Artificial Intelligence: The Answer to Wall Street’s Data Deluge

Would artificial intelligence technology have helped JPMorgan CEO Jamie Dimon and his lieutenants catch and grasp the full story behind the firm’s approximately $6 billion trading losses — buried in the trading data — far faster?

Is a narrative presentation that swiftly provides insight about a large body of financial data preferable to investors and financial managers than is the presentation of pure visual data in a chart or spreadsheet format? Is a word-based explanation more quickly understood and thus more quickly acted upon?

While these questions continue to be debated, new technology developed by Narrative Science, a two-year-old firm born out of artificial intelligence research conducted at Northwestern University, is poised to use machine-generated narratives employing AI to help Wall Street manage its exponentially growing deluge of data.

At a recent technology conference in New York targeting the financial services industry, Narrative Science demonstrated its new AI-based software platform, called Quill, that aims to help individuals in the financial sector and other high data-generating industries to better analyze and comprehend the full meaning of the data more quickly.

“Someone would come by and at first think that all we are doing is text aggregation, and then it would hit them: We take existing data, raw financial data — it can be about investments or data they need to analyze or data they need to communicate to clients — and quickly turn it into stories, a narrative or executive summaries,” that appear to be written by humans, explains Kris Hammond, chief technology officer of Narrative Science.

During a question-and-answer period at the conference, Hammond asserted that his AI platform can conduct swift analysis that can also be used to detect data anomalies, highlight financial fraud and rapidly issue a verbal communication about the findings — sort of like have an investigative reporter or data investigator on site.

But he was also quick to qualify the firm’s capabilities: “We work with structured data — numbers and symbols — where the numbers and symbols are unambiguous. We do not work with free text,” he said. “We can’t take a news article and generate a report, but if there’s data where we have unique symbols for a company and a link to its current earnings, sales or market cap info, we can take all that data, do an analysis and generate a story.”

这与现在的其他公司的同一情况相反，这是采取非结构化数据，例如来自互联网，社交媒体，博客和推文的信息，并尝试聚合并弄清楚数据。叙事科学翻阅该模型，专门使用结构化数据，基于符号和符号的数据金融公司的种类在很大程度上陶醉，并使用算法，机器学习和自然语言发生器将该数据转换为流体内容读起来它已经由人类产生。Rob Passarella是一个财务数据专家，将技术描述为“需要大量结构化数据的平台并将其转变为叙述”，以便人类可以理解内部的复杂性。“

Narrative Science’s unique approach to addressing the “Big Data Challenge,” as the dilemma is sometimes referred to, comes from the company’s unusual genesis: Hammond, the founder of Northwestern’s AI laboratory, decided early on to partner with the university’s respected journalism school to test and refine his lab’s capabilities. A collaboration between engineers and journalism students ensued, allowing the two groups to apply advances in artificial intelligence to content and news generation. At first, the technology was used to produce sports articles based on local baseball team statistics. “You would be amazed at how much analytics is required to produce a really good sports story,” notes Hammond. But in time, he and his partners realized that the technology, while relevant to media companies — some of whom are now clients — was also relevant, if not more so, to data-driven businesses like finance.

The Evanston, Illinois–based company was founded by Hammond along with chief scientific advisor Larry Birnbaum, another Northwestern computer science professor, and CEO Stuart Frankel, previously an executive with digital advertising company DoubleClick, a Google subsidiary.