The Role of Data Science in Preventing Fraud | R-bloggers

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The Role of Data Science in Preventing Fraud, Since about 15 years ago, data mining has been dubbed the “new oil rush,” yet the techniques for processing the unprocessed, unrefined data and the devices we can power with it are still being developed.

Many e-commerce businesses are accustomed to using the information they gather from incoming traffic for purposes like customer segmentation and targeted advertising.

These suggestions are undoubtedly at the top of the profit yield checklist, but making a sizable profit also requires minimizing losses due to fraud, a problem that becomes worse every year.

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Whatever security service you choose, the layers of security it offers will begin with data. In general, the information that any e-commerce website wants to gather will be the first set of data points that a fraud solution wants to examine.

The user may choose to actively contribute to the digital market by choosing to offer their own information when asked.

Examples that are frequently used include opening an account on a website or subscribing to a newsletter. This typically produces information like as,

Alternatively, user information may be collected passively without the user’s conscious, informed participation.

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How people use the website, including how much time they spend there and other behavioral biometric information

Device fingerprint, or identifying information about the device used to connect to the website

These data pieces won’t likely reveal anything about a single consumer on their own.

However, a business that has access to some or all of this data is in a good position to have a solid understanding of how trustworthy its visitors are.

These data elements can be used by fraud software to enrich them, creating a completely fleshed-out digital profile out of a rather anonymous data skeleton.

Almost all fraud mitigation strategies that use identity verification to limit loss will rely significantly on data enrichment, or the expansion of known data points to include associated more beneficial data points.

By using this technique, a single (yet significant) piece of information, such as a phone number, may result in social media posts, images, and friends and family. There are numerous methods for gathering this extended data.

Closed-source data, often known as personally identifiable data, is information that a user voluntarily provides as part of an onboarding or registration process and that is not publicly available online.

Another type of closed-source data is proprietary databases containing user data that have been aggregated.

Large databases are frequently used by fraud prevention programs to cross-reference incoming traffic.

Such databases could include information on historically trustworthy or dishonest individuals, fraudulent transaction activity, reputation data, or even credit history.

Some businesses that use proprietary databases estimate that they have billions of data reference points.

The term “OSINT data,” which stands for “Open Source Intelligence” refers to a collection of information that can be gathered from publicly available sources.

Examples of such information include accounts and registrations connected to an email or phone number, pictures and posts from social media, traditional journalistic sources, events of public records like marriages or arrests, geolocation data, and much more.

The fraud program now has a user profile that is a lot easier, and much more conclusive, to analyze when the initially collected data points are exposed to this form of enrichment.

Each user receives a fraud score once the profile is carefully examined and evaluated. Connecting over a VPN or other potential fraud signs raises the score.

Most solutions have the option to either automatically halt the user’s progress after a certain threshold is reached or to escalate the case to a human counterpart.

Implementing a proactive anti-fraud campaign, which is the most effective sort, includes defining your company’s risk tolerance threshold.

The more information you know about your own business, the more secure you can make it, much like with the fraudsters hiding under your floorboards.

It is crucial to identify clear objectives early on, such as stopping ATO assaults, as well as to clean your data and label it appropriately.

This kind of data preparation is essential for the machine-learning algorithms that underpin the AI of practically all fraud solutions in terms of preventing fraud.

Regardless of the model on which they are based, these algorithms need to be taught to provide correct results for a certain organization.

Training teaches the program how to distinguish between legitimate consumers and fraudsters in your system, improving the machine’s ability to distinguish between acceptable behavior and suspicious outliers.

Trusting machine learning algorithms to operate independently without training is a dangerous move, yet a well-trained system may require very little human supervision, freeing up resources.

When a person first visits your e-commerce website, they sign up for a new account and start shopping.

Since they entered a genuine phone number, email address, name, and location for each field on the registration form, their user data appears to be real.

Then, just in case, your fraud detection software kicks in. The software finds that this user’s phone number is unconnected to any social network, which is quite unusual in 2022, and that their email address looks to be new by running lookups on the OSINT data connected to the provided credentials.

The majority of fraud stacks can be configured to identify such an atypical user as possibly suspicious, and their journey can be paused until it is escalated to manual review, even though this individual may simply be social network agnostic.

A manual reviewer from the specialized fraud team intervenes to examine this user in more detail. Despite having little online activity, the reviewer is prone to classify this individual as a false positive for fraud at first.

They choose to zoom out and look at the data trend analysis provided by the software since they are still on the fence about anything. Analyses reveal a different story.

The software notices that this user’s device fingerprint is remarkably similar to that of 70 other users, drawing an automatic conclusion from the aggregated data.

Additionally, the program’s velocity checks reveal that all of those people visited the website within the last 72 hours and did so for a similar length of time.

The locations of all those accounts are also substantially different from the addresses they declared when registering, and a large number of those IPs come from data center proxies that have previously been reported as suspicious, according to IP analysis of all those accounts.

The member of the fraud team thanks them for holding off on just giving this user the go-ahead and stopping any transactions with the same profile.

After creating a special rule to find relationships in the future that fit this profile, they share a delicious lunch that was made even better by a fulfilling and successful morning.

The key lesson for any e-commerce company should be that preventing fraud from entering the system requires a solution that is used against incoming traffic.

The sophistication of fraud schemes is increasing all the time, and the UK alone lost £2.4 billion to fraud just last year, according to UK Finance.

The second crucial step is to be conscious that any fraud solution will work best when fed the best data your model has access to.

Every e-commerce sector recognizes the value of offering a low-friction, low-churn buying experience, but this must be balanced against your business’ appetite for fraud losses.

Requesting more identifying information may cause a little amount of difficulty in the customer journey, but it shouldn’t have a significant impact on ROI.

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Additionally, it might give you a far clearer picture of your customer base, which is what will ultimately fuel your efforts to reduce fraud and, perhaps, increase earnings.

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