Machine learning is critical to the financial services industry and provides a unique opportunity to develop products in a more deliberate and sophisticated manner. Machine learning also allows for financial services firms to augment current products or create a differentiated customer experience. One firm that is leveraging machine learning is Morgan Stanley. Machine learning is particularly important for Morgan Stanley as it can provide a competitive advantage in a highly competitive financial services marketplace. The ability to customize the customer experience and deliver exceptional customer service is paramount and with machine learning higher levels of product differentiation and customer service can be achieved. If Morgan Stanley were to take a passive approach with machine learning and technology, they could find themselves quickly obsolete in the financial services industry. Machine learning, coupled with other disruptive technologies, are revolutionizing the process improvement cycle and product development cycle to ultimately create value for shareholders. It would be a disservice to all Morgan Stanley stakeholders to not adopt such technologies as the potential to drive revenue (Columbus) and reshape pricing strategies (Rizzi) is a compelling value creation. Morgan Stanley can emerge as the thought leader for machine learning in the financial services industry if they continue to invest in disruptive technologies and take the necessary actions to capitalize on this opportunity.<\/p>\n
Morgan Stanley has taken a lot of steps to ensure that they are well equipped to address the importance of machine learning. In the short term, Morgan Stanley has launched a few key projects that leverage machine learning. Using machine learning, Morgan Stanley launched a robust program that arms their financial advisors with a unique \u2018personal assistant\u2019 (Clary), one that provides insightful customer information to better serve Morgan Stanley clients by providing them with products that customers need. This program allows for a \u201cpersonalized and engaging\u201d (DiCamillo) relationship, a unique value proposition for financial advisers. In addition, Morgan Stanley has acted from an organizational perspective. Morgan Stanley created an entire department, the Analytics and Data Organization (Davenport), to ensure that the company can effectively implement and leverage machine learning. They are also investing in top industry talent (Bielski), which will create value for the organization over the long-term horizon. Another differentiated long-term approach Morgan Stanley has taken is to not simply employ machine learning but become a hub for machine learning research. Instead of taking a complacent approach and outsourcing machine learning techniques, Morgan Stanley employs a team focused on cutting edge machine learning research and technology upgrades. Morgan Stanley creates content i.e. article publications (Helfstein) and is becoming a thought leader in the industry.<\/p>\n
Despite a promising technology with immense benefits, there are some challenges with machine learning adoption in the financial services sector. A heavily regulated financial services industry could potentially have stringent regulations imposed (Machine Learning). I would recommend Morgan Stanley work closely with the regulatory bodies to ensure they are ahead of the curve and proactive instead of reactive. Machine learning will be best leveraged with other technological advancements such as digital, blockchain, and data analytics. From an organizational perspective, to best optimize disruptive technologies, it would be prudent for Morgan Stanley to create a cross-functional team that oversees all technology and disruptive efforts. Education can be a critical element to the success of machine learning customer adoption. It would be beneficial for Morgan Stanley to have internal \u2018translators\u2019 i.e. someone extensively trained in the machine learning field that can teach internal and external stakeholders. Machine learning is an entirely new language, one that can be confusing and misunderstood. I would recommend Morgan Stanley educate their customers and consumers on the powerful benefits of machine learning, but not entirely replace the \u201chuman feel\u201d. Eventually the technology will evolve such that humans can be entirely replaced. I would recommend being cautious with this and allow for some human interaction to be embedded in the customer journey. Another pitfall Morgan Stanley should be cautious with is creating a machine learning environment that is \u2018bias\u2019 in its coding (Baer). Proper machine teaching techniques and clean data integration is a critical component of a successful utilization of machine learning technology.<\/p>\n
Two questions I have are as follows:
\n\u2022\tAs machine learning technology progresses and humans become obsolete in a customer service capacity, do you believe firm\u2019s will and should continue to employ some humans?
\n\u2022\tDo you believe organizations should adopt machine learning in-house or outsource? Should all organizations strive to become technology\/software companies that just happen to sell financial products?
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\nTotal Words: 746
\nWorks Cited
\nBaer, Tobias, and Vishnu Kamalnath. \u201cControlling Machine-Learning Algorithms and Their Biases.\u201d McKinsey & Company, Nov. 2017, www.mckinsey.com\/business-functions\/risk\/our-insights\/controlling-machine-learning-algorithms-and-their-biases.
\nBielski, Vincent. \u201cMorgan Stanley Hires Ex-SAC Capital Artificial Intelligence Expert.\u201d Bloomberg.com, Bloomberg, 26 June 2018, www.bloomberg.com\/news\/articles\/2018-06-26\/morgan-stanley-hires-michael-kearns-to-lead-ai-research-effort.
\nClary, Timothy A. \u201cMorgan Stanley Arms Advisors With Machine Learning.\u201d Barron’s, Barrons, 31 May 2017, www.barrons.com\/articles\/morgan-stanley-arms-advisors-with-machine-learning-1496248706.
\nColumbus, Louis. \u201cMachine Learning’s Greatest Potential Is Driving Revenue In The Enterprise.\u201d Forbes, Forbes Magazine, 20 Nov. 2017, www.forbes.com\/sites\/louiscolumbus\/2017\/10\/23\/machine-learnings-greatest-potential-is-driving-revenue-in-the-enterprise\/#6dbaa4ae41db.
\nDavenport, Thomas H, and Randy Bean. \u201cHow Machine Learning Is Helping Morgan Stanley Better Understand Client Needs.\u201d 性视界 Business Review, 3 Aug. 2017, hbr.org\/2017\/08\/how-machine-learning-is-helping-morgan-stanley-better-understand-client-needs.
\nDiCamillo, Nathan. \u201cMorgan Stanley Draws from ‘Hundreds of Conversations’ with Experts to Build Its AI.\u201d American Banker, 12 July 2018, www.americanbanker.com\/news\/morgan-stanley-draws-from-millions-of-conversations-to-build-its-ai.
\nHelfstein, Scott. \u201cInvesting in Artificial Intelligence and Automation.\u201d Morgan Stanley, Morgan Stanley Wealth Management, 21 May 2018, www.morganstanley.com\/ideas\/artificial-intelligence-and-automation.
\n\u201cMachine Learning.\u201d The Edge, Morgan Stanley Investment Management, 2017, www.morganstanley.com\/im\/publication\/insights\/investment-insights\/ii_theedgemachinelearning_us.pdf.
\nRizzi, Walter, et al. \u201cHow Machine Learning Can Improve Pricing Performance.\u201d McKinsey & Company, Sept. 2018, www.mckinsey.com\/industries\/financial-services\/our-insights\/how-machine-learning-can-improve-pricing-performance.<\/p>\n","protected":false},"excerpt":{"rendered":"
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