Banking Service Agent: 4-Week Proof of Concept

OVERVIEW:

MAQ Software deploys a working banking service agent in your tenant, handling high-volume customer and agent-assist scenarios on Microsoft Copilot Studio, Azure AI Search, and Azure AI Foundry, with every response cited to approved content and governed escalation to a human.

BUSINESS CHALLENGE:

• Banking contact centers absorb huge volumes of routine, answerable questions: balances, card disputes, statements, product eligibility, onboarding.
• Customers queue for answers that already exist in policy documents, while agents spend their day retrieving information rather than resolving complex cases.
• Knowledge scattered across portals and PDFs means the answer varies by who takes the call, and in a regulated environment an inconsistent answer is exposure, not just poor service.

KEY QUESTIONS:

• What share of contact volume is routine and answerable from documentation you own?
• What are your current containment, first-contact resolution, and average handle time figures?
• When the agent should not answer, does it escalate with full context?

AGENDA:

Week 1: Analyze contact volume to identify the highest-frequency intents. Baseline containment, first-contact resolution, and handle time. Define escalation rules and answer boundaries, then provision the services.

Week 2: Ingest approved product, policy, and procedural content into Azure AI Search. Structure and index for retrieval accuracy, surfacing documentation gaps and contradictions as a finding in itself.

Week 3: Build conversational flows in Microsoft Copilot Studio across self-service and agent-assist. Connect read-only system lookups where in scope, with governed escalation and Microsoft Entra ID scoping.

Week 4: Test against real historical queries and adversarial cases. Measure resolution rate, accuracy, and escalation quality against baseline. Review tone and compliance language with quality and risk.

SOLUTION / APPROACH:

Use Case 1: Customer self-service. The agent answers routine account, product, and process questions directly, citing the approved source behind each response.

Use Case 2: Agent assist. Live agents receive retrieved knowledge and suggested wording mid-conversation, cutting handle time on complex cases.

Data Foundation: Approved product, policy, and procedural content indexed in Azure AI Search, structured for retrieval accuracy with citation on every answer.

Governance and Responsible AI: Explicit out-of-scope boundaries, escalation carrying full context to a human, Microsoft Entra ID scoping, and conversation traceability for compliance review.

DELIVERABLES:

• Service baseline covering containment, first-contact resolution, and handle time
• Validation summary with measured resolution rate, accuracy, and escalation quality
• Working banking service agent deployed in your tenant across priority intents
• Grounded knowledge index in Azure AI Search with citation enabled
• Documented escalation rules, scoping, and answer boundaries

Business Users:

• Contact Center Agents
• Customer Service Team Leads
• Knowledge Managers

BUSINESS OUTCOMES:

1. Routine queries resolved without queue time, at materially lower cost to serve
2. Agents freed for complex cases only a human can close
3. One consistent, policy-accurate answer regardless of who takes the contact

WHY MAQ SOFTWARE:

• Financial services expertise: We have built grounded service and knowledge agents for banking contact centers, working with policy libraries, product documentation, and regulated response language.
• Audit before we build on it: We surface the gaps and contradictions first, because an agent grounded in contradictory policy documents will confidently give two different answers.

CALL TO ACTION:

• Reach out to CustomerSuccess@MAQSoftware.com.
• Learn more at MAQ Software Financial Services.