AI in Banking: How to Reduce Costs and Improve Service

The potential role of artificial intelligence (AI) in banking is massive. Predictive AI already supports many standard banking practices, such as chatbots managing routine inquiries or call centre agents’ dashboards. As generative AI continues to evolve, we expect lots of time-saving opportunities around rote tasks that improve the customer experience due to AI’s ability to produce natural language content, images, and coding. McKinsey estimates that banks could add $1 trillion in value annually through strategic use of AI.

To take full advantage of AI’s now-and-future potential, banks must take steps to clean up their data, analyse their existing systems, and identify process challenges that could benefit from the technology. Here are four ways we expect forward-thinking banks will use AI to improve both the employee and customer experience.

Data: Safeguard privacy, security, and trust with financial AI

Nearly nine in 10 analytics and IT leaders are making data management a high priority in their AI strategy. Banks are laser-focused on keeping their data secure: It’s fundamental to building trust with customers. Yet nearly half of executives say they believe AI introduces security risks, while 59% of consumers say they don’t believe AI is secure. Banking regulators are concerned as well, especially when it comes to generative AI, which relies on large language models (LLM) to generate responses.

“Getting your data in order is fundamental,” says Amir Madjlessi, Managing Director and Banking Industry Advisor at LIKE.TG. “You need to evaluate the quality and quantity of your data and, if necessary, upgrade data collection and management processes. Without those steps, your AI won’t be able to extract relevant and accurate insights from your systems.”

Once you’ve prepped your data, deploying AI in banking requires further unique data management, with varying access rights for different functions. For example, to follow fair lending practices, banks must hide demographic information like religion or country of origin from lending officers. But that same information must be available to regulators as evidence of fair lending.

Data management is even more complex when it comes to generative AI, which relies on LLMs to learn how to properly respond to prompts. Leveraging solutions that have built-in data integrity like ethical guardrails can help banks address data challenges and meet compliance rules. LIKE.TG, for example, has a zero data retention policy for LLMs — we don’t share client data with external LLMs.

Sales: Discover opportunities faster

AI can act like a personal assistant, helping relationship managers improve their lead and opportunity scoring across all kinds of services and products — from checking bundles to secured loans. AI improves forecasting by predicting likely performance outcomes for different business lines, whether investment, commercial, or retail banking.

In a single dashboard, predictive AI can surface relevant insights to deepen existing relationships or capture new clients for the bank. Generative AI can integrate data from third parties as well as internal sources to make suggestions in the flow of work, which increases the accuracy and relevance of those recommendations.

With the power of both predictive and generative AI, the relationship manager can understand the best channel to reach the client, with a relevant and compelling offer. These functions help reduce the time relationship managers require to fully understand customer needs across the bank while improving their experience.

Marketing: Scale next-level personalization

Creating marketing segments and subsegments used to take weeks, and results could be lacklustre and generic. Generative AI is changing that, enabling marketers to create segments within the client database using natural language prompts — and the results are available in just seconds.

These tools help marketers quickly build the most relevant offers or promotions, then test and learn from each, to further refine segmentation. For example, marketers using Einstein Copilot can target customers with low savings coverage by creating an offer recommending products or services that improve financial security. The marketers can then use generative AI-powered, prebuilt email templates to share that offer with the targeted customer. Over time, the messaging gets refined as the AI engine learns how customers respond to the content. The net result: Offers become super-personalised and conversion rates improve.

One bank testing Einstein Copilot has seen engagement jump three to four times. The reason? The messaging is rooted in real-time customer behavior and actions, making the recommendations connected and authentic.

Service: Improve agent training and customer satisfaction

Turnover among contact centre agents is an industry-wide problem. Continuously training and onboarding an endless queue of new employees is expensive and ineffective. Using AI to improve the training experience and the day-to-day workflow enables agents to onboard faster, which can contribute to better retention rates. It also makes the service experience more pleasant for the customer.

Generative AI can help surface the precise information contact agents need to quickly resolve issues, by populating content for known answers based on the actual language the customer uses to describe a problem. This empowers agents to make smart decisions, and that’s important in cases that require judgment calls — like whether it’s OK to reverse a charge for an unhappy customer.

Plus, AI provides smarter tools for spotting fraud and verifying identity, which helps agents understand their next best actions. LIKE.TG, for example, now has an out-of-the-box, know-your-customer (KYC) protocol for identity verification and credit scoring.

PenFed Credit Union plans on adding Einstein to internal (and eventually customer-facing) processes with generative AI. Einstein will act as a virtual assistant, suggesting chat and email responses service agents can use to answer questions faster and reduce queues. The assistant will propose responses to a chat or member question, beginning with PenFed’s internal employee support line before it expands to its members.

AI in banking is just getting started

AI in banking has the potential to help banks offer customers more while streamlining costs and effort behind the scenes. In the back office, AI has the potential to shave an estimated 6%–10% off operating budgets spent on compliance by making customer identification, verification, and risk screening more efficient. And, when it comes to clients, AI can help your commercial bankers or wealth managers turn chats into actions — like calendar invitations, automated emails that summarise conversations, and even suggestions for new engagements.

To take advantage of that potential, you need to be laying the groundwork for success now. That means determining your goals for your institution and then getting your data ready for all that AI can do for you in the world of banking.

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