On March 6, 2018, Kensho, became the largest artificial intelligence deal in history . Most people would guess the lucky startup was acquired by a Silicon Valley giant; however, the buyer turned out to be from Wall Street – S&P Global acquired Kensho for $550 million. S&P Global’s acquisition of Kensho exemplifies how A.I. and machine learning are impacting both the internal process improvements and product development strategies in the financial sector.
S&P Global’s strategic shift to focus on artificial intelligence and machine learning
The S&P Global Market Intelligence segment, which includes S&P Capital IQ and SNL, aggregates huge amounts of company financial data. The firm employs thousands of analysts to add 100,000s of new companies to the database every month; to maintain the necessary speed and quality requirements, the company needs to develop more efficient processes using A.I. rather than relying solely on manual analysis . However, automation cannot completely replace human analysis. Instead, as research in the Harvard Business Review demonstrates, if humans and machines work collaboratively the more successful the process improvements in the company will be (See Figure 1) .
Furthermore, customers, such as investment banks, are no longer only looking for large amounts of data; rather, they are demanding tools that will provide them insights on data. Institutions recognize that data which integrates machine learning insights, rather than simply data mining, allows for deeper and broader analysis, and thus increases business value . Ultimately, S&P can no longer stick to its historical strategy of providing large amounts of data but rather needs to integrate machine learning into its products and processes to make them more useful and thus create a stickier customer base.
Integrating artificial intelligence is part of S&P Global’s short- and long-term strategy
Recognizing that customer demands are evolving, S&P Global has created an internal team of engineers to develop machine learning capabilities, such as natural language processes, that will complement its existing data products. For example, one quant hedge fund originally signed up for Capital IQ to receive access to PDF files of investor transcripts . Eighteen months later, the quant fund now uses transcripts that integrate machine learning technology to make the transcript interactive. By adding machine learning into its products development roadmap, S&P will attempt to keep up with customer demands.
To accelerate its long-term strategy to have machine learning fully integrated in its data products, S&P has begun to invest in technology through S&P Global Ventures, relationships with venture capital funds, and direct investments, thus explaining the acquisition of Kensho . Kensho will be integrated into the S&P platform, allowing users to ask financial questions in a Google search format and instantly receive answers (See Figure 2) .
Additionally, A.I. will augment the data mining process of the S&P platform. For example, S&P has invested in bots that help analysts as they pull data . They are investing heavily to automate as much of the analyst process as possible. While integrating Kensho and the bots will take time, the long-term impact will make the S&P platform more efficient, accurate, and better serving customer needs.
S&P Global must implement additional processes to meet the changes of artificial intelligence in the financial sector
S&P Global must take measures to build trust both internally and with the consumer that the AI technology is accurate. In the 1980s financial institutions attempted to use AI to make financial decisions but these systems almost all failed in comparison to human judgement; while in years since, A.I. has been integrated in certain financial institutions, such as with banks to detect fraud, there has yet to be significant evidence that machine learning can make better financial decisions than humans . However, trust does seem to be slowly returning as a handful of funds are using AI-enabled strategies for trading various asset classes .
The only way S&P will garner the necessary confidence is by keeping humans involved in the data collection process. A.I. tools are only as good as the data provided to the algorithms; if humans are not involved in authenticating the data the A.I. tools will lead to false confidence in the results and exaggerate human errors .
Ultimately, S&P needs to stick to its original value proposition – quality and trustful data; to continue to do this, the Company must use the assistance of machine learning.
What else should S&P Global consider?
What are some concerns financial organizations should have about implementing machine learning into their analysis?
What specific aspects of financial analysis do you think will benefit most from machine learning? Feel free to draw on personal experiences.
Figure 1: Human-Machine collaboration leads to more successful process improvements
Figure 2: An Example of an answer from Kensho
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 Antoine Gara. “Wall Street Tech Spree: With Kensho Acquisition S&P Global Makes Largest A.I. Deal In History,” Forbes, March 6, 2018[https://www.forbes.com/sites/antoinegara/2018/03/06/wall-street-tech-spree-with-kensho-acquisition-sp-global-makes-largest-a-i-deal-in-history/#165c780967b8], accessed November 2018.
 S&P Global Inc., 2018 Analyst/Investor Day (New York: S&P Global Inc, 2018.
 James Wilson and Paul R. Daugherty, “Collaborative Intelligence: Humans and AI Are Joining Forces” Harvard Business Review (July-August 2018).
 David He, Michael Guo, Jerry Zhou, and Venessa Guo, Boston Consulting Group, “The Impact of Artificial Intelligence (AI) on the Financial Job Market,” March 2018.
 S&P Global Inc., 2018 Analyst/Investor Day.
 Antoine Gara. “Wall Street Tech Spree: With Kensho Acquisition S&P Global Makes Largest A.I. Deal In History,” Forbes, March 6, 2018
 S&P Global Inc., 2018 Analyst/Investor Day.
 Lauren Cohen, Christopher Malloy, and Will Powley, “Aritifical Intelligence and Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP),” HBS No. 9-218-080 (Boston: Harvard Business School Publishing, 2018).
 Michael Schrage, “Is ‘Murder by Machine Learning’ the New ‘Death by PowerPoint’?”” Harvard Business Review (January 2018).