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For years, financial institutions have invested heavily in artificial intelligence to streamline operations and deliver next-best-action prompts to customer support representatives. Yet, despite highly personalized, algorithmic suggestions, actual conversion rates on these prompts frequently hover under one percent. The fundamental issue isn’t the recommendation engine itself—it’s what happens after the prompt appears on the screen.
While technology excels at pinpointing which customer to target, when to reach out, and what offer to display, it often falls short at managing the actual interaction. Once an automated prompt reaches a screen, the outcome relies entirely on the individual employee handling the call. Unfortunately, many advisors ignore these pop-ups entirely, and only a small fraction possess the skills to guide that prompt toward a closed deal. This persistent disconnect points to a widespread issue in modern banking: AI is being deployed as a cost-cutting tool rather than a driver of top-line growth.
The Flaw in Prioritizing AI Deflection over Conversion
Most financial institutions evaluate their AI platforms using operational metrics such as call deflection, ticket containment, and reduced handle times. While these key performance indicators (KPIs) highlight operational efficiency, they fail to measure lost revenue or abandoned growth opportunities.
When a customer calling about a mortgage or personal loan is diverted to an automated bot, the institution’s cost-per-contact drops, making the system look like a success on paper. However, major financial commitments are rarely completed without human reassurance. When forced into self-service channels for complex decisions, many applicants simply abandon the process.
This strategic misalignment occurs because AI initiatives are typically managed by Chief Operating Officers or customer support heads whose primary responsibility is controlling operating expenses. To unlock real value, Chief Credit Officers and revenue leaders must take an active role in steering AI strategies, measuring success by application completion and funded loan balances rather than operational cost savings.
Unlocking Hidden Value: Replicating Top Financial Performers
Even for relatively simple financial products, such as certificates of deposit (CDs), customers routinely seek personalized guidance before committing their funds. Wealth management divisions have long offered high-touch service to affluent clients, but scaling that level of care across mass-market retail banking has remained a challenge.
In almost every financial institution, a massive performance gap exists between top-performing bankers and the team average. Veteran advisors naturally intuitively understand customer concerns, navigate objections, and guide conversations toward positive outcomes. Regrettably, few banks systematically analyze what these top performers do differently or leverage those insights to train their automated channels.
Shifting the focus from simple static forms to guided conversational experiences yields dramatic results. For instance, one auto refinancing lender recently increased digital application conversions by over 30 percent without altering its interest rates or core products. The single change was replacing static application pages with an interactive process that actively walked applicants through their decisions.
Action Steps to Turn Banking AI into a Growth Engine
- Audit System Ownership: Determine whether customer-facing AI applications are governed by teams focused on operational savings or revenue generation.
- Track True Completion Rates: Move beyond deflection numbers and calculate the precise percentage of started applications that result in funded accounts or active lines of credit.
- Analyze Top Performers: Study call recordings and chat transcripts of your top sales representatives to identify the specific dialogue patterns that drive conversions.
- Integrate Conversation Logs with CRM Outcomes: Merge interaction data with CRM outcome metrics to identify exactly which conversational approaches lead to generating revenue.
Ultimately, financial institutions excel at tracking operational costs per call, but many lack mechanisms to capture and replicate the habits of their most successful sales professionals. By connecting conversational intelligence directly to revenue metrics, banks and credit unions can turn their AI investments into powerful drivers of growth.
About the Insights: Insights shared by Dvir Ginzburg, founder and CEO of Encore AI, an enterprise platform focused on driving revenue-generating interactions for banks, lenders, and financial institutions.
Source: thefinancialbrand.com