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While thinking about big data and all the great things many organizations are currently exploring, I wanted to highlight some of its lesser-known use cases that businesses can leverage with a combination of machine learning and AI. 

Normally, businesses use these technologies for improving their marketing campaigns, product pricing, consumer sentiment analytics, and other functions. However, beyond these, many big data use cases that are relatively less popular can be equally useful. 

I’ll be enlisting some such use cases and also some of the more commented-on topics in detail in my future blogs.

Removing false positives in fraud detection

One of the biggest issues—and yet lesser-known—that companies have had with fraud detection solutions in recent times is a large number of false positives—occasions in which such systems incorrectly flag legal, legitimate transactions and classify them as fraudulent. According to several studies, false positives bring more losses to businesses than actual fraudulent transactions. 

Big data and AI-based solutions improve the accuracy of fraud detection systems. To eliminate false positives, such solutions not only detect abnormal payments, just like standard fraud detection systems but also find patterns in real-time transactions by evaluating factors such as payment behaviors, frequency of transactions, and location from which transactions are made, amongst others. 

This is an interesting use case, and you can read about big data and AI’s role in removing false positives in greater depth by clicking here.

Optimizing GRC management

Governance, Risk & Compliance needs the evaluation of massive quantities of data from various sources such as consumers, market trends, and market competitors that helps businesses create policies that can help them achieve their business objectives quickly.  Big data can play a major role in GRC management by integrating information from various sources and real-time streams and running them through preset algorithms to identify anomalies in data and compare them to the larger data sets of the past to identify trends. This will help set up standards across the organizations and the output consistent across the entire organization.  

Would you like to delve deeper into how big data can optimize GRC processes? Click here to know more about the same.

Leveraging agile AI for resolving diverse problems

Organizations normally use AI in different avatars to resolve problems across multiple departments. Agile AI can optimize this. For example, instead of using an AI solution for limiting financial fraud, another for handling employee grievances, and others for identifying potentially risky business strategies, we can use one agile AI solution. Agile AI is simply using big data and machine learning as a ‘one-for-all’ solution that helps businesses detect and resolve issues in quick, iterative jolts instead of doing so in a slow, linear process. 

Beyond these, organizations can always discover a plethora of underrated big data applications to take full advantage of their own data and the insights that could be generated by leveraging AI. Your business can discover the complete array of lesser-known applications to appreciate big data’s importance for your daily functioning even more than now. 

Looking for more information regarding this use case? Click here for more in-depth details about the same.

Often times there are many options in the technology landscape that are free or cheap that could provide efficient, cost-efficient and innovative solutions that help in new customer acquisition & retention, targeted campaigns, speed-to-market with new products, and the identification of potential risks much quicker. 

If you have experience with some additional use cases or technologies that are free and organizations can take advantage of, then please share your thoughts as comments on my post.

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