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Asset & Wealth Management

AI for asset and wealth managers in 2021: Industry priorities and delivering benefits

Asset and wealth management (AWM) firms are advancing with projects that utilize artificial intelligence (AI): computer systems that can sense their environment, think, learn and take action in response. AI is one of the financial sector’s top priorities, and some asset and wealth managers are moving aggressively on AI for everything from investment decisions to the customer experience and risk management.

Here are the top findings about how AWM firms are using AI, according to our survey of US companies that are actively using AI. We learned where they are encountering obstacles and how they might overcome those obstacles.

Where asset managers are finding AI benefits — right now

The overwhelming majority of asset managers who have invested in AI are already reporting real-world benefits: improved decision-making, product and service innovation, better customer experiences and reduced risks top the list. Why are so many AWM firms doing so well with AI? 

One answer is that many asset managers hold treasure troves of data — and data is the raw material that AI can convert into advanced automation and more accurate forecasts and insights. Another answer is asset managers have so many well-defined use cases, where AI can cut costs or improve operations.

For example, AI enables firms to verify, reconcile and analyze data, helping to enhance the investor experience. With the better data that AI itself helps create, AI can help optimize portfolios, predict transactions and make highly accurate forecasts. It can automate and deepen trader risk profiling, market sentiment analysis, news and event analytics, reputational risk management and client profile generation. It can even help directly manage investment funds. To support these and other use cases, our survey found that asset managers’ top two AI priorities for 2021 are to identify, collect and aggregate data for AI, and to ensure that AI’s data meets regulatory requirements.

AI helps AWM firms enhance the investor experience
AI helps AWM firms enhance the investor experience

Use case: How AI can help make a better investor

What makes a better investor? Better information, received faster — which AI can help provide. Leaders in this space are using an AI solution to automatically ingest and reconcile data (such as expenses, transactions and reports) from multiple sources (ranging from PDFs to APIs.) The solution detects errors or breaks in the data, notifies the right people to remedy the break and observes how the remediation took place — so the next time that break occurs, the AI itself can fix it automatically. This solution also serves as an auditable tool for capturing approvals for the necessary overrides, and it provides a lineage of root causes for data breaks. This lineage helps AWM firms prevent such errors from recurring, as well as better allocate resources to enhance their overall IT and operations landscape.

Asset managers face these AI obstacles

Yet despite all these realized and potential benefits, many AWM firms who seek to deploy AI are getting stuck — usually in the same places. One of the top obstacles to AI in AWM firms is a highly understandable caution. Asset managers are often highly focused on risk mitigation — and there’s concern among many AWM executives that if AI solutions aren’t thoroughly trustworthy, they could create new risks. Top AI-related concerns that asset managers noted in PwC’s survey include the potential for cyber and privacy threats — 71% of respondents said that responsible AI tools to help with privacy and cyber issues would be a top priority in 2021 — and worries that they might not understand exactly how an AI tool is making potentially critical decisions. As highly sophisticated technologies, AI solutions will also need the same level of controls and scrutiny (such as service organization controls [SOC 2] reports) as other critical business processes receive.

Another obstacle is financial: In an often challenging economic environment, AI deployment may require a business case that can deliver fast ROI. That fast ROI may depend on AI’s ability to access verified, standardized data from across multiple functions and business lines, so that it can deliver solutions at scale. Yet many firms have data sets and teams in silos.

The challenge of making a business case for AI is further complicated by a culture common to many firms: one that is wary of emerging technologies and may lack the mindset to fully utilize them. Many firms, after all, have a secure book of business. They may not feel an urgent need to innovate. Yet AI is already providing so many benefits to so many asset managers, this caution itself may create a risk — of falling behind the competition.

Still, many asset managers are making it a priority to meet these AI challenges.  And, fortunately, it’s possible to overcome these obstacles — with the right focus.

Bar chart titled
AWM firms’ top priorities for meeting AI challenges in 2021
Developing AI models and data sets that can be used across the company
Training current employees to work with AI systems
Making AI systems responsible and trustworthy
Measuring AI’s return on investment
Recruiting workers who are already trained to work with AI systems
Managing the convergence of AI with other technologies
Maintaining AI systems that are in production
Standardizing, labeling and cleansing data for use in AI systems
Making the business case for AI
Moving AI initiatives from pilot to production
Creating AI-related governance policies across the business
Not sure
Q: What AI-related challenges are the top priorities for your company in 2021? Source: PwC AI Predictions Survey 2021. Base: 41

How asset managers can accelerate AI

For both AWM firms that are already deploying AI and for those that are behind the curve and need to catch up, the following five guidelines can help accelerate the benefits.

  1. Choose AI that does its own data work. AI runs on data: up-to-date, in scope, standardized, verified and accessible. The right AI solutions will help build their own foundation with tools to ingest, reconcile, verify and standardize data from multiple sources in real time.

  2. Start small and safe. Many AI solutions can begin with bite-sized solutions and quick ROI. The best also have governance, security, compliance and ethics built in. If you choose the right AI tool, you can see quick wins while rigorously controlling your risks.

  3. Get set to scale. Choose solutions that may start in one place, but can solve other problems too once their worth is proven. Spending time and resources building out a solution is often just the initial investment; you can potentially significantly increase your ROI if you then scale it. When well designed, many AI solutions will also overlap and enhance each other.

  4. Build out capabilities. Some initial AI deployments may be plug and play, but as your solutions advance, you’ll need to upskill your workforce. You’ll also need to build a culture and a structure that can take advantage of greater automation and quickly act on real-time, data-driven insights.

  5. Watch the bias. Biased AI — AI that makes decisions that are systematically unfair to certain groups of people — can cause harm in many ways, ranging from brand reputation to recruitment to investment decisions. Be sure to consider solutions that can mitigate biases in AI algorithms. 

AI isn’t just the future. For more and more asset and wealth managers, it’s the present, delivering benefits to the back office and the business today. With these five guidelines, AWM firms can raise their odds of being an AI leader in their industry this year and for many years to come.

PwC's Asset and Wealth Management Services

PwC's Asset and Wealth Management Services

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Bernadette Geis

Asset and Wealth Management Trust Solutions Leader, Boston, PwC US


Brian Marsh

Director, Arlington, PwC US


Anand Rao

Global AI Lead; US Innovation Lead, Emerging Technology Group, Boston, PwC US


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