AI Agent for Relationship Prioritization & Action Planning
This AI agent evaluates customer relationships, time-sensitive events, and revenue signals to determine what branch officers and relationship managers should focus on today and this week.
Book DemoSummary
This is an AI agent that continuously assesses customer relationships, deadlines, and behavioral signals to determine who requires attention now, why it matters, and what action should be taken next.
The agent converts routine banking events—such as CD maturities, loan renewals, documentation deadlines, and excess liquidity—into prioritized, ready-to-execute action plans that help RMs act decisively and proactively.
The Problem We Are Solving
Banks already capture large volumes of operational and customer data across core banking, lending systems, and CRM platforms. However, this information is fragmented and difficult to translate into daily execution.
As a result:
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Relationship managers lack a clear, ranked view of which customers require immediate attention
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Time-sensitive opportunities such as CD maturities and renewals are handled reactively or too late
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RM effort is spread across low-impact tasks instead of high-value relationship moments
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Urgency, revenue impact, and relationship context are not evaluated holistically
This leads to missed revenue, inconsistent customer experience, and inefficient use of RM time.
What Is an AI Agent for Immediate Actions?
In this use case, the AI Agent operates as an agentic prioritization layer, not a static alerting or task-management system.
Rather than surfacing disconnected alerts, the AI agent runs a continuous closed loop:
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It perceives time-sensitive and relationship signals
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Reasons over urgency, revenue potential, and likelihood of success
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Acts by prescribing specific RM actions
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Learns from outcomes to improve future prioritization
This behaviour is reflected directly in the demo scenarios.
How the Solution Works (Using the Agentic Framework)
Perception: Monitoring What Requires Attention
The agent continuously listens to real-time and scheduled signals across the banking ecosystem, including:
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CD maturity and renewal timelines
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Loan renewals, approvals, and documentation due dates
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Excess liquidity and cash-optimization flags
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Relationship milestones and group activity
These signals are unified into a live relationship context for every customer.
Reasoning: Determining Priority and Urgency
ActionSense™ evaluates each customer and event across multiple dimensions:
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Time urgency — days until action is required
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Revenue impact — immediate and near-term opportunity size
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Conversion probability — relationship strength and readiness
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Strategic value — customer tier, group structure, and lifetime value
The agent generates a clear priority score along with an explanation of why a customer or action should be addressed now.
Action: Prescriptive Guidance for RMs
For each prioritized relationship, the agent provides:
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Who to contact and when
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The most appropriate reason to initiate outreach
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Suggested talking points and conversation flow
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Expected outcomes and revenue impact
These recommendations are delivered directly into RM and branch workflows, enabling immediate execution.
Learning: Improving Prioritization Over Time
Customer responses, RM actions, and outcomes are captured as part of a closed loop.
Over time, the agent refines prioritization logic, timing, and guidance based on what consistently leads to successful outcomes.
Example Data Sources Used in the AI Agent
This agent draws from existing banking systems, including:
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Deposit and account data: CD balances, checking balances, excess liquidity indicators
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Lending data: Loan renewals, approvals, utilization, and availability
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CRM context: Relationship owner, profitability tier, lifetime value
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Task and deadline tracking: Documentation due dates and action requirements
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Group and entity data: Parent–subsidiary relationships and entity counts
Data access follows strict minimization and governance policies.
How the Solution Is Applied in Practice
Typical scenarios surfaced by the agent include:
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Customers with CDs maturing in the next 7–30 days
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Relationships with multiple time-sensitive items converging in the same week
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High-value customers with excess liquidity and near-term action requirements
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Accounts requiring proactive outreach to preserve or expand the relationship
The agent prioritizes these scenarios and guides RM action sequencing accordingly.
Continuous Improvement
The system captures the full event → recommendation → action → outcome loop.
Models are recalibrated based on RM follow-through, customer responsiveness, and realized outcomes, ensuring prioritization improves over time.
Real-World Impact
As demonstrated in the scenarios, the AI Agent:
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Ensures time-sensitive opportunities are addressed before value is lost
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Focuses RM effort on the highest-impact relationships each day
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Converts routine deadlines into revenue and relationship wins
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Enables proactive engagement during critical customer moments
