For a relationship banker preparing to meet with a business client, getting a complete picture of that relationship can take longer than the meeting itself. Account balances, transaction histories, lending activity, and outstanding applications might all be available within the institution, but assembling them into a useful picture often means navigating multiple systems, pulling reports, and consulting colleagues.
The same inefficiencies appear throughout a financial institution. Service representatives investigating account issues must piece together customer information, while marketing teams evaluating deposit campaigns spend hours gathering data before identifying opportunities for follow-up. New employees must learn where information resides and how to navigate various systems before becoming fully effective.
Conversational AI offers an opportunity to fundamentally reconsider these workflows. Instead of navigating multiple interfaces to find an answer, staff can begin with the question itself, using AI to retrieve relevant information, connect data points, and suggest potential next steps. Financial institutions are already privy: a recent survey found that internal staff deployments of AI outpace customer-facing deployments by 5-to-1.
Building on its earlier MCP integrations with OpenAI and Anthropic, Narmi has expanded its Model Context Protocol capabilities with the Microsoft 365 Copilot Connector, extending conversational access to banking data into staff-facing workflows.
Through Narmi's Admin MCP server, authorized employees can ask questions about customer relationships, account activity, lending and liquidity, transaction histories, and application statuses without manually navigating multiple screens or assembling reports.
The applications span relationship management, credit analysis, fraud investigations, and onboarding. A banker might ask about substantial outflows to external institutions, review a business customer's line-of-credit utilization, or investigate an unusual transaction. Copilot can also help interpret the information retrieved, identify patterns, organize findings, and suggest potential actions. This creates a consultative layer between banking data and the decisions employees make every day.
Consider two institutions serving similar markets: one continues to rely on manual reporting, while the other uses AI to handle much of the information gathering and preliminary analysis. The latter can devote more resources to proactive outreach, relationship development, and the customer-facing activities that strengthen primacy.
Developing fluency with AI takes time and banks that begin now will be better positioned to understand where these tools can improve their operations. Connecting banking data to the AI tools employees are beginning to use within the Microsoft 365 ecosystem offers a practical starting point.
The integration uses OAuth 2.0 authentication, with data access determined by assigned permissions. Institutions must explicitly enable the capability, and existing institutional access controls remain in place.
The connection is strictly read-only. AI assistants cannot modify accounts, initiate transactions, or perform administrative actions, while sensitive information such as Social Security numbers and enrollment codes remain inaccessible through the integration.
Consider a customer who contacts their bank because they've been locked out of digital banking. Investigating the issue typically requires a service representative to locate the customer's profile, review relevant account information, and determine what requires attention.
With Narmi's MCP integration, staff can ask Copilot about the customer's situation and retrieve relevant information through a single conversation, reducing the time spent navigating separate screens. Here, it quickly notices that the user showed suspicious attempted login activity as the reason why they were blocked, which the banker can use as a foundation for the rest of the workflow.
But resolving the immediate issue is only the beginning. The same conversational approach can help staff develop a more complete, data-backed picture of a customer's banking relationship.
Copilot can examine transaction patterns, deposit activity, and account relationships to surface potential signs of disengagement, such as substantial or increasing transfers to external institutions. It can also suggest possible avenues for outreach, helping staff determine whether a secure message, personal conversation, or other engagement through an owned channel might be appropriate.
These patterns don't necessarily establish why a customer is moving funds, but they can provide an earlier opportunity to investigate. Rather than limiting relationship analysis to periodic reviews, bankers can incorporate these insights into everyday interactions.
A successful CD promotion can attract significant deposits, but understanding the customers behind those balances is essential to developing longer-term relationships.
A customer who opens a promotional CD may maintain their primary checking account and other financial relationships elsewhere. Identifying those customers, assessing their broader banking relationships, and prioritizing follow-up typically requires considerable reporting and manual analysis.
With Copilot, staff can explore available account data to identify promotional CD customers with substantial balances, assess their existing product relationships, and determine which customers might warrant additional attention - turning potential rate shoppers into long-term relationships.
Copilot can help segment potentially high-value relationships and suggest relevant products or engagement opportunities. A customer with a large CD balance but limited use of other products, for example, might be a candidate for a conversation about broader banking needs.
The banker still determines which recommendations are appropriate. But instead of spending hours compiling and sorting information, staff can devote more time to evaluating opportunities and engaging directly with customers. For institutions focused on primacy, this offers a way to turn short-term deposit acquisition into opportunities for more durable relationships.
The value of conversational AI also depends on how employees learn to use it. Staff can refine their questions, request additional context, and ask Copilot to explain the reasoning behind its suggestions.
A prompt as simple as "include your reasoning and the account activity supporting your assessment" can encourage more transparent responses, giving bankers information they can independently evaluate rather than recommendations they must accept at face value.
As employees become more comfortable exploring information this way, institutions can begin reconsidering workflows built around manual reporting and data collection. The larger opportunity is to put more of the information banks already possess to work, giving employees more time to concentrate on the relationships that distinguish community banking.
