September 20, 2018

Pivotal role of a data & analytics strategy in asset management transformation Suvrat Bansal, Head of Innovation and Chief Data Officer UBS Asset Management

About UBS Asset Management

UBS Asset Management—key facts Breadth and depth of expertise across asset classes and markets, serving a diverse client base

CHF AuM1

810bn 2nd One of the largest managers in Alternatives

2nd largest fund of hedge funds managers2 and 5th largest manager globally of direct real estate3

3,600 Around 3,600 employees4

Over 900 investment professionals

Presence in 23 countries

#1 Largest mutual fund manager in Switzerland5, Europe's 3rd largest money manager6, 2nd largest Europe-based passive player7, the top foreign firm in China8

Notes: Past performance is not indicative of future results. 1 As of 30 June 2018 2 HFM InvestHedge Billion Dollar Club, March 2018 3 FT/Towers Watson, 2017 (based on data to 31 December 2016) 4 Thereof around 1,300 internal and external FTE from Corporate Center; as of 31 December 2017 5 Morningstar/Swiss Fund Data FundFlows, July 2018 6 Institutional Investor Euro 100, based on data to 30 June 2017 (based on discretionary assets only, UBS WM and AM combined, excluding fund of funds assets) 7 UBS Asset Management analysis, May 2018 8 Z-Ben Advisors, 2018 China Rankings: The top foreign firms in China, April 2018

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40%

19%

10%

16%

5%

10%

EquitiesFixed Income (ex MM)Money MarketMulti-Asset and Customized Solutions²Hedge Fund BusinessesReal Estate & Private Markets

33%

25%

23%

20%

SwitzerlandEurope, Middle East & AfricaAmericasAsia Pacific

63% 27%

10%

Third party Institutional³

UBS's wealth management businesses

Third party Wholesale

Invested assets CHF 810 billion

A diversified asset manager Diversified across business lines, regions and distribution channels

Notes: As of 30 June 2018. Data represents the internal distribution view for regions and distribution channels and production view for business lines. Data does not include the assets of non-consolidated joint ventures (CHF 41.3bn as of 30 June 2018). Total invested assets CHF 810bn of which passive strategies CHF 315bn.

1 Equities, Fixed Income and Money Market reflect asset classes; the Hedge Fund Business consist of the O'Connor (single manager) business and Hedge Fund Solutions (HFS, multi-manager business); Real Estate & Private Markets (REPM) is a separate business line

2 Multi-asset includes asset allocation as well as some alternative investments not managed by O'Connor and HFS and Real Estate assets managed outside REPM. Total Multi Asset CHF 128bn, of that Investment Solutions manages CHF 115bn 3 Includes UBS Investment Bank channel

Business lines1 Regions Distribution channels

Source: UBS Asset Management

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Digital transformation

“ Data strategy is critical to success in digital transformation

Let's start with you—data champions In last two decades, depth and diversity of data skills required by the organizations have shown tremendous evolution

Relational world • Database developer • Database administrator • Data modeler • Data warehouse expert • ETL developer • MDM architect • Data services architect • Business intelligence expert

Regulatory demand • CDO • Enterprise Data Management • Data owners • Data stewards • Data policy expert • Data quality analyst • Data lineage expert • Data czar

Big Data & Data Science • CXO • No-SQL engineer • Streaming data expert • Data pipeline engineer • Data wrangler • Data scientist - ML • AI expert, Neural Nets • Alternative data expert

Skill

dep

th

Skill diversity 6

Technology and industry developments

Cloud

Big Data Machine Learning

Artificial Intelligence

IoT

Alternative Data

Digitalization

Block Chain Mobility

BPO ITO Paas SaaS

Outsourcing GDPR MiFID AIFMD UCITS…

Regulations

Cybersecurity Data Privacy

RPA

Social Environment Transparency Digital

iGeneration

Catalysts for change

Client Experience & Service

New revenue channels

Complex regulations

Competitive forces

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Given the continuous evolution of technology and industry developments, pace and scale of transformation has consistently increased

We are witnessing exponential changes, but are we prepared to handle them

Exponential change

"As every schoolchild knows, the dinosaur didn't survive the Ice Age…

It's not that he lacked the capacity to evolve. He just didn't have the time. Unlike the dinosaur, bankers can see

the changes ahead. We have a choice in the matter. The dinosaur never did."

Hugh L. McColl Jr.

Leadership is under constant pressure to transform, but organizations are not equipped to execute cross-functional change

Business architecture

Is the traditional approach to transformation working?

Challenges

• Inter-dependent systems and processes, making it difficult to align multiple change initiatives

• Making large scale changes impacting many processes and systems take a long time and often unsuccessful

• Innovation and M&A in last two decades have introduced many more systems in the organizations to maintain or upgrade

• Globalization and diversification has led to complex matrix of global and local flavors of operating models

• Work-forces are traditionally trained in few functions, but lack the understanding of entire value chain

IT architecture

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Options for sustainable transformation

1 Keep trying to transform the old bank in the same way – "we are smarter than others"

2 Create a completely new digital bank—"leave the problem of transforming old world to someone else"

3 Take the data centric approach to transform and create

—"what are you talking about"

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What is data centric transformation? Every process and decision can be translated into consumption of data or contribution to data

Scope: identify a broad boundary of scope—business division, sub-division etc.—ambitious but manageable

Central capabilities: Set up Data Team with the mandate to provision key data architecture and governance capabilities— the incentive

Transformation alignment: Mandate every transformation effort to leverage the central capabilities and enhance for its objectives—consume and contribute

Ensure architecture and governance Integrity: Put KPIs in place to ensure every change continues to follow the principles of architectural and governance integrity—expand with controls

1

2

3

4

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UBS Asset Management—Central Data & Analytics capabilities Global businesses/client segments/functions

Equity Fixed Inc. Solutions Hedge Funds Fund of Funds Passive

Sovereigns Pension Insurance Corporates Whole Sale Retail Wealth Mgr.

Research Port. mgmt. Risk mgmt. Trading Operations Compliance Sales

Regulators

Quant Business architecture

Business processes delivery mechanism Service layer

Finance Products NAVs Flows Performance Position Mandates Assets Trades Derived

Data Alternate

Data Clients

Client / Advisor Hub Operations Warehouse Analytics Hub MIS

Derived Data

Operational Data

Master Data

Markets & Alternate Data

Governance Tools Analytics

Data layer

Data services

Data architecture

Market & Master Data Hub

Data

Information

Knowledge

Intelligence

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UBS Asset Management—Data & Innovation team Pillar I—business coverage & programs:

• Represents data needs of a function • Structure programs to drive data architecture and data

governance for the domain • Validates the approach and plan with Capability Owners

and Knowledge Services • Manages program governance, stakeholder engagement

and communication

Head of Innovation & Chief Data Officer

Investments

Products & Sales

Operations

Regulations

Coverage Capabilities

Data discovery

Data modeling

Data governance

Data analytics

Knowledge Services

Data sourcing

Data mastering

Data quality

Business intelligence

Pillar II—data capabilities: • Provides unique expertise for multiple

data solutions • Provides guidance to Domain owners in formulating

a solution • Single point of contact for IT and manages multi-program

deliverables through the solution

Pillar III—knowledge services • Leads data stewardship to ensure data quality • Manages detailed KPCs and KPIs to measure service levels • Grows from business intelligence to advanced analytics

with the expansion of central capabilities 13

Application of alternative data and advanced analytics in asset management

Industry trends leading to utilization of Alternative Data Secular changes in asset management and significant increase in data availability are leading to incorporation of alternate data into research, product innovation and client acquisition

Source: McKinsey

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Industry survey on data and analytics UBS Asset Management has sponsored an industry survey in partnership with Element 22 to provide a holistic view on the maturity of foundational data efforts, data governance, use of advanced analytics and alternate data within the industry

Participant's asset focus Participants Participant geography1

Data & Analytics Capabilities covered Participant profile • 181 diverse asset managers

– Pure-play Asset Managers – Asset Managers within large

financial corporations – Wealth Managers – Pension Funds – Hedge Funds – Diverse Asset Mix – Diverse Investment Capabilities – Diverse Operating Models

• Foundational Capabilities – Data Strategy – Data Governance – Data Infrastructure – Analytics Strategy – Maturity

• Focus areas – Alpha Generation – Client Acquisition and Retention – Business Operations

• Metrics: – Investments – Value Measurement – Resource Allocations

• Advanced Analytics – Machine Learning – Natural Language Processing – AI

• Traditional Data – Reference – Market – Transactions

• Alternative Data – Individuals (Social Media, News) – Business (Credit Cards, Corporate Data) – Sensors (Weather, IOT, Satellite, Drones)

Note: 1 Profile is subject to change as more participants join and more interviews

get completed

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Early observations on industry use of Alternative data

Areas of focus Value vs. patience Spend and satisfaction • Leaf size—actual investment as %

of revenues • Leaf colour—level of satisfaction

• Pie size—planned investment as % of revenues

• Pie colour—level of satisfaction

• Bars—key area of focus for alt. data—Alpha, Client, Bus.Ops

• Colour—tangible value achieved

• It takes time to realize the value from alternate data

• It requires continues investment and alignment to realize the value

• Organizational patience begins to fade if concrete value is not realized in 2–3 years

• However, once the value is proven, additional investments flow in

• Most firms are realizing the value of alt. data in Alpha generation

• Tangible value realization on average takes 4–5 years

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UBS Asset Management—Spectrum of Investment Analytics UBS AM is harnessing different research and analytics capabilities towards evolving client objectives

• Building blocks analysis—client objective driven risk-return portfolio construction

• Historical index time series to analyze sleeve exposures

Multi-Asset Research

Traditional Equity & Fixed Income Research

Quant Research Evidence & Science Research

• Optimization of decisions through identification of biases

• Predictive models to identify operational risk through 1000s of metrics

Behavioural Research

• Markets, Sector, Issuer, Security research reports, company visits, analyst views

• Historical time series of equity, credit, rates, returns etc.

• Technical factors—momentum, value, growth etc.

• Research teams primarily utilizing alternative data and ML models to identify/support/reject investment ideas

CLIENT

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UBS AM approach to a central analytics capability Similar to enabling capabilities for a strong data foundation, we are enabling the required analytical capabilities centrally

1. Data • Internal governed masters & warehouses • Alternative data—structured • Alternative data—unstructured

2. Pre-processing • Reference entities (e.g. LEI in credit card sales data) • Unstructured data processing (e.g. ticker in news) • Enriched data loads

3. Storage • Operational Data Sets—leveraged for profiling, data quality, data catalogue etc.

• Consumable Data Sets—governed and optimized data sets based on model/API needs

4. Data governance • Data profiling • Reference data entity mapping • Data catalogue & lineage • Data quality (business rules based)

5. Quant models • Factor creation • Portfolio back-testing • Portfolio optimization

6. ML/AI models • ML models to support/reject analyst hypothesis • ML models to predict inter-quarter performance • AI models using NLP for news, reports, transcripts

7. Model governance • Ownership, usage, dependency map etc. • Performance (confusion matrix, ROC, Gini, RMSE,

learning rate etc.)

8. Access control • Data licenses • Results access (users and applications)

9. Insights • Investment ideas • Predictive insights • Alerts, Dashboards, APIs

Analytics boulevard

Data

Traditional data

Alternate data

Streaming data

1

Pre-processing 2 Storage

3

Data governance

4 Quant models

5 ML/AI

models

6

Model governance

7

Access control 8

Insights 9

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UBS AM data roadmap Successfully completed first phase of our transformation with Phase 2 well underway

Transform organization Start monetization

Create foundation

1. Establish central data office 2. Develop critical architecture components—

master data framework (client, product, security, prices etc.), data services, work-flows, data warehouse

3. Establish data governance framework, approach, policies

4. Align priorities with P&L, Regulatory and Cost objectives

1. Mandate data architecture and data governance principles for all new demands/ transformation

2. Complete phase I of "AM knowledge services" – data stewardship – business intelligence

3. Metrics culture—measure everything 4. Develop advanced analytics (ML/AI)

– analytics architecture – alternative data for research – ML and AI model development

1. Complete phase II of "AM knowledge services"—advanced analytics, quant, ML, AI

2. Provide fully digital experience to clients 3. Broader use of alternative data for research,

product innovation etc. 4. Advanced RPA—self-improving processes

through ML 5. Automated regulatory response—trust &

transparency 6. Offer AM platform to clients ("App Store"),

e.g. advanced analytics

• Enable central capabilities • Realize early benefits • Gain organization confidence

• Continued expansion and benefits of central capabilities

• Sharper focus on alpha generation and client service

• Game changing approach to alpha generation and in client service (tools, data, talent, training)

• Data driven business model

2016–2017

2018–2019

2020–Beyond

Defense Offense

Benefits

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DISCLAIMER Cautionary Statement Regarding Forward-Looking Statements This presentation is not a solicitation or recommendation to buy or sell any securities, investment strategy or other financial instrument. Offers to invest will be made only by prospectus or other offering memoranda, as applicable. No offers to invest will be made in any jurisdiction in which the offer, solicitation or sale is not permitted, or to any person to whom it is unlawful to make such offer, solicitation or sale. This presentation is intended for limited distribution to clients, prospects and business associates of UBS Asset Management. No part of this presentation may be reproduced or redistributed in any form, or referred to in any publication, without express written permission of UBS Asset Management.

Unless otherwise noted, all figures as at 31 December 2017. Published in February 2018.

© UBS 2018. The key symbol and UBS are among the registered and unregistered trademarks of UBS. All rights reserved.