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Why Watson? Well there’s a funny story there.
I’m a technophile. My family avidly watched the Jeopardy! contest with IBM Watson years ago—my son and I were rooting for the computer and my daughter was rooting for the human.
My son was 14 and very much into science and technology—a good computer geek, if you will.
We watched the Jeopardy! competition with a lot of interest. Shortly afterward he read in Scientific American about how IBM was commercializing Watson.
He came to me and said, “Hey Dad, you know that Artificial Intelligence system you’ve been building? IBM has now made a set of libraries available to support that kind of thing.”
The Junior Analyst
When my son came to me with the tip about IBM Watson libraries, I said, “That’s great news!”
I went to the website and applied for Coalesce to join the Watson ecosystem. We were lucky. At the time, the window to apply was actually closed, but thankfully the person who received the application used to be in venture capital and said, “Oh, I get it. I get the use case. I understand how painful and laborious it is, and I would love to get you into the ecosystem.”
And that was the beginning of our journey with Watson.
Coalesce makes artificial intelligence easy and inexpensive for business. About four years ago we saw the potential for AI to vastly improve the way people analyze data for their daily jobs, especially in financial services where people are overwhelmed with the exponential growth of digital data.
We set about creating a product that could automate the way people get answers to the most pressing business questions they ask every day, such as, “What are my biggest risks? What are my best investments? Who are my best clients? Who are my best prospects?”
We decided to leverage the huge investment that IBM had made in AI instead of reinventing it from scratch. Since we were already “building the car,” it was easy to “lift out our engine” and replace it with Watson, the “1000-horsepower engine.” To go on top of it, we invented an AI layer that learns from business users on the fly, without the need for any programming.
We started out by automating how customers sift through millions of articles for “good signals” to find their best investments. Along the way, they asked if we could teach the system to look for “bad signals” to help lower their risks. We said to them, “Why don’t you have your risk and compliance experts teach it themselves?”
Since then customers have been using the system to find investments, analyze risk, comply with regulations, improve customer service, find new customers and streamline internal operations. We even have a customer who uses the system to automatically review wire-transfers to protect them against fraudulent attacks.
Although the technology can apply to a wide array of businesses, we’ve focused on financial services – an industry that can benefit greatly from adopting AI solutions. We recently launched a solution in the IBM Cloud specifically for Financial Services Compliance and Risk Analysis. We know that IBM has a reputation for providing the highest quality and security for businesses, which are necessary for financial services to migrate their operations to the cloud.
I believe that AI will continue to bring important and unique benefits to the entire financial system as new and unexpected forms of interconnectedness develop between financial markets and financial institutions. We’re using AI and Watson to make it easy for customers to analyze vast amounts of data to stay competitive in this era of exponential data growth.
For more details on Coalesce.Info, read the IBM case study on Catalyst Investors.