3 types of fraud graph analytics can help defeat

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Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. In this presentation, we examine three types of fraud graph analytics can help investigators combat. Blog post: https://linkurio.us/blog/3-fraud-graph-analytics-help-defeat/

Data & Analytics

3 types of fraud graph
analytics can help defeat

1. Insurance fraud
2. Credit card fraud
3. VAT fraud

Detecting fraud is about connecting the dots

But today’s applications fall short on more complex analysis that would imply
several levels of relationships or data types
RELATIONAL
DATABASE
❌

In graph databases, connections are stored as 1st-class citizens, making it an
interesting model for investigations.
GRAPH
DATABASE
✔

In insurance fraud, people team up to put together a fake road traffic accident
claim reporting light personal injuries.
Insurance
companyInjury
claim
Injury
claim
Injury
claim
Staged
accident
Staged
accident
Staged
accident
Fraudster
ring

The graph approach brings data from various sources under a common model.
Investigators look at all the data together, instead of isolated data silos.
Graph visualization of two customers (blue nodes)
file three claims (green nodes). We identify a
network of three customers connected through
personal information such as phone (brown nodes),
email (pink nodes) with the same lawyer (green
node) involved every time. It is likely they are
recycling stolen or fake identity to file fraudulent
claims.

Payment card fraud takes the form of criminals getting ahold of credit card
information and proceeding to create unauthorized transactions.
Compromised
ATM
Counterfeit
card
Counterfeit
card
Fraudster
Counterfeit
card
Shop
Shop

Graphs help identify the common point of compromise by highlighting the
common links, no matter how large the dataset is.
Graph visualization to identify a common point of
compromise ATM: Clients (blue nodes), report
fraudulent purchases (orange nodes). We can
identify through connections the common ATM
(purple) where they made a withdrawal before the
card fraud.

Carousel fraud, or VAT fraud, is the theft of VAT collected on the sale of goods
initially bought VAT-free in another jurisdiction.
Company A Company B
Disappear
Company C
VAT refund
€ €
€
Sell goods
VAT free
Sell goods,
charges VAT

Organizations use Linkurious Enterprise to
fight fraud across activity sectors:
insurance, banking, law enforcement or
financial administrations.

Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.

Running head HOW TO AVOID INTERNET SCAMS AT THE WORKPLACE 1 .docx

wlynn1

Running head: HOW TO AVOID INTERNET SCAMS AT THE WORKPLACE 1 How to avoid internet scams at the workplace Christophe Bassono CIST3000: Advanced Composition IS&T Amanda L. Gutierrez, M.S. & M.A. UNO-Fall 2018 HOW TO AVOID INTERNET SCAMS AT THE WORKPLACE 2 Online Fraud: How to Avoid Internet Scams in the Workplace This section outlines how the researcher envisions presenting the report. The outline demonstrates the different sections in which the report will be broken into and the information that will be contained in each section Introduction Definition Online fraud refers to deceitful schemes that are done using the internet. Online fraud may come in the form of financial theft, identity theft or a combination of both. History of Online Fraud An influx of online fraud began to be experienced in the 1990s with the increased technology use and e-commerce. In the beginning, online fraud was done by using the names of famous celebrities of the time to commit internet crimes. Over time, more technical and sophisticated plans were developed such as creating card-generator applications with real credit card numbers, setting up dummy merchant websites and mass identity theft. Today, despite attempts by various governments to regulate and mitigate online fraud, more sophisticated online fraud schemes have been established ranging from credit card fraud to phishing, hacking, and identity theft (Saeger & Probert, 2015). In the recent past, computer fraud has evolved through a series of advancements outplaying the traditional security defenses such as the two-factor authentication, antivirus, and SSL encryption in the process. Zeus and SpyEye are the most common attack tools used by hackers since they support the gathering of vast volumes of extremely sensitive authentication data. It has been established that no single application is immune to attacks and the malicious attackers are focusing more on online banking accounts because they offer HOW TO AVOID INTERNET SCAMS AT THE WORKPLACE 3 most direct payoff. Online fraud is based on three core technologies: the botnet controllers capable of handling hundreds of thousands of bots, highly effective data collection, and sophisticated Trojans that are updateable. Form grabbing for PCs running IE/Windows has been a simplified approach for fraud. The technique helps attackers to extract data within browsers. The deployment of form grabbing on compromised PCs allowed hackers to obtain numerous numbers of online bank account IDs and passwords. The password-based authentication was termed no longer safe for online banking prompting the introduction of two-factor authentication (Mellinger, 2011). Nevertheless, criminals still found the loophole that helps them to challenge the security of two-factor authentication through web injects. Malicious attackers that promote online fraud have created various techniques.

TestTestTestTestTestTestTestTestTestTestTestTestTestTestTestTest

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Online Transaction Fraud Detection using Hidden Markov Model & Behavior Analysis

CSCJournals

Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.

Insurance today is considered both as a form of security and investment. It gives a sense of assurance to its client- the courage to mitigate unforeseen mayhem in life. But with the influx of fraudulent activities and felony across various industries, the insurance sector stands to be no exception. One of the ways that miscreants try to get money from insurance companies is through Insurance Claims Fraud

Taming tax frauds new digital frontier

Capgemini

While innovative new technologies have been very efficient in combating traditional fraud, our research has found that digital technologies are also giving rise to new types of digital tax fraud: the increase in the number of e-filings of tax returns across geographies is driving new types of fraud using identity theft as the basis. Another type of fraud taking shape is Zapping – using software programs to automatically skim cash from electronic cash registers (ECR) or point of sale systems. Similarly, the growing usage of third-party payroll processors is opening up a whole new avenue of fraud where unscrupulous processors siphon off taxes due to the state. Our analysis of these new digital tax frauds shows that inaction is not an option for tax authorities. We have modelled the evolution of tax fraud, taking into account new incidents of fraud enabled by digital technologies. Our findings are sobering for tax authorities. In a scenario where tax authorities continue to fight new tax fraud with conventional tools, we estimate digital tax fraud in the US will rise from $32 billion to $49 billion by 2020. To combat this staggering scale of fraud, conventional methods are too slow for the digital age. Tax authorities must move away from an incremental, piecemeal approach to a much more comprehensive transformative line of attack with a long-term vision, roadmap and multifaceted solutions involving people, processes and technology. What is the impact of inaction of tax authorities to rein in new types of digital tax fraud? How can analytics be used as an effective weapon to fight against digital tax fraud.

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Insurance Fraud (Business Decision Making Project - Fina.docx

mariuse18nolet

Insurance Fraud (Business Decision Making Project - Final Collaboration) Running head: INSURANCE FRAUD (BUSINESS DECISION MAKING PROJECT - 1 INSURANCE FRAUD (BUSINESS DECISION MAKING PROJECT - 5 Insurance Fraud (Business Decision Making Project - Final Collaboration) Insurance fraud is something that affects all aspects of insurance. Fraud, according to merriam-webster.com, is “using dishonest methods to take something valuable from another person.” (2015) in this presentation, we will discuss who is affected by fraud, different types of fraud, why fraud is an issue, and how we can help stop fraud. Consumers, insurance companies, innocent “victims” in accidents, police and district attorneys are all affected by fraud of different types. There are many reasons why insurance fraud is an issue. Insurance companies pay out on fraudulent claims, which then cause premiums to increase for consumers. A study conducted by the Coalition Against Insurance Fraud shows that consumers see an increase of between 13% and 18% annually in premiums as a result of insurance fraud. (insurance-fraud.org, 2015) This means that of every $100 dollars consumers pay in premium, between $11.50 and $15.25 of this is due to insurance fraud. Victims in car accidents can be affected because the insurance company may not pay a valid claim due to fraudulent actions by one or both parties in a loss. Police and district attorneys have to use valuable resources to combat against fraud. The Top Questionable Insurance Claims With insurance fraud being so prevalent the Insurance Information Institute released statistical data on the top questionable insurance claim categories that have been increasing year over year. Personal Property claims take top billing when it comes to fraud claims. Included in this section is homeowners insurance and renters insurance. Not only in the residential world do we have claims but in the business world as well with Commercial property claims coming in second place and workers compensation claims comes in a strong third place. These claims seem to be easier to get away with fraud due to the lack of property or injuries that can be claimed. Data analysis The total cost of in the United State is estimated by $ 40 Billion a year which cuts the insurers profits and limits their ability to offer consumers competitive premiums. The data analysis shows that insurance fraud among serious citizen. In addition, and according to the Insurance Fraud organization (2015), the auto insurance fraud $4.8 billion to $6.8 billion in excess payments to auto injury claim. Worker's compensation insurance fraud including employees misclassified by employers increased from 106,000 workers to more than 150,000 workers, schemes that stole $489 million in workers compensation premiums. In the Health care industry, data shows fraud account is increasing by 19% annually as of 2007. The fraud increased from $600 to $800 Billion in waste in the U.S hea.

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Survey on Credit Card Fraud Detection Using Different Data Mining Techniques

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In today's world of e-commerce, credit card payment is the most popular and most important mean of payment due to fast technology. As the usage of credit card has increased the number of fraud transaction is also increasing. Credit card fraud is very serious and growing problem throughout the world. This paper represents the survey of various fraud detection techniques through which fraud can be detected. Although there are serious fraud detection technology exits based on data mining, knowledge discovery but they are not capable to detect the fraud at a time when fraudulent transaction are in progress so two techniques Neural Network and Hidden Markov Model(HMM) are capable to detect the fraudulent transaction is in progress. HMM categorizes card holder profile as low, medium, and high spending on their spending behavior. A set of probability is assigned to each cardholder for amount of transaction. The amount of incoming transaction is matched with cardholder previous transaction, if it is justified a predefined threshold value then a transaction is considered as a legitimate else it is considered as a fraud.

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Unit 8 DB: Identity Theft Article A Chief’s View:Identity Theft:Resources for Police By Stephen White, Chief of Police, Doylestown Township, Pennsylvania, and Monique Einhorn, Attorney, Identity Theft Program, Federal Trade Commission, Washington, D.C. Identity theft seems to be on everyone's radar screen. A 2003 study by the Federal Trade Commission found that identity theft affects almost 10 million consumers a year. Most states have enacted their own identity theft laws to assist law enforcement fight this crime. When Congress criminalized identity theft in October 1998, it directed the Federal Trade Commission (FTC) to establish a national program that included a centralized complaint and education service for victims of identity theft. Today, the FTC's Identity Theft Program attacks the crime on three fronts: it coordinates victim assistance and education efforts; it assists law enforcement by providing investigative resources and facilitating information sharing; and it promotes prevention efforts and best practices through industry outreach. These resources can make it easier for police to work with victims and investigate the crime. (continued on page 38) What's New The Fair and Accurate Credit Transactions Act (FACT Act), a new federal law, provides identity theft victims with important new rights and remedies. Although the identity theft provisions are mostly directed at victims' recovery, they affect how police officers deal with this pernicious crime. The law makes police reports more important than ever as a tool to help victims recover. That is because the new rights and remedies-such as blocking fraudulent trade lines on credit reports and obtaining the suspect's credit application-are available only to victims who present a police report to help prove that they are victims of fraud. One provision of the FACT Act that simplifies the investigation of identity crime relates to the documents used to open fraudulent accounts. For example, if a company extends credit to a suspect using the victim's personal information, the victim now can obtain the identity theft related transaction records at no charge from that company-but only if the victim provides a copy of a police report and other required documentation. Law enforcement also benefit because investigators can get these documents without a subpoena if a victim authorizes, in writing, that the business send a copy of the records directly to the officer. Police reports are the first step in helping identity theft victims clear their names and recover from identity theft. Here's what happens after a victim obtains a police report: · Credit bureaus must block the reporting of inaccurate information identified by the victim as resulting from identity theft. · Businesses where fraudulent accounts were opened must give victims (and police, at the victim's written request) copies of applications and business records relating to transactions that the victim identifies a ...

Secure Payments: How Card Issuers and Merchants Can Stay Ahead of Fraudsters

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Internet Crime Report 2019 by IC3.pdf

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IC3 2019 Internet Crime Report

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The FBI is the lead federal agency for investigating malicious cyber activity by criminals, nation-state adversaries, and terrorists. To fulfill this mission, the FBI often develops resources to enhance operations and collaboration. One such resource is the FBI’s Internet Crime Complaint Center (IC3) which provides the public with a trustworthy and convenient mechanism for reporting information concerning suspected Internet-facilitated criminal activity. At the end of every year, the IC3 collates information collected into an annual report. Credit is due to all original authors and no financial gain was made from the blog, Simply sharing an interesting story for educational purposes,

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一比一原版(NYU毕业证)纽约大学毕业证成绩单

ewymefz

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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

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3 types of fraud graph analytics can help defeat

1. 3 types of fraud graph analytics can help defeat

2. 1. Insurance fraud 2. Credit card fraud 3. VAT fraud

3. Detecting fraud is about connecting the dots

4. But today’s applications fall short on more complex analysis that would imply several levels of relationships or data types RELATIONAL DATABASE ❌

5. In graph databases, connections are stored as 1st-class citizens, making it an interesting model for investigations. GRAPH DATABASE ✔

6. 1) Insurance fraud

7. In insurance fraud, people team up to put together a fake road traffic accident claim reporting light personal injuries. Insurance companyInjury claim Injury claim Injury claim Staged accident Staged accident Staged accident Fraudster ring

8. The graph approach brings data from various sources under a common model. Investigators look at all the data together, instead of isolated data silos. Graph visualization of two customers (blue nodes) file three claims (green nodes). We identify a network of three customers connected through personal information such as phone (brown nodes), email (pink nodes) with the same lawyer (green node) involved every time. It is likely they are recycling stolen or fake identity to file fraudulent claims.

9. 2) Credit card fraud

10. Payment card fraud takes the form of criminals getting ahold of credit card information and proceeding to create unauthorized transactions. Compromised ATM Counterfeit card Counterfeit card Fraudster Counterfeit card Shop Shop

11. Graphs help identify the common point of compromise by highlighting the common links, no matter how large the dataset is. Graph visualization to identify a common point of compromise ATM: Clients (blue nodes), report fraudulent purchases (orange nodes). We can identify through connections the common ATM (purple) where they made a withdrawal before the card fraud.

12. 3) VAT fraud

13. Carousel fraud, or VAT fraud, is the theft of VAT collected on the sale of goods initially bought VAT-free in another jurisdiction. Company A Company B Disappear Company C VAT refund € € € Sell goods VAT free Sell goods, charges VAT

14. Graphs help identify the common point of compromise by highlighting the common links, no matter how large the dataset is. Graph visualization to identify chains of transactions in VAT fraud: Companies (blue nodes) and their parent organizations (flags nodes) sell goods VAT-free and collect back VAT through complex layers of sales between EU and non-EU countries.

15. Organizations use Linkurious Enterprise to fight fraud across activity sectors: insurance, banking, law enforcement or financial administrations.

16. www.linkurio.us contact@linkurio.us

3 types of fraud graph analytics can help defeat

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