Table of Contents
- What is Big Data Anyway?
- How Can Big Data Protect Against Identity Theft?
- How Can Big Data Make Identity Theft Easier?
These days, it’s hard to avoid existing on the internet. Whether you’re connecting with friends over apps like Facebook or Instagram, catching a ride with Uber or Lyft, or shopping on Amazon, there are plenty of opportunities for you to leave a digital footprint online.
In fact, as long as you participate in the modern world, it’s hard to avoid revealing at least a little personal information to some entity that exists on the internet — even if that isn’t where you originally shared the information. So what happens to all this information about you? How is it stored? How is it being used? And how can you be sure that it’s safe from identity thieves?
What is Big Data Anyway?
As the name suggests, “big data” refers to large sets of information, usually regulated and managed with complex algorithms. The best way to think about big data is to break it down into three Vs:
- Variety: Big data is characterized by a wide variety of data types. It can include simple text, audio recordings, financial transactions (credit card, payments), and more.
- Volume: Big data is massive! One defining feature of big data is the sheer volume of it. Organizations like Amazon can be said to use it, given the sheer number of transactions that they process.
- Velocity: Speed is key to big data. Without the ability to rapidly recall and process information, the volume involved in big data would make it unwieldy and impractical.
Since big data involves data sets that are so large and information that is so varied, it requires new ways of understanding and managing data. For this reason, the concept of big data is often brought up alongside discussions of machine learning and artificial intelligence. As our data sets become more complex, simple processing algorithms can’t manage it very well. Instead, we need advanced AIs that can delve into big data and return insights.
Examples of Big Data
That’s enough theory. Let’s take a look at some instances of big data in action:
- Healthcare: Today, patient records, prescription information, and treatment plans are often stored digitally. By deploying machine learning to this big data, healthcare providers can derive insights that tell us more about the diseases that afflict patients and help to improve patient care.
- Banks: Banks and other financial institutions process countless financial transactions each day. Normally, this data set would be too massive to analyze using conventional methods. However, by approaching it as big data, banks can discover trends in customer activity to boost satisfaction and even spot fraud as it’s happening.
- Education: Almost 20 million students are estimated to have attended college in 2018. With that many students, it can be hard to identify individuals within the crowd. However, big data methods allow educators to evaluate the data, both from a holistic perspective and by zooming in on individual student performance. This information can be used to improve education techniques, streamline course offerings, and spot at-risk students before their academic performance becomes problematic.
- Retail: Perhaps the most talked-about use of big data is in retail. There is some concern amongst privacy advocates that retailers are using big data collected from a variety of sources to hone their marketing strategies. For example, big data is the reason why you might see heavily tailored advertisements on social media, that seem to respond directly to your interests and needs.
How Can Big Data Protect Against Identity Theft?
Big data has many potential applications. One of the most exciting developments that it can bring is in protecting against identity theft, especially when it’s theft of a consumer’s financial information.
Machine Learning Can Detect Unusual Habits
The sheer volume of financial activity on even a single person’s accounts can be impractical to track and analyze through traditional methods. However, by using machine learning, we can teach programs to recognize the hallmarks of a consumer’s data. Such as mostly making small purchases, shopping online or within a specific area, or buying mostly within a few domains, such as clothing, groceries, and outdoor sporting goods.
With big data, financial institutions can flag suspicious activity on a consumer’s account for human analysis. If someone who usually spends their money on books in Seattle suddenly becomes interested in a new car in Miami, an AI can alert a human employee to examine the case further, potentially catching an identity thief in their tracks.
Many fraudsters commit their crimes using the very same financial systems as their victims. As we noted above, big data can analyze the financial information of potential victims of identity theft for irregularities. However, it can be used to monitor people who would potentially abuse these systems in order to commit fraud against others.
Monitoring The Internet
Whenever a person’s identity is stolen or someone’s personal information is shared somewhere on the internet, that represents a potential security breach. Normally, it would be difficult to trace individual bits of information back to a single source and identify the breach. However, with big data, it is possible to keep eyes on places online where data is shared, spotting security breaches as they happen.
Sensitive data at risk to be breached:
- Credit Cards & Debit Card
- Social Security Number
- Driver’s license numbers
- Social Media information
Gaining access to this data creates opportunities for multiple types of identify theft.
Access to Information
Big data allows both law enforcement and private individuals to pull large amounts of information down from the cloud with incredible speed. For example, a car dealership could instantly access the history of a vehicle being sold to them using a VIN decoder. With this information, they could confirm that they are participating in a legitimate transaction with the actual owner of the vehicle.
Big data methods allow for the analysis of things like fingerprints or facial features. These kinds of security locks can only be undone by the true owner of a device, making it harder for would-be cybercriminals to crack into our phones and computers. Without the machine learning inherent to big data, this kind of instant security check would not be possible.
How Can Big Data Make Identity Theft Easier?
In spite of the benefits of big data and the ways that it makes identity theft more difficult, many privacy advocates are concerned that our reliance on big data has actually made some forms of identity crime easier to commit.
More Information Means More to Steal
Big data only works with large quantities of information. However, with so much information — from our financial transactions to our vacation plans — stored online, there is some concern that big data has simply produced more targets for identity thieves. The mere fact of storing all this information online create opportunities for fraud which, in turn, creates the need for big data techniques to fight fraud.
More Data in One Place
Thanks to big data, there is more information available online than ever before. Even more than that, though, a great deal of information is being stored together. For example, a consumer’s entire credit and debt history can be collected and managed by a single credit agency. Unfortunately, when an agency gets hacked — as Equifax was compromised in 2017 — the criminals now have access to a breadth of information about a single consumer, rather than just little bits and pieces.
Hackers Can Use Machine Learning Too
Machine learning can be used to spot fraud as it’s happening, but these same technologies can be deployed by criminals. Advanced AI can give hackers a leg up on data security, making it easier for them to commit data breaches and gain access to private consumer information. Unfortunately, big data is a two-way street in this regard.
Biometric Data Can’t be Changed
Although biometric security is difficult to breach, it is also fixed. In the event of a security breach, someone cannot change their face or their fingerprint to update their security protocols, in the same way, that one might update a password after an account breach. Thus, if an identity thief could break a person’s biometric security once, there’s not much that a consumer can do to prevent it from happening again.
Ultimately, the relationship between identity theft and big data is complex. Big data gives law enforcement and financial institutions a unique set of tools for catching identity thieves. However, it gives these same fraudsters new opportunities to steal consumer information that did not exist before the era of big data.