AI-Powered Regulatory Reporting Dashboard That Replaced Days of Manual Data Pulls for a Community Bank
The community bank transformed its regulatory reporting operation from a five-day monthly ordeal into a largely automated process that runs continuously in the background. Reports that once required the full attention of four staff members locked in a conference room with printed spreadsheets now generate automatically from a unified data warehouse, with AI-powered anomaly detection catching discrepancies before they reach regulators. The bank's leadership gained real-time visibility into its regulatory posture year-round, and the compliance team entered every examination cycle with complete, validated, examiner-ready filings prepared days in advance.
A community bank with $180 million in assets and four branch locations had built a solid reputation over fifteen years of serving its local market. But behind the scenes, its regulatory reporting process was a monthly ordeal that consumed the finance and compliance teams for nearly a full business week. Every Call Report, HMDA submission, and CRA data filing required staff to manually extract data from multiple disconnected systems, reconcile discrepancies in spreadsheets, and format the output to meet examiner specifications.
The bank's growth from a single branch to four locations had compounded the problem. Each branch maintained slightly different data entry conventions, and the core banking system lacked native reporting capabilities that aligned with regulatory templates. The CFO and compliance officer spent the final week of every month locked in a conference room with printouts, reconciling numbers across loan origination, deposit, and general ledger systems.
JSG was engaged to design and deploy an AI-powered regulatory reporting dashboard that would automate data consolidation, generate examiner-ready reports, and flag anomalies before they reached regulators.
Client: Community bank operating four branch locations, managing $180M in total assets across commercial lending, consumer deposits, mortgage origination, and small business banking. Fifteen years in operation with consistent growth and a clean examination history -- but increasing strain on the reporting infrastructure.
Employee Size: 60 employees
Industry: Financial Services
Services: - Automated Multi-Source Data Consolidation - Examiner-Ready Regulatory Report Generation - AI-Driven Anomaly Detection & Data Validation - Real-Time Reporting Dashboard Design
The bank's regulatory reporting obligations had grown substantially as it expanded from one branch to four and crossed asset thresholds that triggered additional filing requirements. What had once been a manageable monthly task for a small team had become the bank's most labor-intensive and error-prone operational process.
First, data lived in silos that did not communicate with each other. The bank's core banking system (FIS Horizon) handled deposit and account data, a separate loan origination system (Encompass) managed mortgage and commercial loan records, the general ledger ran on a standalone accounting platform, and branch-level transaction data was aggregated through yet another tool. Producing a single regulatory report required the finance team to manually export data from each system, normalize field names and date formats, and reconcile totals in Excel before any report assembly could begin.
Second, data quality issues were persistent and difficult to catch. Branch staff entered data with minor inconsistencies -- varying address formats, inconsistent loan purpose codes, occasional transposition errors in dollar amounts. These issues were invisible at the individual transaction level but created material discrepancies when data was aggregated for regulatory filings. In the prior year, the bank had submitted two amended Call Reports after discovering post-submission errors, drawing unwanted examiner attention.
Third, the reporting timeline created operational bottlenecks. The five-day monthly reporting cycle consumed the CFO, the compliance officer, and two finance staff members at a time when month-end closing activities already demanded their attention. Client-facing activities, strategic planning, and internal projects were routinely deferred during reporting weeks. The bank estimated that the annual cost of the reporting process -- in direct labor, overtime, and deferred productivity -- exceeded $120,000.
Fourth, the bank had no mechanism for proactive anomaly detection. Data quality issues were discovered only during manual reconciliation, if they were discovered at all. There was no systematic check for unusual patterns -- sudden spikes in specific loan categories, deposit concentration changes, or outlier transactions -- that might warrant investigation before regulators noticed them. The bank's approach to data quality was entirely reactive.
The bank needed:
- Automated data extraction and consolidation from all source systems into a unified reporting layer
- Built-in data validation that caught inconsistencies and anomalies before reports were assembled
- Pre-formatted regulatory report templates (Call Report, HMDA, CRA) that populated automatically
- A dashboard that gave leadership real-time visibility into the bank's reporting posture
- The ability to produce examiner-ready reports on demand, not just on a monthly cycle
JSG designed and deployed an AI-powered regulatory reporting dashboard for the community bank that automated the entire data-to-report pipeline and introduced proactive anomaly detection across all reporting data.
Key Components
Unified Data Consolidation Layer N8N orchestration workflows connected the bank's four primary data sources -- FIS Horizon core banking, Encompass loan origination, the general ledger platform, and branch transaction aggregation -- into a single Azure SQL data warehouse. Automated extraction jobs ran nightly, normalizing field formats, standardizing codes, and reconciling cross-system totals without manual intervention.
AI-Powered Anomaly Detection Azure OpenAI analyzed incoming data against historical patterns and regulatory thresholds to flag potential issues before they entered reports. The system identified unusual concentration shifts, outlier transaction amounts, data entry inconsistencies, and trend deviations that warranted human review. Each flag included a plain-language explanation of why the anomaly was detected and a recommended resolution.
Examiner-Ready Report Templates Pre-configured report templates for Call Reports (FFIEC 031/041), HMDA LAR submissions, and CRA data filings populated automatically from the consolidated data layer. Reports were formatted to match exact regulatory specifications, with supporting schedules and reconciliation worksheets generated alongside the primary filings.
Interactive Reporting Dashboard Power BI dashboards embedded in the bank's internal portal provided real-time visibility into key regulatory metrics. Leadership could view asset composition, loan concentration ratios, deposit trends, and capital adequacy indicators at any time -- not just during reporting periods. Drill-down capabilities allowed staff to trace any reported number back to its source transactions.
Validation & Reconciliation Engine An automated validation layer ran cross-system reconciliation checks before any report was generated. The engine compared totals across source systems, verified that required fields were populated, and confirmed that coded values matched regulatory lookup tables. Discrepancies were surfaced in a review queue with suggested corrections.
On-Demand Report Generation The system supported ad-hoc report generation for examiner requests, board reporting, and internal analysis. Rather than waiting for month-end cycles, any authorized user could generate a current-state regulatory report in minutes, giving the bank unprecedented responsiveness during examinations.
## Quantifiable Impact
- Monthly regulatory reporting cycle reduced from 5 business days to 4 hours
- Data accuracy improved from 96.2% to 99.94%, eliminating the need for amended filings
- Zero amended Call Reports filed since deployment (compared to 2 in the prior year)
- Examiner-ready reports available on demand, reducing examination response time from days to minutes
- Annual reporting labor costs reduced by $94,000 (direct staff time reallocation)
- Frontend: Power BI (embedded regulatory dashboards, board reporting portal)
- Backend: Node.js with Express (API layer, report generation engine)
- Cloud: Microsoft Azure
- Database: Azure SQL (unified data warehouse)
- AI & Automation: Azure OpenAI (anomaly detection, pattern recognition, plain-language flag explanations)
- Workflow Orchestration: N8N (nightly data extraction from 4 source systems, cross-system reconciliation, validation workflows, alert routing)
- Communication: SendGrid (anomaly alert notifications, report completion confirmations)
- Integrations: FIS Horizon core banking, Encompass loan origination system, general ledger platform, branch transaction aggregation system, FFIEC report submission APIs
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