AI Agents

Context

Built for banking within the Customer churn & retention stack, this agent is used when an at-risk relationship requires a non-standard save—for example, policy-sensitive offers, multi-product adjustments, or approvals that sit outside frontline discretion. It sits after risk is detected and a cause is identified, ensuring complex saves are routed, decided, and recorded correctly.

What it does

The agent takes the customer’s churn risk, driver summary, and proposed action, then determines whether the case fits standard playbooks or needs exception handling. For exceptions, it assembles the supporting context (risk score, reason codes, history, proposed remedy, economic impact), applies decision rules and eligibility limits, and routes the case to the right approver or queue with a clear one-page synopsis. Once a decision is made, it verifies that the authorized action was executed (e.g., fee reversal, credit limit change, pricing adjustment) and that the required documentation and acknowledgments are attached for a clean close.

Core AI functions

Rule evaluation against retention authorities and policy; economic-impact estimation (offer cost vs. expected value retention); eligibility checks across products and customer tiers; auto-draft of an exception synopsis with linked evidence; queue selection and SLA tracking; and post-decision validation that promised actions and disclosures were completed. Confidence and materiality thresholds keep borderline cases in standard flows and escalate only when warranted.

Problem solved

Complex saves often bounce between teams, with unclear ownership and inconsistent documentation. Decisions are delayed, offers exceed authority, or actions are executed without the required evidence—driving rework and uneven customer outcomes.

Business impact

Faster, consistent decisions on non-standard saves; better control of offer economics; improved customer experience when high-value relationships need tailored treatment; and stronger QA/audit posture because every exception carries a defensible rationale and a complete evidence trail.

Integration and adjacent use cases

Integration is light–moderate: read risk flags, reason codes, and case data from CRM/servicing; write exception synopses, routing, decisions, and completion checks back to the same workflow—no core changes required.

Common combinations in this stack:

• Churn signal extractor (source of risk and timing),

• Root cause identifier (complaint & sentiment) (to anchor the proposed remedy), and

• Document-driven feedback & closure validator (to ensure execution and evidencing are complete).