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.
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.
How banks can accelerate AI
For both retail and commercial banks, the following five guidelines can help accelerate AI’s benefits.
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.
Focus on data. Collecting the right data, cleaning it up and standardizing it, and making it available will help deploy and scale AI.
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.
Think long term. When you start now on developing key capabilities, such as AI upskilling, you’ll likely see benefits for years to come.
- Make AI responsible AI. To reduce AI’s risks and make it explainable, apply the responsible AI toolkit.
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