How Big Data Helped in Visa Fraud Detection

It has become common practice for individuals worldwide to use credit cards instead of cash when purchasing goods and services. American Express has in fact surpassed handling over $1 trillion worth in transactions in 2014 while Visa has an even bigger number of transactions that amounted to $6.8 trillion in 2015.

It comes as no surprise then that financial companies such as Visa deal with huge amount of data on a daily basis. This makes fraudulent credit card transactions more difficult to control. As such, credit card companies need to know how to leverage all the data in their system.

Traditional databases simply are no longer enough in preparing and analyzing such data. This fact brings about the age of big data and analytics. Many financial companies have upgraded to big data technology not only in appropriately handling everyday transactions but also to effectively detect and prevent fraud.

Visa and other payment processing firms have an important role in connecting merchants, banks and customers while making certain that the entire process remains convenient and safe. In the same manner, the company profits from transactions.

However, card fraud is proving to be a big challenge to Visa’s profitability, and its trust-based model becomes subjected to doubts. Credit card companies are held responsible directly by merchants if fraudulent online payments occur. Besides the actual prices of goods purchased illegally by online criminals, there are other intangible costs that come with credit card fraud that will be shouldered by credit card companies.

In its pursuit to protect its system, situated in a highly competitive market, Visa has acquired the buzzword in technology, that is, Big Data and Analytics. This allows Visa to process billions of transactions worldwide on an annual basis. It can be imagined how large the amount of disparate and unstructured data can be made useful by such a technology in a very short time with little amount of manual labor involved in its operation.

The analytics platform used by Visa in processing data helped it manage almost 100% payments information as compared to only 2% prior to implementing the technology. Through its new platform, Visa can now scan millions of data simultaneously and acquire customers’ purchase patterns that enable it to know consumers better and do away with costly layers of approval when purchasing.

Moreover, the new technology allows the firm to learn from past fraudulent cases by recognizing irregularities and stopping them. The algorithm employed allows for immensely speedy feedback loops that enable easier spotting of fraud. As a result of implementing big data technology, Visa saved more than $2 billion in possible fraud. More significantly, it managed to gain back customers’ trust to the company and its system.

This Visa situation is just one example of how crucial big data and analytics technology is among businesses, whether or not within the financial sector. This is why data preparation and data wrangling startups are being sought after by companies which are cognizant of the necessity of evolving with technology to be able to effectively move forward.

Watch out for my next article on financial technology, more commonly known in the financial world as FinTech. I will also be featuring how big data technology hit Singapore by storm in another article I will feature on my blog. I will show you how this country is investing heavily on modern technology and how such an investment pays off.

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