FINTECH: BEYOND THE STARTUP Bob Hedges, SM 84 Partner, Global, A.T. Kearney, Moderator Phillip Riese, SF 77 President, Riese And Others Mona Vernon,

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1 #MITFintech FINTECH: BEYOND THE STARTUP Bob Hedges, SM 84 Partner, Global, A.T. Kearney, Moderator Phillip Riese, SF 77 President, Riese And Others Mona Vernon, SM 11 Vice President, Thomson Reuters Labs Ben Golub, SB 79, SM 82, PhD 84 Chief Risk Officer, BlackRock

2 RIESE & OTHERS

3 Lazy Money Zero Latency De-Stacking Just-in-Time Insurance Unbanked RIESE & OTHERS

4 of The Customer Massive Customer Disruption Expectations RIESE & OTHERS

5 Technology Analytics The Stack Distribution The Margin The Customer The Compliance Law The Management Will RIESE & OTHERS

6 #MITFintech FINTECH: BEYOND THE STARTUP Bob Hedges, SM 84 Partner, Global, A.T. Kearney, Moderator Phillip Riese, SF 77 President, Riese And Others Mona Vernon, SM 11 Vice President, Thomson Reuters Labs Ben Golub, SB 79, SM 82, PhD 84 Chief Risk Officer, BlackRock

7 EVERY BUSINESS IS A DATA BUSINESS Mona M. Vernon Thomson Reuters Labs mona@sloan.mit.edu

8 Thomson Reuters Labs Canada Waterloo Region United States Boston Central Europe Zurich Region Co-located with, accelerators, startups, corporate labs, & university programs Opening in 2016: Europe London ~12 data scientists, strong MIT relationship Africa Cape Town Enterprise innovation tools, data architecture, prototyping

9 Our short glossary of all things data Raw Data: Data as it is collected from the source not manipulated or processed (e.g. sensor data) Data Exhaust: Data generated as information byproducts resulting from digital or online activities Time Series: Measurements of prices, economic data through time Reference Data: Terms and conditions, financials statements Analytics: Discovery and communication of meaningful patterns in data Predictive Analytics: Extracting information from data to determine patterns and predict future outcomes and trends Entity Analytics: The analysis of the connections/linkages between different entities 139

10 New data business models: Why now? Data abundance Temporal abundance Abundance in scale and scope Exponentially more compute power and faster networks New tools to ingest, store, process, enhance and distribute data Improving algorithms and AI systems 140

11 Platform Three data monetization business models Sell Raw Data Provide raw data directly to customers or through distributors Provide Data Analytics Develop new analytics from internal data alone or combined with other data sources. Develop Data Platform Provide a marketplace and platform for multiple data sources and analytics applications. Easy to accomplish and short time-to-market Differentiation via data-driven decision making internally or as a service Multi-sided network effects Value is a function of exclusivity or differentiated access Need data scientists to develop the capabilities in-house Need data science, software development and business expertise in platform business strategy 141

12 Example: Analytics adds significant value to commodities trading Maritime movement of commodities data Maritime trade accounts for more than 80% of the global commerce Proprietary metadata Advanced cargo-modeling algorithms and analytics Novel trading strategies utilized in internal hedge fund 142

13 Common Issues and Challenges Finding the initial hypothesis and validation method to find signal in the data Finding a reliable, repeatable method to collect the data with the required frequency Establishing history where possible Establishing rights to the data use as business models evolve Establishing internal agreement to externalize and monetize the data Striking balance between source protection and creating new value Confirming the data is not trumped by another better, faster, cheaper source Linking to a tradable security (Financial services) 143

14 #MITFintech FINTECH: BEYOND THE STARTUP Bob Hedges, SM 84 Partner, Global, A.T. Kearney, Moderator Phillip Riese, SF 77 President, Riese And Others Mona Vernon, SM 11 Vice President, Thomson Reuters Labs Ben Golub, SB 79, SM 82, PhD 84 Chief Risk Officer, BlackRock

15 AUM ($ BILLIONS) Digital Advice 14 5 The Rise of Digital Advisors The digital advisory business has grown at a rapid pace. Over the past decade, an increasing number of firms have begun offering digital investment advice. Nearly 140 digital advisory companies have been founded since 2008, with over 80 in the past two years 1. Digital advisors provide a variety of advisory services to clients via internet-based platforms. Utilize a number of different investment philosophies, methods and strategies. Leverage algorithm s simple or complex multi-strategies that evaluate thousands of securities and scenarios to construct a customized portfolio based on current holdings, investment horizon, risk tolerance, and client preferences. Vary in level of sophistication across customization, tax management, human intervention, and type of provider. Digital advisors provide an effective way to engage consumers who have not relied on traditional investment management services. Provide affordable and accessible services at a lower price point. For many consumers, digital advisors are a more natural way to receive investment advice, as opposed to traditional forms of investment services. Largest US Digital Advisors by AUM $35 $31.00 $30 $25 $20 $15 $10 $5.30 $5 $1.98 $2.40 $3.00 $0 Personal Capital Wealthfront Betterment Charles Schwab Vanguard Source: Tracxn Report: RoboAdvisors (Feb. 2016). Consumers Advisors 1Source: Tracxn Report: RoboAdvisors (Feb. 2016). Need more convenient and affordable access to advice Need new ways to reach mass affluent Source: BlackRock. For illustrative purposes only.

16 14 6 U.S. Consumers: Primary Reasons for Interest in Digital Advice Convenient Simpler No one is pushing products on me Lower cost Good for the smaller / new investor Provides more choices than I have now Objective / unemotional advice Service is in my best interest Offered by a reputable firm More comfortable with a tech solution Provides as good or better advice as a personal advisor Not sure Other reason 1% 3% 20% 20% 19% 18% 33% 31% 27% 27% 26% 24% 42% 0% 10% 20% 30% 40% 50% Source: Investor Pulse Depicts responses of US respondents to the question, Why would you be interested in this type of service?

17 14 7 Opportunities and Challenges for Digital Advisors Greater Accessibility and Lower Point of Entry Provide 24/7 access, enabling any investor with a smart phone, computer or tablet to access and update their portfolio. Little or no minimum balance required to establish a digital advisory relationship, enabling investors to start investing without having built a nest egg. Increased Engagement Effective way to engage consumers who have not considered using traditional investment management services or have been discouraged by the cost associated with personalized investment advice. Efficient Communication withclients Provides the ability to reach and interact with more clients efficiently and effectively. Expanded Range of Products The vast majority of digital advisors leverage passive management. The evolution of technology should allow for the management of actively managed products in a similar framework. Globalization of Software Development More people around the globe trained as developers, allowing teams to work virtually. Opportunities Leverage standardized software and coding solutions. Regulatory Barriers The financial services industry is heavily regulated, which serves as a barrier of entry that can not be solved by technology. Adding to the complexity, regulations vary by country (i.e., China vs. U.S.). Regulators define standards of conduct for advisory services, trading practices rules, and safety and soundness rules governing electronic trading, information security regulations, and disclosure requirements. As a fiduciary, financial advisors must ensure that their recommendations, investment methods and strategies are in the best interest of clients. Digital advisors are forced to rely on questionnaires to understand their clients goals and objectives. Algorithm Design and Oversight Algorithms must be designed based on stated investment strategies and methods, and managed according tocoding control procedures. Data Protection and Cybersecurity Cybersecurity creates risks that the largest institutions can only mange imperfectly. Operational Failures Challenges Massive liability arises from operational failures, creating shadow costs for large institutions working with technology.

18 14 8 BlackRock Acquired FutureAdvisor in 2015 Helped facilitate a change in business model from B2C to B2B2C, recognizing the challenge and cost of direct B2C client acquisition. Initial Challenges Maintain a start-up culture and retain the talent. Adapt processes and technology to meet the rigorous requirements of financial institutions. Giant Regulatory & Controls Quagmire FinTech start-up companies are used to delivering their products on their own terms. However, when FinTech start-up companies shift to a business-to-business model, they are forced to comply with large financial institution standards. Through this shift, FinTech start-ups enter into a giant regulatory and controls quagmire: Regulatory Compliance Information Security Privacy Key Changes to FutureAdvisor Matured company to be ready for increased regulations and regulatory oversight. Enhanced product so it was robust enough to interface with other financial institutions. Streamlined and standardized approach to interacting with clients. Introduced risk management concepts, such as operational and reputational risk. Benefits Given the challenges associated with technology implementations, BlackRock provides experience and expertise to FutureAdvisor. FutureAdvisor remains connected to its start-up / Silicon Valley culture, while having the backing of the world s largest asset manager.

19 14 9 Who Will Win? Start-ups that Grow Acorns Betterment Nutmeg Personal Capital TransferWise The Tech Giants Amazon Apple Google WeChat The Financial Incumbents BlackRock Charles Schwab Fidelity Vanguard