Personalized Financial Wellness with Agentic AI

Modernize Personal Financial Management and Customer Engagement:

Retail banks hold rich transaction data but rarely turn it into guidance a customer can act on. Spend categorization is inconsistent, financial-health signals surface late, and product recommendations arrive untimed and unexplained — so customers disengage and relationship managers fall back on generic outreach. This professional services offering delivers a multi-agent solution that converts transaction and behavioral data into personalized insights, wellness signals, and ranked recommendations, each accompanied by a clear rationale.

The result is a financial wellness experience customers engage with, and recommendations relationship managers can put in front of a client with confidence.

Multi-Agent Personalization Framework:

Six specialized agents work across the customer relationship. A customer intelligence agent builds the profile, segmentation, and behavioral context, identifying personas, preferences, and engagement patterns. A spend intelligence agent analyzes transaction data to surface spending insights and trends, detecting anomalies and recurring spend. A financial wellness agent tracks financial health, credit usage, and risk indicators, flagging early warning signals alongside improvement opportunities. Building on those signals, a next-best-action agent generates personalized product and advisory recommendations and ranks them by propensity, relevance, and business value, while an RM copilot agent turns them into client briefings, talking points, and suggested next steps. An explainability agent accompanies every recommendation with a clear rationale, so the reasoning behind any suggestion is transparent to the relationship manager and defensible to the customer.

Business Capabilities:

• Build customer profiles, segments, and behavioral context from transaction and engagement data • Categorize spend automatically and surface spending insights and trends • Detect anomalies, recurring spend, and behavioral pattern shifts • Track financial health, credit usage, and risk indicators over time • Surface early warning signals and improvement opportunities for the customer • Generate personalized product and advisory recommendations • Rank recommended actions by propensity, relevance, and business value • Equip relationship managers with client briefings, talking points, and suggested next steps • Accompany every recommendation with a clear, transparent rationale

AWS-Native Architecture: The solution runs on a governed agentic platform within a firm's AWS environment. Transaction and engagement data is ingested through Amazon EventBridge, Amazon API Gateway, and AWS Glue, then held in Amazon Simple Storage Service (Amazon S3) and Amazon Aurora PostgreSQL for structured customer and account records. Amazon SageMaker AI trains and hosts the categorization, propensity, and financial-health models that underpin spend classification and recommendation ranking — keeping predictive scoring in purpose-built models rather than inference alone. Amazon Bedrock, with guardrails enforced, generates customer-facing insight narratives, RM briefings, and the rationale behind each recommendation, while Amazon Bedrock AgentCore and AWS Step Functions coordinate the agents and AWS Lambda handles tool invocations. Amazon OpenSearch Service retrieves comparable customer patterns and product eligibility context, and Amazon DynamoDB holds agent state and customer session context. AWS Identity and Access Management (IAM) and AWS Key Management Service (AWS KMS) enforce least-privilege access and encryption of sensitive transaction data, while Amazon CloudWatch, AWS CloudTrail, AWS Config, AWS Security Hub, and AWS Control Tower deliver observability, audit logging, and multi-account governance.