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Looking Past the AI Hype: Benefits of Data Science Application in Financial Services

Authored by: Ben Musgrave, Consultant-Sales

While the idea of modern-day Artificial intelligence (AI) has been around for years, rapid innovation in the last couple of years has led to immense growth and potential for AI. Today, chatbots and robots take the lead in AI headlines, highlighting their power to lower costs and the debate on whether they threaten jobs, or help create them. However, the real power of artificial intelligence lies in deeper data science capabilities backed by real business understanding and problem solving.

Financial institutions can collect billions of data points from their technology infrastructure every day, with most of it going untouched. This is where AI can make a significant impact. Data is invaluable, but only if it can be assigned meaning. By employing deep data analytic tools and cognitive machine learning to structured and unstructured data, businesses can have more insight than ever before and turn their data into meaningful assets to apply to problem solving, consumer insights and more.

Arguably, the financial services industry has been employing data science techniques for decades in risk and pricing calculations like Monte Carlo simulations, Black-Scholes models, credit risk scoring, and more. Today’s technology landscape and recent progress in technology and data science innovation allows for financial institutions to leverage data science techniques to delve in to deeper use cases. Deep data science is already at work for the largest technology companies, like Facebook, Google, Amazon, and more. Google has a smaller IT department than HSBC, for example but has 100+ teams that are focused on AI. Financial services is in a position of playing catchup to remain current with the latest innovations, as the space is moving quickly, and waiting too long could have sizeable disadvantages, especially as these advanced technologies are becoming more personalized and more consumerized.


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