Click Forensics is calling this the Internet marketing potential fraud behavior - "Bahama botnet" because initially it was redirecting traffic through 200,000 parked domains in the Bahamas, although it now is using sites in Amsterdam, the U.K. and Silicon Valley.
HTML tag Iframes can be very flexible too and is said to be potential source for the fraud - "Bahama botnet"; much less constrained than a "framed" page. They can be a great way to add an "update" section to a page without having to worry about the size of the new content.
Iframes place a smaller "box" containing another HTML document inside the larger main display. It's like having a smaller window inside the main window to display a separate source of information. Frames split an entire window into two or more sections. Frames run edge to edge rather than being a box placed somewhere inside.
Click fraud affects marketers who spend money on pay-per-click (PPC).
Sophisticated Botnet Causing a Surge in Click Fraud
Study: Half Of Ad Impressions, 95 Percent Of Clicks Fraudulent
NYtimes.com Ad Scam Linked to 'Bahama' Botnet
Click Fraud's New Asian Connection
Yahoo! Cozies Up To Its Click-Fraud Critics
Click Fraud Goes Viral
Google Defends Its Clicks
Note: First introduced by Microsoft Internet Explorer in 1997 and long only available in that browser, the iframe tag is now widely supported by visual browsers. Unlike an object element, an inline frame may be the "target" frame for links defined in other elements and it may be "selected" by a browser as the focus for printing, viewing HTML source etc.
Showing posts with label Fraud Analytics. Show all posts
Showing posts with label Fraud Analytics. Show all posts
Wednesday, September 23, 2009
Saturday, August 15, 2009
BI can lay stepping stone for Fraud Analytics!
Fraud losses can impact every business. Fraud Analytic differs from Business Intelligence(BI) type Analytic with relatively higher human interaction and deep dive, but it's all about data. Understanding of data and visual representation can provide early alert, enabling users to take timely action to stop fraud and halt losses. A proactive approach will combat fraudulent behavior and increase the perceived value of the organization and help to the cause of Customer loyalty, competitive edge in dynamic market place, merger and acquisition so on so forth ...
Information Life-cycle and evolution: Yes, Information has a lifecycle. Information derived from data should be following the information supply chain. Data becomes information when it represents business relationships. Data should be aligned to business model and process such that precise insight can be represented of customer behavior, how much acceptable risk can be taken and so on. Organization of information should be able to cater to wide range of strategies covering definition, policy, infrastructure and operation and functionality. For example - Data is born when customer places an order, which gets associated to identification definitions like customer, product, market .. Then goes thru order fulfillment and then customer service repair so on so forth. The data in the meantime goes through various transformations as it is related to financial, marketing, demand planning or predictive uses, in order to answer questions crucial to operating and optimizing business decisions. Finally, information has an end game. The customer moves away or the product is discontinued. The data is no longer updated, remains unused by the business and eventually become irrelevant both to the enterprise and the society in which the enterprise does business. As the volumes of data accumulate, the data warehouse becomes “obese.” Meanwhile, the data warehouse become entwined with mission-critical systems, impacting the performance of both transactional and decision support systems. This drive information mining with stale information and more resources to deep dive into a slice of information set for confirming the outliers found in the initial data set.
Managing Your Data Growth: A system that has been in production for several years is likely to contain a significant volume of data that is not used at all or used infrequently. Data warehouses often start big and get even bigger. The lifecycle of the data warehouse and the requirement to perform archiving shifts into the foreground. Enterprises engage in data archiving as part of an approach to information lifecycle management, of which data warehousing is an essential part. Archiving is the best way both to improve performance of the data warehouse (or transactional system) and to satisfy the requirements for data retention and security. This will enable improved ROI, information richness and better response when reaching for active and/or inactive data.
Visual representation of Information for Discovery: The human brain is good at doing some things and limited in others. For example, our brains is good at recognizing visual patterns, while they are able to remember relatively little from large amount of information. Primarily data analysis is making sense using comparison as individual facts mean nothing by themselves. Facts become meaningful when we compare them to one another. By displaying data in series of small graphs arranged as a visual cross-tab, which allows multiple dimensions to be compared simultaneously. This will allow users to see and compare patterns and trends of outlier or inconsistent behavior. Thus provide potential fraud candidate for further investigation.
Conforming Architecture: Fraud identification has a different approach than general Data warehouse or BI solution. In case of general BI solution, the measurement criteria is often a set of transaction quantifying success of a campaign or Sales Measures so on, however fraud would taking a subset of data and analyze association with scenarios by means of data and deep dive. As BI can provide model based information of trends, human interaction will identify the outlier cases for further investigation. Mature Business Intelligence architecture need to consider the commonalities and differentiators in their architecture to cater to these different audience needs. In addition, the deployment design of information architecture if not flexible enough then there will be high cost of reaching to the tip of the iceberg beyond that potential could be more investment for each scenario i.e unsustainable spiral.
Holistic approach: Fraud Management Lifecycle is dynamic, evolving, and BI solution architectures should be flexible enough to adaptive it. Effective fraud management requires a balance in the competing and complementary actions within the Information Lifecycle. Solutions can be defined based on past data trend, but the power of success lies with solutions that can embrace the new data to provide the insight.
Consolidation has left a lot of companies with multiple incompatible systems, inconsistent applied policies, more holds and less penetration in dynamic market place. Even with mature BI organization, fraud analytic can only be effective with ability to efficiently link with different data set and robust architecture.
Reference: Journal of Economic Crime Management
Statistics: The Fraud Management Lifecycle Theory
Information Life-cycle and evolution: Yes, Information has a lifecycle. Information derived from data should be following the information supply chain. Data becomes information when it represents business relationships. Data should be aligned to business model and process such that precise insight can be represented of customer behavior, how much acceptable risk can be taken and so on. Organization of information should be able to cater to wide range of strategies covering definition, policy, infrastructure and operation and functionality. For example - Data is born when customer places an order, which gets associated to identification definitions like customer, product, market .. Then goes thru order fulfillment and then customer service repair so on so forth. The data in the meantime goes through various transformations as it is related to financial, marketing, demand planning or predictive uses, in order to answer questions crucial to operating and optimizing business decisions. Finally, information has an end game. The customer moves away or the product is discontinued. The data is no longer updated, remains unused by the business and eventually become irrelevant both to the enterprise and the society in which the enterprise does business. As the volumes of data accumulate, the data warehouse becomes “obese.” Meanwhile, the data warehouse become entwined with mission-critical systems, impacting the performance of both transactional and decision support systems. This drive information mining with stale information and more resources to deep dive into a slice of information set for confirming the outliers found in the initial data set.
Managing Your Data Growth: A system that has been in production for several years is likely to contain a significant volume of data that is not used at all or used infrequently. Data warehouses often start big and get even bigger. The lifecycle of the data warehouse and the requirement to perform archiving shifts into the foreground. Enterprises engage in data archiving as part of an approach to information lifecycle management, of which data warehousing is an essential part. Archiving is the best way both to improve performance of the data warehouse (or transactional system) and to satisfy the requirements for data retention and security. This will enable improved ROI, information richness and better response when reaching for active and/or inactive data.
Visual representation of Information for Discovery: The human brain is good at doing some things and limited in others. For example, our brains is good at recognizing visual patterns, while they are able to remember relatively little from large amount of information. Primarily data analysis is making sense using comparison as individual facts mean nothing by themselves. Facts become meaningful when we compare them to one another. By displaying data in series of small graphs arranged as a visual cross-tab, which allows multiple dimensions to be compared simultaneously. This will allow users to see and compare patterns and trends of outlier or inconsistent behavior. Thus provide potential fraud candidate for further investigation.
Conforming Architecture: Fraud identification has a different approach than general Data warehouse or BI solution. In case of general BI solution, the measurement criteria is often a set of transaction quantifying success of a campaign or Sales Measures so on, however fraud would taking a subset of data and analyze association with scenarios by means of data and deep dive. As BI can provide model based information of trends, human interaction will identify the outlier cases for further investigation. Mature Business Intelligence architecture need to consider the commonalities and differentiators in their architecture to cater to these different audience needs. In addition, the deployment design of information architecture if not flexible enough then there will be high cost of reaching to the tip of the iceberg beyond that potential could be more investment for each scenario i.e unsustainable spiral.
Holistic approach: Fraud Management Lifecycle is dynamic, evolving, and BI solution architectures should be flexible enough to adaptive it. Effective fraud management requires a balance in the competing and complementary actions within the Information Lifecycle. Solutions can be defined based on past data trend, but the power of success lies with solutions that can embrace the new data to provide the insight.
Consolidation has left a lot of companies with multiple incompatible systems, inconsistent applied policies, more holds and less penetration in dynamic market place. Even with mature BI organization, fraud analytic can only be effective with ability to efficiently link with different data set and robust architecture.
Reference: Journal of Economic Crime Management
Statistics: The Fraud Management Lifecycle Theory
Thursday, April 9, 2009
Finding Fraud with analytics
Fraud analytics starts with a theory. Theory has assumptions and some gut factor. Think about it, all our life from childhood to adult life we are involved in search-n-seek. Childhood days it's cookies, candies and so on, but as we grow up car keys, glasses, remote so on so forth; however the point to note is that the strategy to find things constantly evolves. Perhaps, to find fraud in business process and systems, the search-n-find skill need to be taken to the next level.
The detection strategy should be such that is proactive and constantly audited. But, based on experience one need to outline some assumptions to identify trees in the forest. The approach to find the tree in the forest should be such that it can be modeled and be repetitive processes. These models and processes can be deployed by IT teams for business to monitor and evolve the pattern. Sounds simple, but a properly designed fraud plan begins with simply looking for instances where a fraud scenario is most likely to occur, much like a search-and-find game.
Effective fraud plan also requires awareness, or the ability to interpret the data for the indicators, of the fraud scenario. While the simple fraud scenarios can be detected via a properly designed fraud data procedure, a fraud scenario with a sophisticated concealment strategy requires the ability to see through the concealment strategy.
1. List your assumptions based on high probable cases. The key considerations are to
understand the variations of the scenario that are caused by the fraud opportunity. This helps define the scope of the Fraud Audit.
2. Develop a fraud data profile with data, using the process of drawing a picture of a fraud scenario. For example, one variation of a false billing scheme through a false company is when the accounts payable takes over the identity of a dormant vendor on the database and charges invoices to a large cost center.
3. Structured step-by-step approach to identifying transactions consistent with a fraud scenario/assumption, as described through the fraud data profile.
4. Obtain pertinent data and their relation to the assumption.
5. Define data interrogation procedure - pattern & Frequency, identify outlier cases for good and bad both, Trends, GAP in business process, potential mistakes in data capture and transactional history, Master data accuracy
6. Define the KPI (Key performance Indicator) and monitor the indicators regularly
7. Prepare plan to respond to the indicator pattern. Evolve the KPIs for further sophistication and insight.
8. Once the culprits is identified monitor their behavior for firming up the plan. This will also help evolve the good vs bad outliers.
Search routines help focus identifying of “red flags” of the fraud scenario/assumption. By using data interpretation, one can develop reports or documentation and interpret the data.
Facts:
Insurance: In United States, about $67 billion is lost every year to fraudulent claim.(Federal Bureau of Investigation [FBI], 2003).
Telecommunications: $1.5 trillion phone industry loses approximately 10% to fraud, that is $150 billion at current estimates (Mena, 2003).
Bank Fraud: For the period of April 1, 1996 through September 30, 2002, the FBI received 207,051 Suspicious Activity Reports equaled approximately $7 billion in losses (U.S. Department of Justice [DOJ], 2002).
Money Laundering: United States Treasury officials estimate that as much as $300 billion is laundered annually, worldwide, with from $40 billion to $80 billion of this originating from drug profits made in the United States. (Mena, 2003).
Internet: According to Meridien Research, worldwide credit card fraud[the Internet component] will represent $15.5 billion in losses [annually] by 2005. However, if merchants adopt data mining technology now to help screen credit-card orders prior to processing, the widespread use of this technology is predicted to cut overall losses by two thirds to $5.7 billion in 2005” (Mena, 2003).
Credit Card: The numbers from the Nilson report indicate that issuer credit card fraud losses run approximately 1 billion dollars annually. This list does not even include debit card fraud, brokerage fraud, fraud at casinos, health care fraud, and other miscellaneous fraud types such as bankruptcy fraud
Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2
Senator Everett Dirksen so aptly said, “A billion here a trillion there; the first thing you know, you’re talking about real money.”
Source: Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2
Related Articles:
1. Fraud By the Book
2. Medicare Fraud
The detection strategy should be such that is proactive and constantly audited. But, based on experience one need to outline some assumptions to identify trees in the forest. The approach to find the tree in the forest should be such that it can be modeled and be repetitive processes. These models and processes can be deployed by IT teams for business to monitor and evolve the pattern. Sounds simple, but a properly designed fraud plan begins with simply looking for instances where a fraud scenario is most likely to occur, much like a search-and-find game.
Effective fraud plan also requires awareness, or the ability to interpret the data for the indicators, of the fraud scenario. While the simple fraud scenarios can be detected via a properly designed fraud data procedure, a fraud scenario with a sophisticated concealment strategy requires the ability to see through the concealment strategy.
1. List your assumptions based on high probable cases. The key considerations are to
understand the variations of the scenario that are caused by the fraud opportunity. This helps define the scope of the Fraud Audit.
2. Develop a fraud data profile with data, using the process of drawing a picture of a fraud scenario. For example, one variation of a false billing scheme through a false company is when the accounts payable takes over the identity of a dormant vendor on the database and charges invoices to a large cost center.
3. Structured step-by-step approach to identifying transactions consistent with a fraud scenario/assumption, as described through the fraud data profile.
4. Obtain pertinent data and their relation to the assumption.
5. Define data interrogation procedure - pattern & Frequency, identify outlier cases for good and bad both, Trends, GAP in business process, potential mistakes in data capture and transactional history, Master data accuracy
6. Define the KPI (Key performance Indicator) and monitor the indicators regularly
7. Prepare plan to respond to the indicator pattern. Evolve the KPIs for further sophistication and insight.
8. Once the culprits is identified monitor their behavior for firming up the plan. This will also help evolve the good vs bad outliers.
Search routines help focus identifying of “red flags” of the fraud scenario/assumption. By using data interpretation, one can develop reports or documentation and interpret the data.
Facts:
Insurance: In United States, about $67 billion is lost every year to fraudulent claim.(Federal Bureau of Investigation [FBI], 2003).
Telecommunications: $1.5 trillion phone industry loses approximately 10% to fraud, that is $150 billion at current estimates (Mena, 2003).
Bank Fraud: For the period of April 1, 1996 through September 30, 2002, the FBI received 207,051 Suspicious Activity Reports equaled approximately $7 billion in losses (U.S. Department of Justice [DOJ], 2002).
Money Laundering: United States Treasury officials estimate that as much as $300 billion is laundered annually, worldwide, with from $40 billion to $80 billion of this originating from drug profits made in the United States. (Mena, 2003).
Internet: According to Meridien Research, worldwide credit card fraud[the Internet component] will represent $15.5 billion in losses [annually] by 2005. However, if merchants adopt data mining technology now to help screen credit-card orders prior to processing, the widespread use of this technology is predicted to cut overall losses by two thirds to $5.7 billion in 2005” (Mena, 2003).
Credit Card: The numbers from the Nilson report indicate that issuer credit card fraud losses run approximately 1 billion dollars annually. This list does not even include debit card fraud, brokerage fraud, fraud at casinos, health care fraud, and other miscellaneous fraud types such as bankruptcy fraud
Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2
Senator Everett Dirksen so aptly said, “A billion here a trillion there; the first thing you know, you’re talking about real money.”
Source: Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2
Related Articles:
1. Fraud By the Book
2. Medicare Fraud
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