Infrastructure for Sentiment Analysis & Relationship Health Monitoring

Sentiment analysis requires explicitly documented definitions of what constitutes a negative sentiment signal, escalation thresholds (at what score does a relationship manager receive an alert?), and intervention protocols. These business rules must be current and findable — not tacit in individual advisor judgment — so the NLP system applies consistent scoring logic across all communication channels. Regulatory requirements for client communication records also demand documented retention and analysis policies.

Sentiment analysis requires systematic capture of all client communications through defined workflows: email threads routed to NLP pipeline, call recordings transcribed via standard process, chat logs exported with full metadata. Templates and process requirements ensure completeness — missing channels produce blind spots in relationship health scoring. Capture must be process-driven, not ad-hoc, so the system sees a representative view of each client relationship across all touchpoints.

NLP-based sentiment analysis requires formal ontology mapping communication entities: Client, Interaction (Email/Call/Chat), Sentiment Score, Topic, Relationship Health Trend, Alert. Relationships must be defined: Interaction.generatedBy.Client, Interaction.hasScore.SentimentScore, Client.hasHealthTrend.RelationshipHealth. Without explicit entity relationships, the system cannot aggregate sentiment across interaction types to compute a composite relationship health score or identify deterioration patterns across months of mixed-channel communication.

The sentiment system must access email archives, call recording platforms, chat logs, CRM (for client context), and write alerts back to relationship manager interfaces. This requires API access to most of these systems — email systems, telephony/transcription platforms, and CRM must be queryable programmatically. The baseline confirms API access to most systems is achievable in this environment, though legacy core banking access is restricted. For sentiment analysis, the critical data sources (communications, CRM) are accessible via API.

Relationship health models must update when escalation thresholds change, when new communication channels are added, or when sentiment patterns shift (e.g., industry-wide client frustration during market stress). Event-triggered maintenance ensures the system reflects current business rules. When a compliance requirement mandates new escalation criteria, the sentiment scoring logic updates accordingly. This prevents the AI from alerting on outdated thresholds that no longer reflect business intent.

Sentiment analysis must integrate email systems, telephony/transcription platforms, chat platforms, CRM (for client context and alert delivery), and potentially NPS/survey tools. These systems must share context via API-based connections — the AI needs to correlate sentiment signals across channels for the same client, and write health scores and alerts back to the CRM where relationship managers work. Point-to-point API connections between these systems enable the multi-channel sentiment workflow.