Search results “Data mining and fraud detection”
Data Mining Techniques to Prevent Credit Card Fraud
Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 10724 Ben Rodick
CANATICS – Fraud Detection through Data Analytics
CANATICS is a not-for-profit organization created by the auto insurance industry to help fight organized insurance crime. This video tells a story about how we do it.
Views: 5919 CANATICS
Anomaly Detection: Algorithms, Explanations, Applications
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 8920 Microsoft Research
Social Networks for Fraud Analytics
Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 8283 Bart Baesens
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 4921 The Audiopedia
19. Fraud Detection
Views: 622 Audit Academy
Mini Lecture: Social Network Analysis for Fraud Detection
In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
Views: 14397 Bart Baesens
Lecture 15.1 — Anomaly Detection Problem | Motivation  — [ Machine Learning | Andrew Ng ]
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Build A Complete Project In Machine Learning | Credit Card Fraud Detection | Eduonix
Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Early Black Friday Sale is here!! Get the premium courses starting at just $5. Check out the courses here: http://bit.ly/2OFHWZa Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Applied Machine Learning - Credit Card Fraud Detection Problem
BIA 780 Applications of Artificial Intelligence Final Project Presentation
Views: 7095 Joshua Krause
Fraud Analysis and Detection: Using Benfords Law and Other Effective Techniques
NASACT, in conjunction with the Association of Government Accountants and the Association of Local Government Auditors, is pleased to announce the latest in its series of training events addressing timely issues in government auditing and financial management. Would you like to know how to mine data as part of a fraud investigation? If so, this highly interactive webinar will show you how to conduct fraud investigations using data analytics. You will also gain an understanding of the concepts behind Benford's Law and how to apply statistical tools when reviewing financial records for fraudulent activity. The training includes an interactive demonstration of Benford's Law using data provided by participants. You will also learn about other real world methods to identify outliers that could indicate fraud or performance issues. These techniques have been used by the Oregon Audits Division to help put many fraudsters behind bars. The training will include demonstrations of how to use both ACL and Excel in applying these techniques. Concepts and techniques presented in this webinar can be applied by auditors, comptrollers or treasurers – essentially anyone wishing to detect fraud in government payments or programs.
Views: 2676 NASACT
Bank Fraud Prevention & Detection - The Case for Data Analytics
Join BKD for an informative session exploring data analytics and how it can be used to detect some of the most common fraud schemes affecting banks and other financial institutions. Interested in becoming a BKD Client? Contact a #TrustedAdvisor here: https://bit.ly/2zsU6jO Find us online! Twitter: https://bit.ly/2QY6rTV LinkedIn: https://bit.ly/2DwGGYp Facebook: https://bit.ly/2Igq2e3 Glassdoor: https://bit.ly/2QVdzR0
Views: 12940 BKD CPAs & Advisors
Credit Card Fraud Detection
Get the project at http://nevonprojects.com/credit-card-fraud-detection-project/ The credit card fraud detection features uses user behavior and location scanning to check for unusual patterns.
Views: 19407 Nevon Projects
Final year project demo Crime detection using data mining
Contact me here for details and other info: http://qazwsx.hol.es/mm
Views: 1355 Vineet Pande
Fraud Prevention & Detection: The Case for Data Analytics
Transportation companies and companies operating vehicle fleets are susceptible to multiple types of fraud. Your organization needs to know what tools are available to spot potential red flags. For more information visit http://www.bkd.com.
Views: 2510 BKD CPAs & Advisors
Fraud Detection
Views: 4196 IBM Redbooks
25 Fraud Prevention & Detection
In this lesson we learn how to prevent and detect fraud. Learn more and become student at EF University for FREE - http://executivefinance.teachable.com/ Like us Facebook- https://www.facebook.com/exfinance/ Linkedin- https://www.linkedin.com/company/executive-finance Twitter- https://twitter.com/exfinance
Views: 15489 Executive Finance
NEW - Fraud and Anomaly Detection using Oracle Advanced Analytics Part 1 Concepts
This is Part 1 of my Fraud and Anomaly Detection using Oracle Advanced Analytics presentations and demos series. Hope you enjoy! www.twitter.com/CharlieDataMine
Views: 5896 Charles Berger
Final Year Projects | Credit card Fraud Detection using HMM
Final Year Projects | Credit card Fraud Detection using HMM More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 7700 Clickmyproject
Improving Fraud Detection Techniques Using Social Network Analytics
Bart Baesens and Véronique Van Vlasselaer of KU Leuven talk to Inside Analytics host Maggie Miller about using social network algorithms to stay ahead of fraudsters. For more information about the Analytics 2014 conference, visit http://www.sas.com/events/analytics/europe/
Views: 2533 SAS Software
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 7694 Talks at Google
Fraud Prevention | AI in Finance
Can AI be used for fraud prevention? Yes! In this video, we'll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We'll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we'll try it out on a transaction dataset. Code for this video: https://github.com/llSourcell/AI_for_Financial_Data Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 https://www.youtube.com/watch?v=GlV_QO5B2eU https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://pythonprogramming.net/python-programming-finance-machine-learning-framework/ https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676 https://www.quantopian.com/posts/simple-machine-learning-example Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 25799 Siraj Raval
Case studyof Data mining for fraud detection tools with clustering analysis
This was a presentation done for Dr. Nagy's class. It follows the results we found while looking at a case study for fraud detection and our implementation of that tool into Mircosofts Northwind database. Like the video then hit that like button. Wanna see more videos then hop on over to https://www.youtube.com/channel/UC7eKBcMdOVFLr65mXSNN84w for more of my videos.
Views: 321 James Burns
027 Anomaly detection in R
Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 3622 Tukang Leding
Smart Cash a Money Transaction System  with  Fraud Detection Using Data Mining Techniques
Smart Cash a Money Transaction System with Fraud Detection Using Data Mining Techniques
Views: 44 Md Mehadi Hasan
Fraud Detection in Health Insurance using ECM, Bayesian and SVM
Fraud Detection in Health Insurance using ECM, Bayesian and SVM To Purchase This Project Contact: 9579380830
Views: 422 jayesh baviskar
Fraud Detection and Prevention Services by FraudGrade
Detect, mitigate and prevent online fraud to minimize losses, maximize profits and control costs. Deploying artificial intelligence and machine learning to maintain over 17,200+ risk-based fraud rules, FraudGrade can detect fraudsters quicker than any other traditional fraudulent review methods. Requiring only an email address and ip address to perform a fraud review, you can fully assess the risk associated with any customer before they can even finish typing their billing details. FraudGrade is the world's first one-size-fits-all solution to online fraud. Using artificial intelligence and machine learning to accurately assess the risk related to any potential customer world-wide on desktop, mobile apps, and mobile browsers. Requiring only an Email Address and IP Address to perform a review, you can assess risk in the background before the user has even reached a checkout form. FraudGrade deploys over 17,200+ risk-based fraud rules with a combination of Email Address Validation, IP Address Validation, Domain Validation, Proprietary Data, and Merchant Data to evaluate customers for indicators of fraud. The combined logic of all fraud rules and data validation provides a powerful and highly effective defense against online fraud. FraudGrade is the most cost-effective fraud detection and prevention solution online. Unlike some services which require a minimum of $1,000 a month or others that charge up to 10% of the sale value, FraudGrade is affordable for even the smallest of businesses with a simple pay as you go pricing structure. Signup now and receive your first 10 fraud reviews for FREE! https://www.FraudGrade.com
Views: 10571 FraudGrade
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Hey guys and welcome to another fun and easy machine tutorial on Eclat. Today we are going to be analyzing what video games get sold more frequently using an associated rule algorithm called Eclat. The Eclat algorithm which is an acronym for Equivalence CLAss Transformation is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys Halo, he also buys Gears of War. This type of pattern is called association rules and is used in many application domains such as recommender systems. In the previous lecture we discussed the Apriori Algorithm. Eclat is one of the algorithms which is meant to improve the Efficiency of Apriori. Eclat is a depth-first search algorithm using set intersection. It is a naturally elegant algorithm suitable for both sequential as well as parallel execution with locality-enhancing properties. It was first introduced by Zaki, Parthasarathy, Li and Ogihara in a series of papers written in 1997. Support us on Patreon, so we can bring you more cool Machine and Deep Learning Content :) https://www.patreon.com/ArduinoStartups ------------------------------------------------------------ To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 3950 Augmented Startups
Fraud Detection in Real Time with Graphs
Gorka Sadowski, a CISSP from the akalak cybersecurity consulting firm and Philip Rathle, VP of Product for Neo4j, talk about handling real-time fraud detection with graphs. They discuss retail banking + first-party fraud, automobile insurance fraud and online payment ecommerce fraud.
Views: 16402 Neo4j
Build Intelligent Fraud Prevention with ML and Graphs
Neo4j & Expero
Views: 1606 Neo4j
AWS re:Invent 2016: Fraud Detection with Amazon Machine Learning on AWS (FIN301)
In this session, we provide programmatic guidance on building tools and applications to detect and manage fraud and unusual activity specific to financial services institutions. Payment fraud is an ongoing concern for merchants and credit card issuers alike and these activities impact all industries, but are specifically detrimental to Financial Services. We provide a step-by-step walkthrough of a reference solution to detect and address credit card fraud in real time by using Apache Apex and Amazon Machine Learning capabilities. We also outline different resource and performance optimization options and how to work data security into the fraud detection workflow.
Views: 5902 Amazon Web Services
Data Mining Techniques to Prevent Credit Card Fraud
Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks. CANATICS is a not-for-profit organization created by the auto insurance industry to help fight organized insurance crime. This video tells a story about how we . Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud .
Views: 18 babyw0lfff
Fraud and Anomaly Detection using Oracle Advanced Anlaytics Part 2 Demo
This is Part 2 of my Fraud and Anomaly Detection using Oracle Advanced Anlaytics YouTube posting a few days ago.
Views: 3096 Charles Berger
Machine Learning for Risk Management: Fraud Detection Using Machine Learning
Credit card fraud may be one of the most common fraudulent activities in many countries. However, the number of fraudulent activities is very small (less than 1%). Common performance metrics, such as accuracy, may not be that useful for determining model performance. In this demo, you will learn how to use machine learning to detect fraudulent activities as well as how to use built-in functions in MATLAB® to calculate the area under the precision-recall curve (AUPRC), a custom performance metric. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Views: 641 MATLAB
Natalie Hockham: Machine learning with imbalanced data sets
Classification algorithms tend to perform poorly when data is skewed towards one class, as is often the case when tackling real-world problems such as fraud detection or medical diagnosis. A range of methods exist for addressing this problem, including re-sampling, one-class learning and cost-sensitive learning. This talk looks at these different approaches in the context of fraud detection. Full details — http://london.pydata.org/schedule/presentation/40/
Views: 16052 PyData
Fraud Detection with DataVisor
Lou Maresca talks with David Ting about DataVisor and the company's mission to protect businesses, like Yelp and Pinterest, from sophisticated new attacks online. Full episode at https://twit.tv/twiet/283 Subscribe: https://twit.tv/subscribe About us: TWiT.tv is a technology podcasting network located in the San Francisco Bay Area with the #1 ranked technology podcast This Week in Tech hosted by Leo Laporte. Every week we produce over 30 hours of content on a variety of programs including Tech News Today, The New Screen Savers, MacBreak Weekly, This Week in Google, Windows Weekly, Security Now, All About Android, and more. Follow us: https://twit.tv/ https://twitter.com/TWiT https://www.facebook.com/TWiTNetwork https://www.instagram.com/twit.tv/
Fraud Detection Tools
Views: 190 hasaraish
Fraud Detection system using Deep Neural Network - Hendri Karisma
The views expressed are those of the authors and don’t necessarily reflect those of Blibli.com This presentation took place at the Deep Learning Summit Singapore in April 2017. Event information: https://www.re-work.co/events/deep-learning-summit-singapore-april-2017
Views: 643 Blibli Engineering
Fraud Detection
With current technologies, identifying signals in order to detect fraud earlier is a game of guess and check. With the Ayasdi Platform, one can eliminate the guess work by automatically segmenting transactions in order to uncover all of the subtle signals that can lead to smarter classification of transactions.
Views: 4152 Ayasdi
Analysis on Credit Card Fraud Detection Methods
To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Analysis on Credit Card Fraud Detection Methods Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.
LDSS 2017 - Building a Real-time Banking Fraud Detection System - Dr Karthik Tadinada, Featurespace
If you enjoyed this talk join us at our next event, https://cambridgespark.com/datascience-summit or sign up for regular updates, https://bit.ly/2rA5VRc
Views: 1316 Cambridge Spark
Final Year Projects | Credit card transaction fraud detection Markov model
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 4552 myproject bazaar

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