04/16/2019

Top use cases for artificial intelligence in banking

Beth Bourgoin April 16th, 2019

Analysts estimate a $1 trillion AI opportunity for the industry.

$1 trillion. That’s the magic number for artificial intelligence (AI) in banking. Numerous analysts predict that in the next 10 to 15 years, an influx of AI-powered applications will create $1 trillion in savings for the industry.1

That figure comprises front and back office opportunities—everything from more efficient data processing to automated customer service to shifts in staffing levels. It equates to a 22 percent reduction in operating expenses.1

No matter how you slice it, it adds up to a monumental opportunity.

AI investments fall into four categories

How your bank captures its share of the AI opportunity will depend on your strategy and resources (in-house data scientists or fintech partners). These are the top four categories of AI investment to consider.

Top use cases for AI in banking

  • Risk management and compliance
  • Customer experience
  • Operational efficiency
  • Revenue growth

Risk management and compliance

This is the most mature category for FIs, garnering the bulk of early bank investments. Credit card processors, for example, have relied for years on sophisticated AI algorithms to scan millions of transactions and detect potentially fraudulent purchases.

Newer AI initiatives focus on smarter, faster detection of fraud, and stronger decision-making.

Banks like HSBC are testing applications that automate Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance. These tools can review and extract data from a variety of sources during onboarding, or examine millions of transactions and quickly flag suspicious activity.

AI is also fueling credit and underwriting decisions. The fintech Upstart claims the first AI-driven lending platform. It uses non-traditional criteria, such as a borrower’s education and job history, in its consumer credit decisions.

Analysts estimate FIs could save $217 billion through risk, compliance and authentication projects.2

Customer experience

Numerous FIs are also piloting AI projects that strengthen engagement with retail and commercial customers. Recent improvements in natural language processing (NLP)  and image recognition  unleash a number of possibilities, from chatbots and virtual assistants for customer service, to facial and biometric recognition that replaces password-based authentication.

Most projects in this category aim to reduce “friction points” when the customer interacts with the bank. With most, the goal is not to replace bankers or threaten human-to-human relationships. Instead, AI delivers greater customer insights to support stronger conversations. Automation also eliminates routine and redundant tasks so staff can refocus their efforts on more valuable activities.

For example, when an AI-powered interface helps customers reset their access codes or passwords automatically, it frees customer service staff for more complex interactions.

Many banks have already launched conversational interfaces; most are embedded in the bank’s mobile app. Wells Fargo channels their chatbot through Facebook Messenger. Ally Bank, one of the early movers in this space, takes assistance a step further: Ally AssistSM works within their app, and the new Ally SkillSM lets users manage their accounts with voice commands, through the Amazon Alexa virtual assistant.

Operational efficiency

Streamlining processes and automating workflows is a natural fit for AI. Banks now leverage robotic process automation (RPA)  for everything from contract reviews to reporting. Next-generation applications take RPA to the next level—called intelligent automation (IA) —where these bots begin to train and improve themselves.

Efficiency programs take advantage of AI’s core strength: handling unstructured data. Unstructured data typically holds great value, but resides in a PDF, email or format that’s difficult for a traditional system to access. Applying AI frees staff from tedious data entry and reduces errors. It surfaces new insights that were previously “trapped” in data.

With an eye on efficiency, J.P. Morgan rolled out its COiN platform to streamline review of commercial credit agreements; what took staff 360,000 manual hours, the new AI-driven tool can accomplish in seconds. BNY Mellon reported similar results from implementing AI-powered bots for straight-through processing; employees saved $300,000 annually in their funds transfer area alone.

Analysts predict up to $200 billion in savings for FIs through back office efficiencies.2

Revenue growth

Generating new revenue is one of the most exciting AI opportunities. With greater customer insights and automation, banks can deepen customer relationships, provide more support to bankers and sales teams, execute stronger marketing efforts, and even launch new products.

It starts by using data to make customer relationships more personal. Banks and credit card issuers, for example, are using AI to enhance their loyalty programs. Offers are based on each cardholder’s behaviors, spending habits and even travel locations, rather than generic points or rewards. In marketing, AI enables customer segmentation with far greater precision. Outcomes may be upsell/cross-sell recommendations or even financial guidance from “robo-advisors.”

FIs can also harness AI to provide early warning when high-value customers are at risk, helping to stem attrition. These tools monitor numerous variables, from decreased usage of the bank portal to fluctuating transaction levels, then alert the banker to take action.

AI-powered products are the final opportunity for growth. For example, Integrated Receivables (IR) solutions can leverage sophisticated algorithms and machine learning technologies to match customer invoices with electronic remittances. This technology solves a common issue for businesses that receive high volumes of ACH receivables. When AI pairs with IR, banks can offer a compelling product that adds value to their relationships with corporate customers, demonstrates the bank’s commitment to innovation, and introduces an entirely new revenue stream.

Data access becoming mainstream

As these use cases demonstrate, there’s no shortage of AI opportunities in banking. Consumer appetites for convenience and personalized service are also driving the AI revolution. According to a recent Accenture survey, two-thirds of consumers globally are now comfortable granting their bank “greater access” to their personal data—so long as there’s a stronger experience or a tangible value to be had in exchange.3 It all bodes well for forward-thinking FIs.

Up next in our AI series: How chatbots, virtual assistants and conversational interfaces are poised to disrupt commercial banking.


Sources & Further Reading

¹Financial Brand. (2018, May 29). Artificial Intelligence and the Banking Industry’s $1 Trillion Opportunity.

²Autonomous. (2018, April). #Machine Intelligence & Augmented Finance.

³Accenture. (2017). 2017 Global Disruption & Marketing Consumer Study: Banking Report.

This content is accurate at the time of publication and may not be updated.