Predictive AI Customer 360

Modernize Client Intelligence and Relationship Management: Relationship managers juggle a dozen disconnected systems—payments, credit, treasury, receivables, CRM—just to answer which client needs attention today. Existing dashboards report what already happened; they don't forecast a liquidity gap, flag a payment-failure pattern, or surface a closing cross-sell window. This professional services offering delivers a multi-agent solution that unifies client data into a single forward-looking decisioning layer, moving relationship management from hindsight to foresight—while keeping every recommendation explainable and reviewable. The result is earlier detection of client risk, better-timed commercial conversations, and recommendations that risk and compliance can stand behind.

Multi-Agent Decisioning Framework: A coordinated set of specialist agents works across the client relationship: a customer profile and portfolio agent assembles the unified view including entity hierarchy and portfolio health; cash flow, payments, receivables, and treasury agents forecast liquidity, monitor payment performance, and surface working-capital opportunities; and credit, risk, business insights, and cross-sell agents assess eligibility, score behavior, and rank product opportunities. A supervisor agent ranks the outputs, resolves conflicts between them, and synthesizes a single recommendation feed. Every recommendation carries a confidence score and a named source agent, and is logged to a visible audit trail with timestamps—so risk, compliance, and the relationship manager can trace the reasoning behind any AI-driven suggestion. Human review, override, and escalation are supported throughout.

Business Capabilities: • Build a unified 360° client view across accounts, entity hierarchy, and portfolio health • Forecast cash position and liquidity gaps ahead of time, with what-if scenario simulation • Monitor payments in real time, including failure rates, latency, and rail performance • Surface working-capital opportunities such as early-payment discounts and idle-cash sweeps • Assess credit eligibility and recommend financing when a need is detected • Score client risk and behavior continuously, flagging anomalies and unusual patterns • Rank cross-sell opportunities by revenue potential and product eligibility • Deliver confidence-scored, explainable recommendations with a complete audit trail

AWS-Native Architecture: Built on AWS, the solution runs on a governed agentic platform. Amazon Bedrock provides foundation models, agents, and guardrails, with Amazon Bedrock AgentCore and AWS Step Functions orchestrating specialist agents and AWS Lambda handling tool invocations. Amazon SageMaker AI hosts custom models for cash-flow forecasting and risk scoring. Amazon OpenSearch Service powers vector and hybrid retrieval across client and product data, with Amazon Simple Storage Service (Amazon S3) for documents, Amazon Aurora PostgreSQL for structured stores, Amazon DynamoDB for agent state, and Amazon Neptune for the client knowledge graph. Core banking, payments, credit, and CRM data are ingested through Amazon API Gateway, Amazon EventBridge, and AWS Glue. AWS Identity and Access Management (IAM) and AWS Key Management Service (AWS KMS) enforce least-privilege access and encryption, while Amazon CloudWatch, AWS CloudTrail, AWS Config, AWS Security Hub, and AWS Control Tower deliver observability, audit logging, and multi-account governance. The architecture is modular, event-driven, and designed for enterprise-grade resilience.