Using Artificial Intelligence to Extract Actionable Insights From Data: Q&A with vPhrase CEO Neerav Parekh

April 26, 2018

We had a pleasure of sitting down with Neerav Parekh, Founder and CEO of vPhrase, to talk about the application of Artificial Intelligence in generating business intelligence. vPhrase has worked with multiple financial institutions in Asia including Nomura, Motilal Oswal and Asia Pacific Bank.

 

 

Q: What is vPhrase?

 

At vPhrase, we have built a product that helps companies make their report easier to understand. Basically, what we do is that we automate interpretation.

 

Let me give you an example. Think of the last report that you would have seen. It may be a dashboard or a spreadsheet. You would have seen a lot of charts and tables. What would you have done after that? You would have taken all the data points from that report, and in your mind, you would have tried to coordinate those data points and come up with an insight — something like “maybe because this has happened, this would have gone down”. So the kind of insight that you have created in your mind, is the kind of insight that we automatically create for you.

 

So you don’t have to spend time and energy creating those insights. That’s what our product Phrazor does — it analyses data, draws out insights, and it communicates those insights in a natural language in a few bullet points.

 

Q: What makes your technology unique?

 

I think a typical data analysis platform would create a dashboard for you. What our technology does is that it goes a step further — it tells you the story around that data.

 

The unique part about our platform is its ability to write language. So we can explain the insights in the data by using language. Because language is something that comes very naturally to us, so when we talk, we use language, we don’t use visualisation. So that is what is unique about our technology — it can write language and explain the insights in a very easy to understand manner.

 

Q: What are the use cases of vPhrase and have there been any feedback from clients to date?

 

The enterprises are seeing good value in our product. We are already working wit 25+ large enterprises in India and across Asia. So the kind of use cases from using our platform vary.

For example, Nomura is using our platform for risk reports for the region’s bonds and stocks. Motilal Oswal, one of the largest brokerages in India, is using our platform to create an easier-to-understand industry communication for its 500 thousand investors every month. Asia Pacific Bank is using our platform to create personalized reports for its four and a half thousand branch managers every month.

 

So you can see that the use cases are varied, and it can be used for any company in any industry in any geography. If you have data, our company can help you understand the data better.

Q: AI is now a hot technology and a hot topic in the industry. What trends do you foresee in terms of AI applications and services in Hong Kong?

 

I think AI has been kind of abused over time. It’s overused across the Fintech industry. I think when we talk about AI, clients expect it’s more of a generic gear. You give them the software and they can throw anything in the software to make sense of it, which is not true. So whatever AI you see in the market is a very limited usage of AI. The application for that specific algorithm is very limited — you give it X type of information and it will give Y kind of output. But anything beyond that, it will not be able to do.

 

I think that will continue over the next foreseeable future. I think in this Fintech industry, what the AI will do is with all the data that have been collected, it will help make sense of that data, it will be used predominantly on the data analytics side like what we are doing for risk analysis, fraud prevention… I think those are the areas AI would be certainly used.

 

— — —

 

vPhrase is one of the 10 companies admitted to the SuperCharger FinTech Accelerator HK 2018 Program.You can find out more about vPhrase here.

 

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