Last Updated on by SCARS Editorial Team

SCARSSCARS SCARS - Society of Citizens Against Relationship Scams Inc. A government registered crime victims' assistance & crime prevention nonprofit organization based in Miami, Florida, U.S.A. SCARS supports the victims of scams worldwide and through its partners in more than 60 countries around the world. Incorporated in 2015, its team has 30 years of continuous experience educating and supporting scam victims. Visit www.AgainstScams.org to learn more about SCARS.™ Guide: What Is Big Data?

Big Data – Big ScamsScams A Scam is a confidence trick - a crime -  is an attempt to defraud a person or group after first gaining their trust through deception. Scams or confidence tricks exploit victims using their credulity, naïveté, compassion, vanity, irresponsibility, or greed and exploiting that. Researchers have defined confidence tricks as "a distinctive species of fraudulent conduct ... intending to further voluntary exchanges that are not mutually beneficial", as they "benefit con operators ('con men' - criminals) at the expense of their victims (the 'marks')". A scam is a crime even if no money was lost. Databases

If the pile of manure is big enough, you will find a gold coin in it eventually. This saying is used often to explain why anyone would use big data. Needless to say, in this day and age, the piles of data are so big, you might end up finding a pirate’s treasure. In our case we look for scammers – the real scammers in the noise of all the reports filed.

How Big Is The Pile?

But when is the pile big enough to consider it big data? Per Wikipedia:

“Big data is data sets that are so big and complex that traditional data-processing application software is inadequate to deal with them.”

As a consequence, we can say that it’s not just the size that matters, but the complexity of a dataset. The draw of big data to researchers and scientists, however, is not in its size or complexity, but in how it may be computationally analyzed to reveal patterns, trends, and associations.

When it comes to big data, no mountain is high enough or too difficult to climb. The more data we have to analyze, the more relevant conclusions we may be able to derive. If a dataset is large enough, we can start making predictions about how certain relationships will develop in the future and even find relationships we never suspected to exist. For example, we might find a financial manager in Malaysia that is managing the proceeds from scams in the Ivory Coast!

The Treasure

We mentioned predicting the future or finding advantageous correlations as possible reasons for using big data analysis. Just to name a few examples, big data could be used to set up profiles and processes for the following:

  • Stop terrorist attacks by creating profiles of likely attackers and their methods.
  • More accurately target customers for marketing initiatives using individual personas.
  • Calculate insurance rates by building risk profiles.
  • Optimize website user experiences by creating and monitoring visitor behaviorBehavior   Behavior / Behavioral Actions Otherwise known as habits, behavior or behavioral actions are strategies to help prevent online exploitation that target behavior, such as social engineering of victims. Changing your behavior is the ONLY effective means to reduce or prevent scams. profiles.
  • Analyze workflow charts and processes to improve business efficiency.
  • Improve city planning by analyzing and understanding traffic patterns.
  • Discover how a local African University has been co-opted by scammers, or how a new corporate scammerScammer A Scammer or Fraudster is someone that engages in deception to obtain money or achieve another objective. They are criminals that attempt to deceive a victim into sending more or performing some other activity that benefits the scammer. group has begun operations in Mauritius.

Beware of Apophenia

Apophenia is the tendency to perceive connections and meaning between unrelated things. What statistical analysis might show to be a correlation between two facts or data streams could simply be a coincidence. There could be a third factor at play that was missed, or the data set might be skewed. This can lead to false conclusions and to actions being undertaken for the wrong reasons.

For example, analysis of data collected about medical patients could lead to the conclusion that those with arthritis also tend to have high blood pressure. When in reality, the most popular medication to treat arthritis lists high blood pressure as a side effect. Remember the old research edict: correlation does not equal causation.

In statistics, we call this a type I error, and it’s the feeding ground for many myths, superstitions, and fallacies.

This is a word most victims should remember since the tendency to make false assumptions is profound.

The Researchers

As more and more data becomes digitized and stored, the need for big data analysts grows. A recent study showed that 53 percent of the companies interviewed were using big data in one way or another. Some examples of use cases for big data include:

  • Data warehouse optimization (considered the top use case for big data) (such as for scamScam A Scam is a confidence trick - a crime -  is an attempt to defraud a person or group after first gaining their trust through deception. Scams or confidence tricks exploit victims using their credulity, naïveté, compassion, vanity, irresponsibility, or greed and exploiting that. Researchers have defined confidence tricks as "a distinctive species of fraudulent conduct ... intending to further voluntary exchanges that are not mutually beneficial", as they "benefit con operators ('con men' - criminals) at the expense of their victims (the 'marks')". A scam is a crime even if no money was lost. reports numbering in the millions)
  • Analyzing patterns in scammer language used to identify scriptwriters
  • Sports statistics and analysis; sometimes the difference between being the champion or coming in second comes down to the tiniest detail
  • Prognosis statistics or success rates of particular medications can influence a doctor’s recommended course of treatment; an accurate assessment of which could be the difference between life and death
  • Selecting stocks for purchase and trade; quick decision-making based on analytical algorithms gives traders the edge

We use big data in the form of anonymous data gathered from reports to monitor active threats. Viewing these data sets allows us to see trends in scams development, from the types of scams that are being used in the wild to the geographic locations of attacks.

From these data, we’re able to draw conclusions and share valuable information with the public and our data feed recipients in government and law enforcement or website operators, in report forms, such as our annual CybercrimeCybercrime