Regulatory Data Quality & Template Automation Digital Worker

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Coordinates all 8 agents through 9 workflow phases, manages agent dependencies and execution order, generates AI recommendations with confidence scores for detected issues, creates executive compliance reports with audit trails, and tracks overall mission metrics including quality scores, processing times, and cost estimates.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Processes uploaded data files including NAV data, fund holdings, ESG scores, cost information, and performance metrics. Detects file structure (rows, columns), validates data types, normalizes formats, and prepares structured data for downstream validation. Reports file status and data completeness metrics.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Executes 487+ validation rules across multiple rule sets including data completeness, format validation, range checks, cross-field consistency, and regulatory schema compliance. Reports validation issues by severity (info, warning, critical), identifies affected funds and fields, and generates quality scores for each fund and overall dataset.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Cross-references fund data across 5+ external data sources to identify discrepancies. Compares NAV values, holdings weightings, and key metrics against authoritative sources. Calculates variance percentages, flags values outside thresholds, and provides source reliability scores to support data reconciliation decisions.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Applies Isolation Forest machine learning algorithm to detect statistical anomalies in time-series data. Loads 12-month historical baselines, identifies outliers exceeding standard deviation thresholds, cross-references anomalies with portfolio changes to distinguish genuine changes from data errors, and provides confidence-scored explanations.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Loads regulatory template schemas (EMT v4.1, EET v3.2), maps 156+ data points to template fields, generates templates for all funds and share classes, validates outputs against schema requirements, and produces files in required formats (XML, CSV, JSON) with comprehensive metadata including record counts and checksums.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Validates all generated templates against regulatory schemas, runs completeness checks (95% threshold), verifies data accuracy (99% threshold), and assigns quality scores to each output. Provides go/no-go recommendations for distribution based on quality thresholds.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Establishes connections to 6+ distribution platforms, uploads templates via appropriate protocols (API for Morningstar Direct, SFTP for Bloomberg Terminal), tracks delivery status, handles retries for failed deliveries, and records confirmation IDs for audit compliance.