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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Kerala Blockchain Academy first Indian academy to get Hyperledger membership

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What Makes Your Brand a Magnet for Great Talent?

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The battle to attract top talent in the technology industry has been raging worldwide for years. As Meghan Biro, an author and CEO of TalentCulture Consulting Group, puts it, “The talent war is rampant in the tech industry, and engineers are now attracted not only by financial prospects, but also because of a brand’s name and reputation. When they join these companies, the workplace culture is so strong that every little detail embodies what the company stands for. This is what makes all the employees feel like they belong to a family, not just a business.”

Dell EMC employees outside at the company CX Day event in Hopkinton

At Dell Technologies, this is in our DNA. We know people want to work for a great business, and also for a great place to work. When it comes to our brand reputation, and more importantly to our team members and the culture that so strongly binds and unites us, our business serves as its own talent magnet. In fact, LinkedIn just recognized Dell Technologies as a top company where the U.S. wants to work nowLinkedIn Top Companies logo

We’re honored to be on LinkedIn’s list of top companies for being a respected brand and innovator, and attracting consistently heavy interest from job seekers. In addition to the U.S. market, Dell Technologies also made the coveted lists for the United Kingdom, Germany and Australia for 2018. It’s also in line with research recently carried out by Indeed, which ranked Dell at the very top of its list for Ireland’s best places to work list.

I wrote about building an exceptional brand after Dell Technologies was recognized among Fortune’s Most Admired Companies for 2018, and, of course, how market-leading products and services are instrumental to this endeavor. But who makes that happen? Our people. Our people embody our Culture Code, which defines our values and is made real every day by how we work and lead. Our people are the heart and soul of Dell Technologies’ success and they drive the industry-leading, award-winning innovation behind our products and services, while always putting our customers first. And it’s our people who are the reason we’re being recognized again as a top company by LinkedIn.

The 2018 lists from LinkedIn represent the companies where professionals most want to work today, based on the actions of LinkedIn’s 546 million professionals (with 146 million in the U.S. alone), including a brand’s reputation, reach and engagement with job seekers, and also how well a brand retains its new hires, a critical measure of success. The passion and dedication of our people across the company is truly inspiring and helps serve as a magnet for more top talent in search of a great place to work. I’m happy to say we’re hiring. Learn more about our career opportunities today.



ENCLOSURE:https://blog.dell.com/uploads/2018/03/dell-emc-cxday_1000x500.jpg

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Not able to block FB and Line application in IPAD(mobile users)

I need a solution

Hi Team,

I can’t block facebook application and Line app in the IPAD users through proxy.

Facebook:

I have made a rule “Request url” facebook.com and  Control applicaiton blocked both facebook app and facebook plugin”

But still user say they can see the new feeds but cant play streaming video.–>  need to fix this.(need to bock facebook app in mobile)

Line application:

We have bocked the destination IP range 203.104.160.0/20 

But still the mobile users can access this app.

Thanks,

Ram,

0

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Building Your Brand: Why It’s Important

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Did you know that before interviewing with a company, candidates look at the LinkedIn profiles of the company’s team members with similar jobs to learn what projects and responsibilities they have? They take note of the media, articles and posts the company’s team members have shared.

silhouette of person standing in front of windows looking out over a large city

Photo by Alex Knight on Unsplash

The influencer landscape is changing – institutional trust is declining and people want to hear from individuals like you and me. Candidates now have the power to be investigators and reporters with technology and social media at their fingertips. Therefore it is imperative that team members develop their personal brand as it is not only a reflection of them, but their company as well.

LinkedIn is the world’s largest professional network, with 500 million members worldwide and counting. It’s a great place to build your personal brand presence with more creativity than résumés allow. Building your brand on LinkedIn not only helps you establish a personal brand, it also gives the external market insight into your impactful work and your company’s culture.

With that in mind, Dell’s Employment Brand team designed a “Build Your Brand” training program for our team members and external partners to help them build their personal and professional brands. After all, people tend to want to work with people they know and people they trust.

college classroom

From the Ground Up

Build Your Brand teaches Dell hiring leaders and individual contributors specific actions they can take to enhance their personal brand, harness the power of social media, build and develop rewarding networks, and learn best practices on LinkedIn.

At Dell, we believe that our team member’s LinkedIn profiles should represent their personal brand. LinkedIn is now more than just a place to put your work history – it’s a living, breathing page that gives the external market insight into the incredible talent that we have at Dell. We empower our team members with tools and resources so that they are confident when sharing their life at Dell.

Building Your Brand is not only easy, it’s encouraged. Build Your Brand is divided into three stages:

  1. BUILD: A home starts with a solid foundation. Here, we’ll walk you through the process of building your brand from the ground up. This is where you’ll learn things like how to write a summary section and what your profile picture should look like. We’ll give you the blueprint to make your new digital home a success that’s able to stand the test of time.
  2. FURNISH: Now it’s time to settle in. We’ll need to add some bells and whistles, and get the ball rolling on making your personal brand something welcoming, and your digital home a place worth showing off.
  3. INVITE: To truly make your digital home complete, it’s all about creating experiences for your community to be a part of. Share. Speak. Start a dialogue! That’s how you’ll scale your personal brand—and hopefully, your career—to all-new heights.

Our Employment Brand team created the Build Your Brand training program with our internal team members in mind, but we also have external versions to share with the community. If you are looking to bring Build Your Brand to your college campus, veteran’s event, or professional workshop – contact a Dell recruiter today!

“Follow” the Dell LinkedIn company page

Be sure to “Follow” the Dell LinkedIn company page to easily learn, discuss and share company news. Our presence on LinkedIn showcases what Dell is like as an employer and who we are as individual team members.



ENCLOSURE:https://blog.dell.com/uploads/2018/02/alex-knight-181471-unsplash_1000x500.jpg

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