REIMAGINING SUSTAINABLE INVESTING WITH

AIFS

PERSPECTIVES

Applying AI to drive profit with purpose for asset managers in ESG investing

Contents03 Integrating ESG in asset management

06 ESG investing: All eyes on AI

08 ExplAIned−Helping you ride the ESG investing wave

10 References

Contents 2

Integrating ESG in asset management 3

Integrating ESG in asset managementTaking stock—around 30 percent of global assets under management are ESG-principled investment

The Asset Management industry is experiencing a paradigm shift—changes in investor preferences, margin compression, regulatory developments and unprecedented technology-led disruption.

Today’s investors are looking to make a social and environmental difference. They want to put their money in investments that not only generate high returns but also serve a purpose.

It’s no surprise then that traditional mutual fund and investment firms are incorporating “long-term” and “risk-free” ESG-based investments into their existing business models. ESG investing, or sustainable investing, incorporates environment, social and governance (ESG) principles into investment decisions and portfolio construct.

ESG investing presents a huge opportunity, with assets under management (AuM) of ESG-related funds or ESG-principled investing currently accounting for around US$31 trillion—almost one-third of global AuM.1

ESG-dedicated funds control about US$850 billion in assets (or 2 percent of global AuM) and are expected to see huge growth in the coming years. A majority of these funds are equity-based and accounts for about US$560 billion (See Figure 1). Meanwhile, ESG exchange-traded funds and exchange-traded products account for approximately US$25 billion (See Figure 2). The ESG investing landscape is also seeing the rise of green bonds that finance environmental projects.2

Source: Bloomberg Finance L.P., and IMF staff calculations.Note: Refers only to ESG-mandated funds, but the universe of funds that use ESG principles is significantly larger.

Figure 1: Betting big on sustainability

(US$ billion)

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Integrating ESG in asset management 4

Source: Global Sustainable Investment Review3

Asset management is going green

Figure 2: Global ESG ETFs & ETPs on a rally

Source: ETFGI data sourced from ETF/ETP sponsors, exchanges, regulatory filings, Thomson Reuters/Lipper, Bloomberg, publicly available sources and data generated in-house.

Note: This report is based on the recent data available at the time of publication. Asset and flow data may change slightly as additional data becomes available.

ESG ETF/ETP Assets (US$ billion)

Assets (US$ billion)

2019 Jan-end

The domain of ESG investing has evolved into mainstream for global asset managers. Institutional investors, mutual funds, hedge funds and family offices are taking ESG considerations into account and keeping in mind new ratings, market events, news and research while shaping decisions on portfolio construct.

However, despite the increasing pressure on investment managers from investors and clients to adopt ESG investing, the massive amount of unstructured and incompatible data presents

a challenge to asset managers seeking ESG-friendly investments. This is where AI comes in. It can help reimagine Asset Management. AI technologies can process massive amounts of data, including ESG-related data, and provide valuable, actionable insights that can be applied for ESG investing.

• Around 30 percent of global AuM, which is close to US$31 trillion, are invested under ESG criteria. • Global AuM under ESG investing has risen 25percentinthelastfiveyears and the market is growing.• Japan is the third-largest center of sustainable investing, after Europe and the US. • Environmental is the strongest factor of growth in the US and Japan, while governance is a major driver of growth in Europe.

• In Asia, Japan and India are the most active impact investment markets, benefiting from a broad range of investor and entrepreneur experience with impact investing.

As Figure 3 shows, responsible investing is gaining wider acceptance. Around 2,100 investment management members have subscribed to the Principles for Responsible Investment (PRI).5

Quick Facts4

Figure 3: The rise of impact investing

Source: https://www.unpri.org/pri/about-the-pri

Integrating ESG in asset management 5

ESG investing: All eyes on AI From cyborgs of science fiction to a responsible player in the sustainable investment theme, AI is key to ESG integration for asset management

AI is powering responsible investing. AI-based solutions are providing actionable insights for ESG integration into asset management.

Beyond ESG investing, the advancement of machine learning models and quantitative methods can well be applied to other thematic and non-thematic portfolios, such as dynamic sector rotation strategies, multi-factor tactical asset allocation, and more. In recent years, such innovations in ESG investing are making it easier for investors to find funds that suit their world view. AI-powered ESG funds are gainingsignificantmarketshare,asshownby their growing popularity among global asset managers such as ESG Quant Funds and multiple ESG indices.

TheglobalAIinthefinancialAssetManagementmarket is expected to witness a CAGR of 33.48 percent to reach a US$11.39 billion revenue by 2025.6

While referring to ESG investing and AI, there are two primary considerations to bear in mind. First, AI can be an investing theme, identifying the companies that are in the business of developing such technologies. Secondly, there’s the exposure of any given portfolio company to AI.

Particularly in the second case, AI systems based on Knowledge Graph technologies can make a difference. Knowledge Graphs can project billions of relationships across hundreds of millions of data points and augment the capability of investment analysts and researchers by screening more information and finding unintuitive inferences that result in a faster and better quality of insights.

ESG investing: All eyes on AI 6

An AI-driven data capture would classify, extract and validate the right set of ESG factors based on economy, sector, company strategy and governance and identify material factors that affect the company and apply this data to the asset manager’s ESG portfolio strategy. The foremost activity in the integration of ESG framework is to gather the relevant set of structured and unstructured ESG data from multiple sources (public information, reports and statements, offline data and social media).

QualitativeAnalysis

Capture/Sense

01

A quantitative analysis will identify the correlation of ESG factors with various financial factors such as price movements. The AI-based ESG model willinterpretandanalyzetheimpactofnon-financialfactorswiththefundamentalfinancialdataandadjustthefinancialforecastandvaluationmodel for ESG-themed portfolios appropriately. Also, the AI-based Knowledge Graph can help in the fund or portfolio view across dynamic variables to better align and provide insights to investors’ goals. Data analysis and interpretation have a huge role to play in using AI for global asset management given the huge volumes of data available for non-financial and financial data.

02

Quantitative Analysis

Interpret/Analyze

Investment Decision

Learn

03 Based on the right set of data captured and interpreted for the ESG-based portfolio model, the system can enable the decisioning of asset managers to “buy/sell/hold/don’t invest” securities to generate alphas or reduce risks. This will boost the decision making capability of asset managers in managing large complex portfolios that have significant ESG constructs.

Truvalue Labs, for example, uses AI to provide alternative ESG data insights to institutional investors such as the UK’s £30 billion Brunel Pension Partnership, State Street, or Government Pension Investment Fund. It uses natural language processing and machine learning to mine data from more than 100,000 non-company sources.

JP Morgan leverages AI-based Themebot to scrape through keywords in patent filings and help find companies linked to new low-carbon technology. Or take the case of Ball Corporation, one of the largest manufacturers of aluminum cans in the US. The company learned how a traditional analysis could miss key ESG themes.

Ball Corporation had scored poorly with ESG rating agencies because of its high-energy usage. However, digging deeper into the company’s ESG data and products, the company’s data team saw an opportunity for future outperformance—the fact that over 75 percent of the aluminum that has ever been produced is still in use today. In the long run, it reduces energy consumption and pollution.

As the Ball Corporation example shows, an AI-based ESG analytical model to capture, interpret, learn and act can address enormous structured and unstructured data and mine valuable insights from it (see diagram below).

Figure 4: Four steps to integrating ESG in asset management

The AI-based Analytical Framework for ESG Investing

ESG investing: All eyes on AI 7

AssessmentAct

04 An AI-based ESG model with machine learning capabilities will automatically assess by learning and improving from experience of how the material financial and non-financial factors, investment analysis and investment decisions have been acted upon in various scenarios by asset managers. This will provide insights into future investment analysis and have an impact on subsequent investment decisions.

ExplAIned - Helping you ride the ESG investing waveWith our distinctive capabilities and deep understanding of asset management and ESG, we can help clients integrate the ESG model into their investment decision using the AI-based framework to capture, analyze and act on the vast amount of alternative data related to ESG.

Deep industry knowledge Our global team works with 200+ asset managers, including 7 out of the world’s 10 biggest asset managers.

Broad functional experienceIndustry veteran consultants with an average 15 years of experience in investment systems and enterprise data architecture, and vendor product knowledge.

Emerging technology acumen Accenture is a trusted advisor on innovation and strategic transformation for the Asset Management industry. Our business, technology and industry knowledge are enabling clients at every step of the way—in AI use case definition, exploration, business process implementation in portfolio construction, portfolio maintenance, order-trade execution, fund administration and operation.

Deep functional experience Our global team of 120 sustainability experts have delivered 350+ sustainability projectsinthelast2years.

Thought leadershipOur latest research, in partnership with the UN Global Compact, is the largest-ever global CEO survey on sustainability.

PartnershipWe have partnered with Arabesque and use their advanced analytics and AI-based tool, S-RayTM that provides GC score & ESG score. A range of NGOs, thought leaders, and investors. Big Data providers to offer a comprehensive perspective on ESG issues.

RecognitionOur own sustainability programs have received numerous accolades and award, including the #2 ranking in Responsibility Magazine’s 100 best Corporate Citizens.

Accenture’s artificial intelligence capabilities Focuses on building assets and solutions to solve some of the real and biggest issues the industry faces, enrich user experience, minimize human intervention, and reduce time and effort on various activities and processes in Financial Services.

Numerous AI assetsVirtual agents, ML, computer vision, speech-to-text and text analysis tools, sentiment analytics algorithms, our proprietary Financial Advisory framework, AI-based Knowledge Graph.

Accenture investment$600 million in AI capability building185+ Related Patents and Applications;Best people: 4,000+ data scientists, data engineers, architects & technology specialists across the globe

ExplAIned - Helping you ride the ESG investing wave 8

We can help our clients by extracting insights from the vast amounts of data that comes in various formats from different sources. With our existing AI-based solutions, we can customize our assets based on the clients’ ESG strategy to help extract and provide meaningful insights by using sentiment analytics, and the AI-based Knowledge Graph that will help in cross referencing the ESG data and extract securities to fit as per the ESG criteria.

Accenture developed a Competitive Agility Index that scores 7,030 companies across 20 sectors based on data from the past two and a half years across three interdependent dimensions of competitiveness: growth, profitability,andsustainability/trust. The Index relies on both publicly available data, including historical data and future consensus data, and innovative sustainability and trust measurements. Accenture worked with Arabesque to source data and create a new proprietary measure of trust.

With our partnership with Arabesque,7 we generated insights into extra-financial performance, helping the organizations to bridge the gap in areas of underperformance to enhance their financial performance. Arabesque’s unique advanced analytics tool S-Ray™ uses self-learning quantitative models and big data to analyze and monitor the sustainability performance of over 7,000 of the world’s largest corporations on real-time basis.

At the crossroads of technology, innovation and sustainability, AI can make a dramatic impact on ESG investing—accounting for

environmental, social and governance risks, and opportunities in investing. However, while AI can unearth key data for investors seeking sustainable investments, discerning unreliable information will be a crucial challenge. When it comes to decision making, machines are still catching up with human intelligence. But, there’s no denying that advances in AI, which encompasses a variety of emerging computer technologies, have made it easier than ever before for computers to automate complex tasks at incredible speed and scale, thus revolutionizing how companies work with data.

These AI capabilities will prove useful for ESG investing, which reflects the growing sensitivity of consumers to how sustainably companies operate as factors in their buying decisions. They are of increasing interest to investors who are concerned about companies adopting practices that will mitigate risks and ensure long-term sustainability.

AI holds the answer for investment managers who are coming under increasing pressure to measure ESG criteria in their portfolios.

ExplAIned - Helping you ride the ESG investing wave 9

https://blogs.imf.org/2019/10/10/connecting-the-dots-between-sustainable-finance-and-financial-stability/

https://www.imf.org/~/media/Files/Publications/GFSR/2019/October/English/ch6.ashx

http://www.gsi-alliance.org/wp-content/uploads/2019/03/GSIR_Review2018.3.28.pdf

http://www.gsi-alliance.org/wp-content/uploads/2019/03/GSIR_Review2018.3.28.pdf

https://www.unpri.org/pri/about-the-pri

https://www.researchandmarkets.com/reports/4774697/global-ai-in-financial-asset-management-market.

https://newsroom.accenture.com/news/half-of-companies-on-the-accenture-competitive-agility-index-experienced-a-major-drop-in-trust-losing-out-on-180b-in-potential-revenues.htm

https://www.responsible-investor.com/articles/arabesque-feature

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About Accenture

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Authors

Copyright © 2020 Accenture.All rights reserved. Accenture and its logo are registered trademarks of Accenture.

Sandip SinhaIndustry & Function Lead,Capital Markets,Advanced Technology Centre, India

Mohan KumarasamyManaging Director,Financial Services / Client Account Leadership,Singapore

Shakti NaikBusiness and Integration Architecture Manager, Capital Markets,Advanced Technology Centre, India