Financial institutions (FIs) across all industry sectors are increasingly experimenting with AI technology in an effort to augment their human staff’s capabilities, with leading Japanese asset manager Nomura Asset Management (NAM) becoming the latest incumbent to join the club.
It announced that it has conducted a proof of concept (POC) with consultancy and software provider Nomura Research Institute (NRI) — also part of Nomura Holdings, to which NAM belongs — to determine whether AI and natural language processing (NPL) could improve the accuracy of NAM portfolio managers’ investment decisions.
The POC was designed to help portfolio managers handle growing volumes of data that can influence stock prices. The parties explained that managers have to take into account large volumes of qualitative information — like analyst reports, news articles, blog posts, and social media postings — when deciding how to weight stocks in a portfolio. However, data volumes keep increasing, the parties say, leaving managers struggling to make sound judgments. As such, part of the POC’s goal was to convert this qualitative raw data into quantitative takeaways processed by AI on the managers’ behalf. The AI was first trained on analyst reports to identify “positive” and “negative” language patterns in the data sources a manager would have to process. That enabled it to weigh up those factors to determine their overall effect on a stock.
Despite the hype, AI seems to be solving a prosaic but real problem for FIs. It’s a hard fact that a more digitized world is generating ever-growing amounts of data, at a time when FIs increasingly depend on being able to process data effectively to remain competitive. When information volumes were lower, human processing power sufficed, but it’s becoming impossible for humans to effectively cope with such quantities.
Although AI still falls short on soft skills, it is proving extremely capable of crunching vast amounts of information to derive complex insights, and as such, is perfectly positioned to solve this overload problem. As a result, at least some element of AI will likely soon become standard for all FIs.
Maria Terekhova, research analyst for BI Intelligence, Business Insider’s premium research service, has compiled a detailed report on core banking system overhauls that:
- Looks at how legacy systems’ structure, and how it makes effective data handling impossible.
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- Gives an overview of how banks should go about moving their organizations to new core systems.
- Discusses the most common risks of overhauls, and how to avoid them to reap the benefits.
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