AI Agents

Context

Built for banking as part of the Stand-alone quick win stack, this agent is used in retail portfolios to help customers qualify for better credit terms and product eligibility. Typical scenarios include pre-application coaching, post-decline remediation, and relationship-deepening programs where specific, compliant steps can raise a customer’s score within a defined window.

What it does

The agent reviews a customer’s credit profile and recent behavior, then produces an actionable, compliant plan to improve the credit score. It identifies score-dragging items (e.g., utilization spikes, thin or stale trade lines, recent missed payments), prioritizes actions by impact and timing (balance reductions, limit reallocations, small builder lines, payment regularization), and generates clear next steps with reminders and evidence of completion. Recommendations are framed so customers and bankers know exactly what to do, when to do it, and how progress will be measured.

Core AI functions

Profile analysis aligns bureau-style attributes and internal behavior; rules and impact models estimate score movement from specific actions; timeline planning sequences steps to avoid counter-effects (e.g., keeping utilization low after a pay-down); and explanation layers translate the plan into plain language with rationale. The agent records actions taken and outcomes so future recommendations adapt to what worked.

Problem solved

Generic advice and manual coaching don’t scale, and customers often receive vague guidance that doesn’t translate into score movement. This agent focuses on concrete, high-impact actions and tracks completion, so effort turns into measurable improvement.

Business impact

More customers become eligible for credit, approvals rise with better risk profiles, and relationship value grows as previously marginal applicants convert. Banks see faster, consistent decisions and a clearer link from remediation to approvals and usage.

Integration and adjacent use cases

Integration is light: read credit-profile data and recent behavior from your warehouse or decisioning layer; publish the action plan, status, and reminders to CRM or customer channels (app, email, branch tasks).

Common combinations in this stack include:

• Alternative risk assessor (to complement bureau gaps with additional signals),

• Customer profiling & product recommender (to match improved profiles to suitable offers),

• Customer 360 summary generator (to brief RMs on progress and next steps), and

• Inbound email agent (RM copilot) (to triage customer replies and keep the plan moving).