Can Big Data Help Build an Online Marketplace Free of Digital Fraud?

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Billions of dollars have been lost – both by consumers and businesses – to online scammers. Can big data and machine learning algorithms be the ultimate saviors of cyberspace?

The year 2018 saw an unprecedented rise in online fraud with account takeover and credit card scams leading the way.

Identity theft is the most common form of digital fraud with synthetic IDs being used to either defraud the online businesses or make purchases that eventually has to be paid by genuine credit cards of actual users.

Despite all the innovation and security measures adopted by online businesses, scammers and fraudsters have been able to beat them time and again.

For a time, it was considered that no form of tech can be able to deter these online attacks, but it seems that the digital world might have found its savior after all.

Can Big Data Help Build an Online Marketplace Free of Digital Fraud?

Big Data – The Ultimate Savior

Big data has been gaining prominence in the tech world. Not only because it helps professional businesses to track down trends but it also holds the key to fight online fraud for good.

With the help of machine learning algorithms, big data has the potential to usher a new wave of online transparency with every attempt of digital fraud and online identity theft being tracked almost immediately.

Unique data points and lightning-fast processing speed can build an online marketplace free of digital fraud. Because at the end of the day, what good is a digital age if it cannot evolve at the same speed as those who want to wrongfully benefit from it.

Most common forms of Identity frauds

The most common format of identity fraud used in availing online services or for buying products with the help of fake identity is through face verification.

Some online fraudsters have been able to create synthetic IDs in order to claim an identity that is generated by merging identity profiles of not one but many genuine users.

Big data can compile all those attempts of defrauding companies and machine learning can make sure that any future effort of using the same method for identity fraud can be identified much more efficiently.

This is the main reason why facial recognition is being favored as a preferred format of biometric verification as opposed to fingerprint scans and iris scans.

Several businesses that have opted for face verification as their preferred mode of verification has faced the problem of identity fraud, by allowing still images for face verification.

Still images are used as common means to defraud companies by using techniques such as image editing, photoshopping certain aspects of a still image or by altering the metadata of the image in order to get the image verified as original.

Machine learning and big data work in coordination to track down these common formats of image-based identity frauds. And all efforts made by scammers to cheat an online verification system with the help of the even the most sophisticated software.

Big Data for Credit Card Fraud Prevention

Surely, the biggest threat for the online marketplace is the ever increasing numbers of credit card frauds. And the recent financial and personal data breaches have substantially increased the dangers of financial frauds.

Online fraudsters can easily get hold of original financial information and use it to make purchases that will either be charged to original card holders or the digital businesses in the form of cashback requests.

Now, the ideal scenario will be to check each credit card for the provided credit card number and ascertain the ownership of a credit card account before processing each transaction.

But as one can assume, such action will require a substantial amount of time, something that can hurt an online e-commerce business significantly.

Data analytics based on big data can enable companies to perform geo timing, a technique through which credit card firms can compare the geographical locations of different transactions performed by a cardholder within a certain timeframe.

In case, transactions are performed at two distinct geographical locations within a time period, during which it is physically impossible to travel between those two locations, the credit card firm can easily flag the transactions as fraudulent.

Speed of Verification

The dichotomy of commerce is not forgotten when it comes to user verification on cyberspace.

On one hand, businesses want to eradicate the menace of online scammers and digital identity theft. But on the other hand, they also want the verification process to remain entirely frictionless so that their genuine clientele is not disturbed by a verification process that takes more than a few minutes to perform.

Machine Learning supported by big data is an easy solution for such concerns. As the larger dataset is collected about different forms of identity theft and scams, the easier it becomes to detect any effort of digital fraud or online scams.

Reduced Processing Time

Several industries require swift processing in order to perform their core functions, insurance industry being the perfect example of this.

Every insurance claim has to be carefully vetted, but spending a considerable amount of time in order to process each insurance claim is also not considered efficient for an industry that is known for their sluggish application processing.

Big data can help insurance companies to efficiently detect any attempt of financial fraud, without having to spend substantial time on each claim.

Red flags go up as soon as any loophole is detected in the provided information or documents attached with the claim.

Public data and the company’s own proprietary databases are being put into use to find any unusual piece of information that can point towards any attempt of insurance or financial fraud.

Conclusion

For the online marketplace to become free of financial scams and identity frauds, big data has to be incorporated into fraud detection measures.

It will not only result in more accurate, faster and scalable fraud detection for multiple industries but any new scheme to perform online scams will become easier to track. And will be red flagged across multiple platforms in a short span of time.

Moving towards a future that promises frictionless user experience for digital clients and unprecedented revenue streams for online businesses, Big data will eventually have to play a vital role to fight financial frauds and digital identity theft.

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