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Banking & Capital Markets

AI for banks and capital markets in 2021: benefits, challenges and the path forward

Retail and commercial banks have big ambitions for artificial intelligence (AI), which we define as computer systems that can sense their environment, then think, learn and take action in response. Here are the top findings from the banking and capital markets (BCM) respondents in our survey of US companies actively using AI: the top benefits they see, the top obstacles they report and how they might overcome those obstacles.

Bar chart titled
AI in BCM today: driving efficiency and growth
Operate more efficiently or increase productivity
%
Grow revenue
%
Achieve cost savings
%
Create better customer experiences
%
Innovate our products and services
%
Improve internal decision-making
%
Improve talent retention and recruitment
%
Enhance employee training and upskilling, including remotely
%
Reduce risks
%
Q: What are the primary goals of your company’s AI strategy? Source: PwC AI Predictions 2021. Base: 53

Banks’ top goal for AI is to increase efficiency. Yet in a sign that AI is accelerating its move into the business, a full 40% of BCM respondents cited revenue growth as a top goal for their AI strategy. 

Top use cases for retail banks, as reported by PwC experts assisting banks with AI initiatives, include (but are not limited to) using AI to: 

  • Target marketing

  • Enable and enhance virtual wallets

  • Identify suspicious transactions

  • Underwrite loans based on nontraditional data sources

  • Automate analysis of contracts

  • Predict customer churn

Top AI use cases for commercial banks also include AI to:

  • Settle, route and monitor trades

  • Execute algorithmic trading

  • Provide research analysts with insights

  • Analyze market sentiment and nontraditional data

  • Assess credit risk for SMB loans

  • Reconcile data for customers who cross business lines and national borders

Despite all these realized and potential benefits, many banks that seek to deploy AI are getting stuck — usually in the same places.

Banks face these AI obstacles

Making AI systems responsible and trustworthy is banks’ top challenge, cited by 42% of BCM respondents. Retail banks face special challenges over consent management for consumer data. Commercial banks face an especially high demand for explainable AI: Traders, for example, need to understand why AI is recommending a certain trading strategy.

Banks’ second most common AI challenge (called out by 38%) is managing its convergence with other technologies. It’s an especially large obstacle for banks that have been slow to move to the cloud. Challenge number three, cited by 34%, is training current employees to work with AI. That includes risk and regulatory teams, who often lack the needed skills and processes to assess AI models’ impact.

Bar chart titled
Top AI challenges for BCM companies
Making AI systems responsible and trustworthy
%
Managing the convergence of AI with other technologies
%
Training current employees to work with AI systems
%
Creating AI-related governance policies across the business
%
Standardizing, labeling and cleansing data for use in AI systems
%
Developing AI models and data sets that can be used across the company
%
Recruiting workers who are already trained to work with AI systems
%
Maintaining AI systems that are in production
%
Making the business case for AI
%
Moving AI initiatives from pilot to production
%
Measuring AI's return on investment
%
Q: What AI-related challenges are the top priorities for your company in 2021? Source: PwC AI Predictions 2021. Base: 53

How banks can accelerate AI

For both retail and commercial banks, the following five guidelines can help accelerate AI’s benefits.

  1. Move to the cloud. Cloud providers offer AI capabilities and can often help integrate AI tools with other technology offerings and more complete data sets.

  2. Focus on data. Collecting the right data, cleaning it up and standardizing it, and making it available will help deploy and scale AI.

  3. Centralize capabilities. Bring AI, analytics and automation together to help best allocate resources, fully utilize data, improve governance and scale up solutions for faster ROI.

  4. Think long term. When you start now on developing key capabilities, such as AI upskilling, you’ll likely see benefits for years to come.

  5. Make AI responsible AI. To reduce AI’s risks and make it explainable, apply the responsible AI toolkit.
Banking and capital markets

Banking and capital markets

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Mohib Yousufani

Principal, Growth & Digital Innovation, Banking Transformation Leader, Chicago, PwC US

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Anand Rao

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

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