FAQ: Access to Shared Address Book and Distribution Groups

Q: Is there a way to restrict access to the Shared Address Book or Distribution Groups?

A: It is not possible to deny access to these areas of an account. They will be visible to all employee users.

Q: But what if I don’t want users to be able to change the information in the Shared Address Book?

A: Employee users cannot edit the Shared Address Book unless they have been granted the “Edit Shared Address Book” permission.

Q: Why can’t I find a Distribution Group created by an Employee user when I’m an administrator?

A: The creator of a distribution group needs to select “Share this distribution group with all employees” in order for other users to have access.

Q: How can I create a Shared Distribution Group without the ability for other users to edit the group?

A: If you share a distribution group with all employee users, any employee with the admin permission for “Edit other users’ shared distribution groups” will be able to edit the group.

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This Deep Learning Technology Is Redefining Ad Integrity

By Russ Banham, Contributor

In the pre-social media era, marketers had a good idea about how many times consumers saw their corporate logo and other brand images. Then, around 2010, after social media sites took off, millions of people could see a company’s logo, unbeknownst to the organisation. That was both good and bad news.

While exposing people to a company’s logo could, in theory, make them more familiar with the brand, products, and services, there was also the possibility the image could be put into a negative context and go viral. Until recently, marketers had no way to monitor the use of their advertising images by third parties.

Today, deep learning makes this possible. Computer vision, a subset of deep learning technology, gives marketers insights into how many people have viewed a logo or other brand images, in addition to their context.

“The use of computer vision for marketing purposes is becoming an increasingly common application of deep learning technology. Companies can see just how they are being represented in online images, both positively and negatively.”

Clement Chung, Director of Machine Learning at Wave Financial



Ad Impact, Across Mediums



Deep learning is a subset of machine learning, itself a subset of artificial intelligence, in which computers are instructed to learn by example. For instance, in self-driving cars, the car’s computer is instructed to stop at a red light. Once the car is programmed to do so, the machine will know to stop at all red lights.

Computer vision technology, then, uses object recognition software to tabulate how many times an ad or logo has been viewed in social or traditional media, and the context in which the image appeared.

“The technique involves two parts—the development of an algorithm to train the computer to find the image, and then the use of object recognition to determine the context of the image,” said Chung.

This has to two positive effects for marketers. To begin, it helps them identify how many times an ad was viewed outside of its original distribution campaign. If we were to consider a Dodgers Stadium beer ad, for example, computer vision technology could provide a way to calculate how many times the billboard at the game was seen both on social media and traditional media. If the game is televised, chances are local and even national news stations may also carry images of the event in which the billboard ad is visible.

Detecting viewership can help marketing teams decide where to put ad dollars. “What if the ratings on the televised event have dropped significantly, meaning fewer people are seeing it at home?” Brian Kim, senior vice president of product at GumGum, a leading computer vision company,said. “This might convince the marketer to put its spend elsewhere.”

Using computer vision, companies can calculate if more people saw the image on social media or other media than the TV ratings indicate. “That’s a far better determinant of the advertisement’s value,” Kim noted.

Chung agreed, stating that a marketer may also discover things like a billboard placed behind the catcher was seen by more people than one situated in left field. “The goal in all cases is to get a bigger bang for your advertising dollar,” he said.

An Opportunity Algorithm

The technology also identifies missed opportunities and flags necessary damage control. If the context is positive, the company has the opportunity to push the advertised image toward becoming viral.

However, there is also the risk that an image of the brand or its logo could become a “meme of the worst kind, used for satirical purposes,” said Chung. “In such cases, the product’s brand value can quickly erode, especially if the marketer is unaware of the negative associations and is too late to do anything about it.”

Computer vision offers a way to be notified in real time about insulting brand imagery. “An algorithm can be created to spot the use of certain offensive words that accompany the image on social media,” Chung pointed out. One obvious example, he noted: “This product sucks!”

With the emerging technology, marketers have the ability to counter the offense. “There have been memes created by overworked millennials and teenagers fed up with too much homework where they’ve snapped a picture of themselves ‘drinking’ a household cleaning product—the ‘my fake suicide’ kind of thing,” said Kim. “With computer vision tools, brand managers have instant access to what is now a negative trend to quickly adjust the conversation in a more positive direction.”

Of course, computer vision can also help spot and seize marketing opportunities. For instance, GumGum has created a way for marketers to run an advertisement at opportune—often fleeting—moments in social media.

“We’ve developed a contextual relevance algorithm using object recognition software that can pinpoint, for example, when happy images involving humans and cats appear on social media,” said Kim. “Say this image appeared on CNN. We now have the opportunity to stick a banner ad for a national pet store chain into the image in real time. We would receive income from the pet store chain to display the advertisement and arrange for CNN to be paid a portion of the earnings.”

The technology is also able to train the algorithm to find a context in which a brand should appear in certain images, but doesn’t. With the beer company example, this might include adding logos to images of people photographed at parties, restaurants, or taverns where they know the beer is served.

“Computer vision can find images that fit the marketer’s desired demographic, and if the brand is not evident in these images, the information is nonetheless insightful for marketing purposes. The company now has better intelligence on where to put its marketing spend.”

Clement Chung, Director of Machine Learning at Wave Financial

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Find the Truth With Data: 5 Fraud Detection Use Cases

According to Ernst and Young, $8.2 billion a year is lost to the marketing, advertising, and media industries through fraudulent impressions, infringed content, and malvertising.

The combination of fake news, trolls, bots and money laundering is skewing the value of information and could be hurting your business.

It’s avoidable.

By using graph technology and the data you already have on hand, you can discover fraud through detectable patterns and stop their actions.

We collaborated with Sungpack Hong, Director of Research and Advanced Development at Oracle Labs to demonstrate five examples of real problems and how graph technology and data are being used to combat them.

Get started with data—register for a guided trial to build a data lake

But first, a refresher on graph technology.

What Is Graph Technology?

With a graph technology, the basic premise is that you store, manage and query data in the form of a graph. Your entities become vertices (as illustrated by the red dots). Your relationships become edges (as represented by the red lines).

What Is Graph Technology

By analyzing these fine-grained relationships, you can use graph analysis to detect anomalies with queries and algorithms. We’ll talk about these anomalies later in the article.

The major benefit of graph databases is that they’re naturally indexed by relationships, which provides faster access to data (as compared with a relational database). You can also add data without doing a lot of modeling in advance. These features make graph technology particularly useful for anomaly detection—which is mainly what we’ll be covering in this article for our fraud detection use cases.

How to Find Anomalies with Graph Technology

Gartner 5 Layers of Fraud Detection

If you take a look at Gartner’s 5 Layers of Fraud Protection, you can see that they break the analysis to discover fraud into two categories:

  • Discrete data analysis where you evaluate individual users, actions, and accounts
  • Connected analysis where relationships and integrated behaviors facilitate the fraud

It’s this second category based on connections, patterns, and behaviors that can really benefit from graph modeling and analysis.

Through connected analysis and graph technology, you would:

  • Combine and correlate enterprise information
  • Model the results as a connected graph
  • Apply link and social network analysis for discovery

Now we’ll discuss examples of ways companies can apply this to solve real business problems.

Fraud Detection Use Case #1: Finding Bot Accounts in Social Networks

In the world of social media, marketers want to see what they can discover from trends. For example:

  • If I’m selling this specific brand of shoes, how popular will they be? What are the trends in shoes?
  • If I compare this brand with a competing brand, how do the results mirror actual public opinion?
  • On social media, are people saying positive or negative things about me? About my competitors?

Of course, all of this information can be incredibly valuable. At the same time, it can mean nothing if it’s all inaccurate and skewed by how much other companies are willing to pay for bots.

In this case, we worked with Oracle Marketing Cloud to ensure the information they’re delivering to advertisers is as accurate as possible. We sought to find the fake bot accounts that are distorting popularity.

As an example, there are bots that retweet certain target accounts to make them look more popular.

To determine which accounts are “real,” we created a graph between accounts with retweet counts as the edge weights to see how many times these accounts are retweeting their neighboring accounts. We found that the unnaturally popularized accounts exhibit different characteristics from naturally popular accounts.

Here is the pattern for a naturally popular account:

Naturally Popular Social Media Account

And here is the pattern for an unnaturally popular account:

Unnaturally Popular Social Media Account

When these accounts are all analyzed, there are certain accounts that have obviously unnatural deviation. And by using graphs and relationships, we can find even more bots by:

  • Finding accounts with a high retweet count
  • Inspecting how other accounts are retweeting them
  • Finding the accounts that also get retweets from only these bots

Fraud Detection Use Case #2: Identifying Sock Puppets in Social Media

In this case, we used graph technology to identify sockpuppet accounts (online identity used for purposes of deception or in this case, different accounts posting the same set of messages) that were working to make certain topics or keywords look more important by making it seem as though they’re trending.

Sock Puppet Accounts in Social Media

To discover the bots, we had to augment the graph from Use Case #1. Here we:

  • Added edges between the authors with the same messages
  • Counted the number of repeated messaged and filtered to discount accidental unison
  • Applied heuristics to avoid n2 edge generation per same message

Because we found that the messages were always the same, we were able to take that and create subgraphs using those edges and apply a connected components algorithm.

Sock Puppet Groups

As a result of all of the analysis that we ran on a small sampling, we discovered that what we thought were the most popular brands actually weren’t—our original list had been distorted by bots.

See the image below – the “new” most popular brands barely even appear on the “old” most popular brands list. But they are a much truer reflection of what’s actually popular. This is the information you need.

Brand Popularity Skewed by Bots

After one month, we revisited the identified bot accounts just to see what had happened to them. We discovered:

  • 89% were suspended
  • 2.2% were deleted
  • 8.8% were still serving as bots

Fraud Detection Use Case #3: Circular Payment

A common pattern in financial crimes, a circular money transfer essentially involves a criminal sending money to himself or herself—but hides it as a valid transfer between “normal” accounts. These “normal” accounts are actually fake accounts. They typically share certain information because they are generated from stolen identities (email addresses, addresses, etc.), and it’s this related information that makes graph analysis such a good fit to discover them.

For this use case, you can use graph representation by creating a graph from transitions between entities as well as entities that share some information, including the email addresses, passwords, addresses, and more. Once we create a graph out of it, all we have to do is write a simple query and run it to find all customers with accounts that have similar information, and of course who is sending money to each other.

Circular Payments Graph Technology

Fraud Detection Use Case #4: VAT Fraud Detection

Because Europe has so many borders with different rules about who pays tax to which country when products are crossing borders, VAT (Value Added Tax) fraud detection can get very complicated.

In most cases, the importer should pay the VAT and if the products are exported to other countries, the exporter should receive a refund. But when there are other companies in between, deliberately obfuscating the process, it can get very complicated. The importing company delays paying the tax for weeks and months. The companies in the middle are paper companies. Eventually, the importing company vanishes and that company doesn’t pay VAT but is still able to get payment from the exporting company.

VAT Fraud Detection

This can be very difficult to decipher—but not with graph analysis. You can easily create a graph by transactions; who are the resellers and who is creating the companies?

In this real-life analysis, Oracle Practice Manager Wojciech Wcislo looked at the flow and how the flow works to identify suspicious companies. He then used an algorithm in Oracle Spatial and Graph to identify the middle man.

The graph view of VAT fraud detection:

Graph View of VAT Fraud Detection

A more complex view:

Complex View of Graph Technology and Anomaly Detection

In that case, you would:

  • Identify importers and exporters via simple query
  • Aggregate of VAT invoice items as edge weights
  • Run Fattest Path Algorithm

And you will discover common “Middle Man” nodes where the flows are aggregated

Fraud Detection Use Case #5: Money Laundering and Financial Fraud

Conceptually, money laundering is pretty simple. Dirty money is passed around to blend it with legitimate funds and then turned into hard assets. This was the kind of process discovered in the Panama Papers analysis.

These tax evasion schemes often rely on false resellers and brokers who are able to apply for tax refunds to avoid payment.

But graphs and graph databases provide relationship models. They let you apply pattern recognition, classification, statistical analysis, and machine learning to these models, which enables more efficient analysis at scale against massive amounts of data.

In this use case, we’ll look more specifically at Case Correlation. In this case, whenever there are transactions that regulations dictate are suspicious, those transactions get a closer look from human investigators. The goal here is to avoid inspecting each individual activity separately but rather, group these suspicious activities together through pre-known connections.

Money Laundering and Financial Fraud

To find these correlations through a graph-based approach, we implemented this flow through general graph machines, using pattern matching query (path finding) and connected component graph algorithm (with filters).

Through this method, this company didn’t have to create their own custom case correlation engine because they could use graph technology, which has improved flexibility. This flexibility is important because different countries have different rules.

Conclusion

In today’s world, the scammers are getting ever more inventive. But the technology is too. Graph technology is an excellent way to discover the truth in data, and it is a tool that’s rapidly becoming more popular. If you’d like to learn more, you can find white papers, software downloads, documentation and more on Oracle’s Big Data Spatial and Graph pages.

And if you’re ready to get started with exploring your data now, we offer a free guided trial that enables you to build and experiment with your own data lake.

Related:

  • No Related Posts

5 Graph Analytics Use Cases

According to Ernst and Young, $8.2 billion a year is lost to the marketing, advertising, and media industries through fraudulent impressions, infringed content, and malvertising.

The combination of fake news, trolls, bots and money laundering is skewing the value of information and could be hurting your business.

It’s avoidable.

By using graph technology and the data you already have on hand, you can discover fraud through detectable patterns and stop their actions.

We collaborated with Sungpack Hong, Director of Research and Advanced Development at Oracle Labs to demonstrate five examples of real problems and how graph technology and data are being used to combat them.

Get started with data—register for a guided trial to build a data lake

But first, a refresher on graph technology.

What Is Graph Technology?

With a graph technology, the basic premise is that you store, manage and query data in the form of a graph. Your entities become vertices (as illustrated by the red dots). Your relationships become edges (as represented by the red lines).

What Is Graph Technology

By analyzing these fine-grained relationships, you can use graph analysis to detect anomalies with queries and algorithms. We’ll talk about these anomalies later in the article.

The major benefit of graph databases is that they’re naturally indexed by relationships, which provides faster access to data (as compared with a relational database). You can also add data without doing a lot of modeling in advance. These features make graph technology particularly useful for anomaly detection—which is mainly what we’ll be covering in this article for our fraud detection use cases.

How to Find Anomalies with Graph Technology

Gartner 5 Layers of Fraud Detection

If you take a look at Gartner’s 5 Layers of Fraud Protection, you can see that they break the analysis to discover fraud into two categories:

  • Discrete data analysis where you evaluate individual users, actions, and accounts
  • Connected analysis where relationships and integrated behaviors facilitate the fraud

It’s this second category based on connections, patterns, and behaviors that can really benefit from graph modeling and analysis.

Through connected analysis and graph technology, you would:

  • Combine and correlate enterprise information
  • Model the results as a connected graph
  • Apply link and social network analysis for discovery

Now we’ll discuss examples of ways companies can apply this to solve real business problems.

Fraud Detection Use Case #1: Finding Bot Accounts in Social Networks

In the world of social media, marketers want to see what they can discover from trends. For example:

  • If I’m selling this specific brand of shoes, how popular will they be? What are the trends in shoes?
  • If I compare this brand with a competing brand, how do the results mirror actual public opinion?
  • On social media, are people saying positive or negative things about me? About my competitors?

Of course, all of this information can be incredibly valuable. At the same time, it can mean nothing if it’s all inaccurate and skewed by how much other companies are willing to pay for bots.

In this case, we worked with Oracle Marketing Cloud to ensure the information they’re delivering to advertisers is as accurate as possible. We sought to find the fake bot accounts that are distorting popularity.

As an example, there are bots that retweet certain target accounts to make them look more popular.

To determine which accounts are “real,” we created a graph between accounts with retweet counts as the edge weights to see how many times these accounts are retweeting their neighboring accounts. We found that the unnaturally popularized accounts exhibit different characteristics from naturally popular accounts.

Here is the pattern for a naturally popular account:

Naturally Popular Social Media Account

And here is the pattern for an unnaturally popular account:

Unnaturally Popular Social Media Account

When these accounts are all analyzed, there are certain accounts that have obviously unnatural deviation. And by using graphs and relationships, we can find even more bots by:

  • Finding accounts with a high retweet count
  • Inspecting how other accounts are retweeting them
  • Finding the accounts that also get retweets from only these bots

Fraud Detection Use Case #2: Identifying Sock Puppets in Social Media

In this case, we used graph technology to identify sockpuppet accounts (online identity used for purposes of deception or in this case, different accounts posting the same set of messages) that were working to make certain topics or keywords look more important by making it seem as though they’re trending.

Sock Puppet Accounts in Social Media

To discover the bots, we had to augment the graph from Use Case #1. Here we:

  • Added edges between the authors with the same messages
  • Counted the number of repeated messaged and filtered to discount accidental unison
  • Applied heuristics to avoid n2 edge generation per same message

Because we found that the messages were always the same, we were able to take that and create subgraphs using those edges and apply a connected components algorithm.

Sock Puppet Groups

As a result of all of the analysis that we ran on a small sampling, we discovered that what we thought were the most popular brands actually weren’t—our original list had been distorted by bots.

See the image below – the “new” most popular brands barely even appear on the “old” most popular brands list. But they are a much truer reflection of what’s actually popular. This is the information you need.

Brand Popularity Skewed by Bots

After one month, we revisited the identified bot accounts just to see what had happened to them. We discovered:

  • 89% were suspended
  • 2.2% were deleted
  • 8.8% were still serving as bots

Fraud Detection Use Case #3: Circular Payment

A common pattern in financial crimes, a circular money transfer essentially involves a criminal sending money to himself or herself—but hides it as a valid transfer between “normal” accounts. These “normal” accounts are actually fake accounts. They typically share certain information because they are generated from stolen identities (email addresses, addresses, etc.), and it’s this related information that makes graph analysis such a good fit to discover them.

For this use case, you can use graph representation by creating a graph from transitions between entities as well as entities that share some information, including the email addresses, passwords, addresses, and more. Once we create a graph out of it, all we have to do is write a simple query and run it to find all customers with accounts that have similar information, and of course who is sending money to each other.

Circular Payments Graph Technology

Fraud Detection Use Case #4: VAT Fraud Detection

Because Europe has so many borders with different rules about who pays tax to which country when products are crossing borders, VAT (Value Added Tax) fraud detection can get very complicated.

In most cases, the importer should pay the VAT and if the products are exported to other countries, the exporter should receive a refund. But when there are other companies in between, deliberately obfuscating the process, it can get very complicated. The importing company delays paying the tax for weeks and months. The companies in the middle are paper companies. Eventually, the importing company vanishes and that company doesn’t pay VAT but is still able to get payment from the exporting company.

VAT Fraud Detection

This can be very difficult to decipher—but not with graph analysis. You can easily create a graph by transactions; who are the resellers and who is creating the companies?

In this real-life analysis, Oracle Practice Manager Wojciech Wcislo looked at the flow and how the flow works to identify suspicious companies. He then used an algorithm in Oracle Spatial and Graph to identify the middle man.

The graph view of VAT fraud detection:

Graph View of VAT Fraud Detection

A more complex view:

Complex View of Graph Technology and Anomaly Detection

In that case, you would:

  • Identify importers and exporters via simple query
  • Aggregate of VAT invoice items as edge weights
  • Run Fattest Path Algorithm

And you will discover common “Middle Man” nodes where the flows are aggregated

Fraud Detection Use Case #5: Money Laundering and Financial Fraud

Conceptually, money laundering is pretty simple. Dirty money is passed around to blend it with legitimate funds and then turned into hard assets. This was the kind of process discovered in the Panama Papers analysis.

These tax evasion schemes often rely on false resellers and brokers who are able to apply for tax refunds to avoid payment.

But graphs and graph databases provide relationship models. They let you apply pattern recognition, classification, statistical analysis, and machine learning to these models, which enables more efficient analysis at scale against massive amounts of data.

In this use case, we’ll look more specifically at Case Correlation. In this case, whenever there are transactions that regulations dictate are suspicious, those transactions get a closer look from human investigators. The goal here is to avoid inspecting each individual activity separately but rather, group these suspicious activities together through pre-known connections.

Money Laundering and Financial Fraud

To find these correlations through a graph-based approach, we implemented this flow through general graph machines, using pattern matching query (path finding) and connected component graph algorithm (with filters).

Through this method, this company didn’t have to create their own custom case correlation engine because they could use graph technology, which has improved flexibility. This flexibility is important because different countries have different rules.

Conclusion

In today’s world, the scammers are getting ever more inventive. But the technology is too. Graph technology is an excellent way to discover the truth in data, and it is a tool that’s rapidly becoming more popular. If you’d like to learn more, you can find white papers, software downloads, documentation and more on Oracle’s Big Data Spatial and Graph pages.

And if you’re ready to get started with exploring your data now, we offer a free guided trial that enables you to build and experiment with your own data lake.

Related:

  • No Related Posts

Veterans Transitioning to Civilian Careers: Time to Build Your Brand

EMC logo


What is Build Your Brand?

Build Your Brand is a program at Dell that helps our team members and networks build their personal brand presence on LinkedIn so they are confident in representing themselves to the external market. Within Build Your Brand, participants experience a deeper dive into the how-to’s of profile development and receive tips on ways to engage on LinkedIn.

veteran holding hat

LinkedIn, once known almost exclusively as the social media platform used during a job search, has now become a staple networking asset in the tool belt of individuals and organizations around the globe. A few of the most utilized functions allow users to:

  • Network
  • Identify and recruit top talent
  • Join groups based on industries and interests
  • Connect with colleagues and professionals
  • Publish and share thought leadership

Today, LinkedIn’s brand has evolved into a platform with over 500 million users, a sizable proportion of which log on multiple times a week. Long progressed from that account checked only once or twice a month, this tool allows individuals to share their thought leadership, find like-minded connections and make a brand for themselves.

Army Veterans at Ft. Hood in a classroom setting

A Tough Transition

At Dell, we want to empower our employees to share their thought leadership with the world and provide the resources to do so. We believe that everyone has a story to tell and their own brand to promote.

In military life, however, this is usually not a categorical requirement. A soldier’s brand is most heavily represented by their comrades or respective branch of the military. When transitioning out of a military role, this missed opportunity creates additional stress and adds barriers returning to civilian life. This already overwhelming process leaves many feeling at a loss, especially when the numerous skills gained over years of service are rarely found listed on a job description.

How We Help

Dell aims to help our veterans with the transition into a civilian career by teaching the basics of building your own brand. Last month, Dell representatives drove to Fort Hood to present on the importance of building a personal brand and continuously growing a professional network. One of the presenters, Army veteran and member of the Dell Commercial Client Product Group, Dan Ireland, was thankful to give back to his fellow veterans after he personally navigated the transition himself a few years back:

It’s really powerful when as representatives of one of the world’s most admired tech companies we’re able to provide insights and actionable advice to transitioning service members.

Through offering resources such as Build Your Brand, a military careers page, a MOS translation generator and individuals such as Lou Candiello with a role dedicated to supporting military placement; Dell is doing its best to make a difference for those going through this life change.

 “I attended the brown bag and was very impressed at Dan’s passion for taking care of soldiers and briefing them on tricks of the trade for them in setting up a LinkedIn profile.”

– John H. Vella IV; G6, Operations Officer

“I never knew you could do so much with LinkedIn. I am a firm believer that intentionally connecting with the right people can make a huge difference in finding the right job.”

–  Chelsea Williams; Senior Military Intelligence Officer

Veterans and military groups are consistently among the most active groups on LinkedIn. If these individuals were aware of the resources, such as Build Your Brand, and the importance of leveraging their networks, they would have the ability to take their career anywhere. Through increasing awareness of the many tools and opportunities veterans can capitalize on, we are helping make that transition into civilian life smoother while encouraging individuals to be confident in what they bring to the table.

“Follow” the Dell LinkedIn company page



ENCLOSURE:https://blog.dell.com/uploads/2017/04/steve-stoll-hat_1000x500.jpg

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