AI strategy for banking: How to prioritize high-value AI opportunities

Workflows: Transforming processes with AI

An AI strategy focused on being effective will scrutinize each workflow. Banks will step back and ask, “Do we even need this process? Is there a better way to achieve our objective?” 

Consider a customer seeking a mortgage for an investment property. Traditionally, this involves multiple departments, numerous touchpoints, and significant manual work. AI can allow you to change this experience altogether.

A focus on using AI to be more effective could mean using an autonomous assistant to interact with the prospective borrower who describes their need on the website.

The AI assistant obtains permission to access the customer’s credit information and provides an estimate of the potential loan amount and qualifying interest rate range. It then offers to initiate an online application, outlining the required documents and information. Customers can apply immediately, but the institution has the option for a manual review to finalize the loan amount and rate. The streamlined process frees up the underwriting team to focus on more complex applications.

How to rethink workflows

Here are a few steps that will help financial institutions make this transformation happen:

1. Map out existing workflows: Document every step involved in current workflows. Identify all roles, actions, and systems used in each process.
2. Identify value-adding steps: Review each step and determine if it truly adds value or exists because “that’s how it’s always been done.”
3. Pilot AI-driven changes: Use customer feedback to identify and implement AI in parts of the workflow where redundant tasks, such as data gathering or preliminary assessments, can be replaced. Pilot programs will help identify challenges and measure the effectiveness of these changes iteratively. Galvanize support around early wins produced in the pilot process and use this momentum to implement the new workflow more broadly.
4. Integrate AI with human oversight: For areas traditionally requiring human expertise, AI can handle the initial stages and flag exceptions for review. This approach makes the overall process more effective without sacrificing quality control.

This is the key difference between efficiency and effectiveness—one approach improves old processes; the other fundamentally redefines them. AI affords banks the chance to focus on what can make the biggest difference in achieving their goals. It creates a more effective experience for customers and staff and a more impactful outcome for the institution.

How to evaluate an AI pilot 

Once an institution identifies opportunities for AI, it should consider which use cases are best suited for a controlled pilot. A few factors can help narrow the options: 

• Business value: What measurable outcome could the pilot improve for the institution, its employees, or its customers? Consider whether it could improve capacity, turnaround time, consistency, decision-making, or the customer experience. 

• Time to value: How quickly can the institution test the use case, measure its effectiveness, and determine whether the AI is producing the intended result? 

• Implementation effort: What data, integrations, process changes, vendor support, and employee training will be needed to put the pilot into practice? 

• Risk and oversight: What security, compliance, data privacy, or other risks does the use case introduce? Determine where human review is needed and who will remain responsible for the outcome.