Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
This 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
"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.
"
Build an Ensemble classifier that can detect credit card fraudulent
transactions.Implemented a classifier by use of machine learning algorithms, such as
Decision Trees, Logistic Regression, Artificial Neural Networks and Gradient Boosting
Classifier.
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance. In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
This presenation shows how to deal with the problem of fraud detection with
1. Classic machine learning techniques. All supervised machine learning algorithms for classification will do, e.g. Random Forest, Logistic Regression, etc.
2. Techniques from the outlier detection or the anomaly detection approach, e.g. autoencoder and isolation forest
First presented by Kathrin Melcher (KNIME) at ODSC Europe in London in November 2019.
Bovill - the UK financial services regulatory consultancy - runs regular briefings. These are the slides from the February briefing on anti-money laundering. For more information visit http://www.bovill.com/FinancialCrime.aspx.
Information on the event is below:
Taking a company-wide approach to money laundering
“The FCA has made it very clear that responsibility for the overall culture of firms sits at the top. We need leaders and senior managers within the industry to set the tone for how their staff behave.”
Tracey McDermott, Director of Enforcement and Financial Crime, FCA
The regulator has recently reiterated their intention to carry out further thematic and enforcement work in financial crime. However, many firms still have a fragmented approach to managing the risks of money laundering.
The responsibility for preventing financial crime is shared across the firm from the back office to the boardroom. Firms need to take a company-wide approach to tackling money laundering to ensure they are complying with regulation and managing risks effectively.
Bovill’s briefing looked at Anti-Money Laundering (AML), covering:
• Governance arrangements: as the foundation for effective communication and issue resolution
• Risk management: the difficulties of negotiating the right level of due diligence for higher risk customers and what tools can be used to help with this process
• Systems and controls: ensuring that these are fit for regulatory purpose and are appropriately maintained within your firm.
Credit Card Fraudulent Transaction Detection Research PaperGarvit Burad
Credit Card Fraudulent Transaction Detection Research Paper using Machine Learning technologies like Logistic Regression, Random Forrest, Feature Engineering and various techniques to deal with highly skewed dataset
This 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
"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.
"
Build an Ensemble classifier that can detect credit card fraudulent
transactions.Implemented a classifier by use of machine learning algorithms, such as
Decision Trees, Logistic Regression, Artificial Neural Networks and Gradient Boosting
Classifier.
Machine Learning (ML) for Fraud Detection.
- fraud is a big problem (big data, big cost)
- ML on bigger data produces better results
- Industry standard today (for detecting fraud)
- How to improve fraud detection!
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance. In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
This presenation shows how to deal with the problem of fraud detection with
1. Classic machine learning techniques. All supervised machine learning algorithms for classification will do, e.g. Random Forest, Logistic Regression, etc.
2. Techniques from the outlier detection or the anomaly detection approach, e.g. autoencoder and isolation forest
First presented by Kathrin Melcher (KNIME) at ODSC Europe in London in November 2019.
Bovill - the UK financial services regulatory consultancy - runs regular briefings. These are the slides from the February briefing on anti-money laundering. For more information visit http://www.bovill.com/FinancialCrime.aspx.
Information on the event is below:
Taking a company-wide approach to money laundering
“The FCA has made it very clear that responsibility for the overall culture of firms sits at the top. We need leaders and senior managers within the industry to set the tone for how their staff behave.”
Tracey McDermott, Director of Enforcement and Financial Crime, FCA
The regulator has recently reiterated their intention to carry out further thematic and enforcement work in financial crime. However, many firms still have a fragmented approach to managing the risks of money laundering.
The responsibility for preventing financial crime is shared across the firm from the back office to the boardroom. Firms need to take a company-wide approach to tackling money laundering to ensure they are complying with regulation and managing risks effectively.
Bovill’s briefing looked at Anti-Money Laundering (AML), covering:
• Governance arrangements: as the foundation for effective communication and issue resolution
• Risk management: the difficulties of negotiating the right level of due diligence for higher risk customers and what tools can be used to help with this process
• Systems and controls: ensuring that these are fit for regulatory purpose and are appropriately maintained within your firm.
Presentation on Financial Crimes. Money is one of the most important reasons behind all forms of crime whether Cyber or Internet crimes, Physical or Theft crimes. With the advancement of technology the crime has not decelerated but only esteemed and many more new techniques were by people and they were popularly called as Blackhat hackers. In this presentations we give an over view of the whole scenario.
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.
A robust risk assessment process is central to maintaining a strong Anti-Money Laundering (AML) compliance program. In this new Accenture presentation we explore how financial services firms can set-up an effective process. Visit our fraud and financial crime blog post for more on AML risk assessment program: http://bit.ly/2aPlQQ7
Does security and convenience go well one with another and how to increase customer's convenience in digital commerce? What's new in ACS 2.0 and how SA supports online commerce safety? Presentation will give you answers to all of those questions but also an insight about advanced security options topics.
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
CONFidence 2014: Arkadiusz Bolibok,Paweł Goleń: Evaluation of Transactional C...PROIDEA
There are more then 20 000 000 of users of internet banking systems in Poland. More than 7 000 000 of them use such systems actively. They usually lack of technical knowledge and are not aware of the current threat landscape linked with such systems. As a result users of internet banking are often profitable targets for cybercriminals.
Not all attacks are targeted against wealthy clients and highly sophisticated. It is more profitable to execute relatively simple mass attacks which affects as large population of targets as possible. The gain from single victim may be not very impressive, but number of users affected by such attacks yields a considerable outcome for the attackers.
As in every system, there are some security controls implemented in internet banking. Two such mechanisms are: user authentication, and transaction authorization. The transaction authorization is the (last?) line of defense which should be able to stop such attacks even if the first line of defense, authentication, has failed. As we know this is not always the case.
To design an effective control one needs to understand the environment in which this control will operate and the type of attacks it should withstand. To get this context we will analyze several typical attack scenarios and identify what weaknesses are exploited.
Based on this analysis we will prepare a list of requirements for an effective control s which could be used in internet banking to thwart typical attacks.
Next we will evaluate the typically used transaction authorization methods used in polish internet banking systems to check if they meet the requirements identified by us. Sample attack scenarios will be provided to demonstrate that if even one of these requirements is not met a gap is created which may lead to a successful attack.
Finally, one important question needs to be answered – can this transaction authorization mechanism be efficient enough to be the last line of defense against these mass attacks?
IWMW 2000: Trusted e-Commerce: What Does it Mean?IWMW
Published on Mar 6, 2016
Slides used in "Selling Mugs to Masters" parallel session.
See http://www.ukoln.ac.uk/web-focus/events/workshops/webmaster-2000/materials/ecommerce-parallel/
Psdot 16 a new framework for credit card transactions involving mutual authen...ZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
This presentation covers why blockchain technology uniquely addresses payments challenges. It also covers case studies of companies doing cross-border payments, payroll and identity management on private and public blockchains. For the narrative to these slides, please see this recording: http://event.on24.com/wcc/r/1253290/A48EFF87AF53E87670B41D1D98EADB6D
2. Contents
• Introduction
• Problem Definition
• Proposed Solution
• Block Diagram
• Implementation
• Software and Hardware Requirements
• Benefits
• Results and Conclusion
3. Introduction
• Online Shopping – one of the largest and
fast going trend
• Mode of payment – credit card, debit card,
Net Banking
• Online payment does not require physical
card
• Major Risk – credit /debit card detail is
known to other
4. Problem Definition
• Online payment does not require physical
card
• Anyone who know the details of card can
make fraud transactions
• Currently, card holder comes to know only
after the fraud transaction is carried out.
• No mechanism to track the fraud
transaction
5. Proposed Solution
• A mechanism is developed to determine
whether the given transaction is fraud or not
• The mechanism uses Hidden Markov Model
to detect fraud transaction
• Hidden Markov Model works on the basis of
spending habit of user.
• Classifies user into Low, Medium or High
category
7. Implementation
• Project is implemented using following technologies :
HTML, CSS, JavaScript, PHP and MySQL
• HTML and CSS is used for interface designing
• JavaScript is used for client side validation
• PHP is used for server side scripting
• MySQL is used for database
8. Hardware & Software Req.
Online
Auction System
• Pentium Core 2
Duo processor or
above
• I GB RAM
• 20 GB HDD
• Router for Internet
Connection
• Windows 2000/
Windows XP/
Windows Vista/
Windows 7
• WAMP
• Macromedia
Dreamweaver
9. Benefits
• Reduction in number of fraud transaction
• User can safely use his credit / debit card for
online transaction
• Added layer of security
10. Results and Conclusion
• Fraud detection is based on Hidden Markov
Model which is learning algorithm, hence not
100% correct
• It has detected those transaction as fraud
where user belongs to low category and high
category payment is made or vice versa
• The mechanism require at least 10 transaction
to determine accurately the transaction as
fraud or not.
