhopal,” International Journal of Computer Applications (0975 – 8887) Volume 45 No.22, May 2012. [5] Dr R. Dhanapal, Gayathiri. P, “ Credit Card Fraud Detection Using Decision Tree For Tracing Email And Ip," International Journal of Computer Science Issues (IJCSI) Vol. 9, Issue 5, No 2, September 2012. [6] K.RamaKalyani, D.UmaDevi “ Fraud Detection of Credit Card Payment System by Genetic Algorithm”, International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012. [7] Rinky D. Patel, Dheeraj Kumar Singh “ Credit Card Fraud Detection & Prevention of Fraud Using Genetic Algorithm”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue- 6, January 2013. [8] Joseph Pun, Yuri Lawryshyn “ Improving Credit Card Fraud Detection using a Meta-Classification Strategy”, International Journal of Computer Applications (0975 – 8887) Volume 56– No.10, October 2012. [9] Raghavendra Patidar, Lokesh Sharma “ Credit Card Fraud Detection Using Neural Network”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-NCAI2011, June 2011. [10] Avinash Ingole, Dr. R. C. Thool Credit Card Fraud Detection Using Hidden Markov Model and Its Performance”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) ISSN: 2277 128X, Volume 3, Issue 6, June 2013. [11] Gajendra Singh, Ravindra Gupta, Ashish Rastogi, Mahiraj D. S. Chandel, A. Riyaz “A Machine Learning Approach for Detection of Fraud based on SVM”, International Journal of Scientific Engineering and Technology (ISSN : 2277-1581), Volume No.1, Issue No.3, pg : 194-198 01 July 2012. [12] Arunabha Mukhopadhyay, Sayali Mukherjee and Ambuj Mahanti, “ Artificial Immune System for detecting online credit card frauds," Research Front, www.csi-india.org, CSI Communications , December 2011.