From Raw Transactions to Targeted Offers: Building a Customer Intelligence Engine for Banking
Transforming unstructured transaction logs into a layered intelligence engine that matches customer behavior to high-conversion banking offers.
Request a Free ConsultationMulti-Agent Conversational Analytics Platform
Most companies have valuable data locked behind a technical barrier. Getting an answer means writing a query, building a chart, and manually interpreting the result — a slow loop that depends entirely on technical staff. We built an AI agent that removes that barrier entirely: plug in a MongoDB or SQL database, ask a question in plain English, and the system fetches the data, builds the visualization, and surfaces relevant insights — all autonomously.
Multi-agent orchestration
Specialized agents handle distinct responsibilities (intent parsing, query generation, data retrieval, visualization, insight retrieval), coordinated through a graph-based control flow for reliable, multi-step reasoning.
Database-agnostic & plug-and-play
Connects directly to a client's MongoDB or SQL databases with no schema rework required. Point it at a database and it's ready to answer questions.
RAG-powered insight retrieval
A vector database stores generated insights, retrieved at query time through a Retrieval-Augmented Generation pipeline so answers come with relevant context, not just raw numbers.
Continuously updated insight layer
As new client data flows into the database, insights in the vector DB are refreshed automatically — the system's knowledge stays current without manual intervention.
Natural language querying
Users ask questions in plain English; the system interprets intent and generates the correct query under the hood, then returns answers with appropriate visualizations.
Automated visualization
The system doesn't just return rows; it chooses and renders appropriate charts and visualizations for the question asked.
Built Production-Ready From Day One
Additional capabilities we engineered into the platform so it can be trusted with real-world enterprise data and real users.
Challenges the Client Faced
The client, managing a vast portfolio of insurance policies, relied heavily on a multi-channel outreach strategy (WhatsApp, Email, SMS) to drive premium collections and policyholder engagement. However, they faced a complex data architecture challenge that led to operational blind spots.
Fragmented Multi-Channel Visibility
Data for each outreach channel lived in silos. The client could send thousands of communications but could not view the collective engagement history of a single policyholder across all platforms.
The "Never Contacted" Risk
They couldn't answer a fundamental question: which policies were slipping entirely through the cracks? Thousands of high-priority policies received zero outreach due to database gaps, leading directly to lapsed premiums.
Operational Optimization Guesswork
Marketing teams lacked objective data to determine the optimal timing or frequency of messages. They were operating on intuition rather than data-backed evidence.
Technical Root Cause — The Row Explosion
The data relationship caused a classic "row explosion." A simple database join produced inflated and inaccurate engagement counts, making every downstream metric unreliable.
Process Workflow & Strategic Solution
To resolve the structural data chaos, we did not simply overlay a visualization tool. We engineered a robust data transformation pipeline that resolved the many-to-many relationship using custom bridge logic in SQL and advanced modeling in Python, before presenting refined data in Power BI.
Data Pipeline Ingestion
Raw multi-channel communication logs and policy data are ingested and routed through a custom transformation pipeline.
Custom Bridge Logic (SQL)
We engineered robust bridge logic in SQL to resolve the many-to-many relationship and eliminate the row-explosion issue at its source.
Advanced Modeling (Python)
Behavioral and conversion models are built in Python to enrich the structured data with derived signals and engagement scoring.
Unified Data Layer
Refined, deduplicated data is loaded into a unified layer — clean, accurate, and ready for analytics consumption.
Power BI Dashboard
Strategic intelligence is exposed through a real-time Power BI dashboard with granular, channel-level, and policy-level views.
Key Analytics Delivered
Once the data was structured correctly, the Move37 AI dashboard exposed granular insights that were previously impossible to calculate. We delivered a unified analytics layer connecting communication activity directly to policy outcomes.
Delivery Intelligence & Failure Categorization
Instead of simple "sent" metrics, we provide Delivery Success vs. Failure tracking. We categorize why communications failed (invalid numbers, blocked contacts, bounce reasons) per channel, allowing the client to fix specific data quality issues.
Behavioral Conversion Analysis
The client can finally view Channel-wise Conversion Rates, objectively measuring which platform actually drove premium payments most effectively. Supported by Timing-vs-Maturity Analysis that identifies the optimal window (days before a due date) for outreach to maximize payment behavior.
Unified Engagement History
A single policyholder view across WhatsApp, Email, and SMS — closing the multi-channel visibility gap and surfacing the policies that were slipping through the cracks.
Business Impact & Results
The deployment of the Insurance Communication Analytics Dashboard transformed the client’s outreach strategy from disjointed, high-volume broadcasting to precise, data-driven engagement.
Eliminated the outreach gap. The "Never Contacted" detection flag provided real-time alerts for missed policies, ensuring every high-priority policy received communication — directly increasing premium recovery.
By identifying WhatsApp as a high-converting, low-lag channel, the client intelligently shifted communication budgets away from lower-performing SMS segments.
Correlation of timing-vs-maturity and time-to-conversion lag enabled evidence-based outreach schedules — sending specific reminders during the discovered "Golden Windows" that maximize response and payment rates.
Resolved the many-to-many data relationship at the source with custom SQL bridge logic, eliminating inflated counts and making every downstream metric trustworthy.
Tools & Tech Stack
Leveraging a future-ready tech ecosystem — from agent orchestration and retrieval pipelines to data transformation and visualization.
Programming & Modeling
- Python (Data processing and behavioral modeling)
Data Transformation
- SQL (Custom bridge logic and aggregation)
Visualization
- Power BI (Unified analytics and real-time dashboarding)
Agent Framework & Orchestration
- LangChain
- LangGraph
Retrieval Pipeline
- RAG architecture
- Vector database for insight storage and retrieval
Databases Supported
- MongoDB
- SQL databases
Ready to Turn Your Communication Chaos into Actionable Insights?
Stop operating on intuition. Leverage the expertise of Move37 AI to create intelligent analytical solutions for your firm.
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