DQS-AI: Open-Source Agentic AI for Data Quality Governance in Payments
Problem Statement
Modern payment systems process millions of transactions and customer records across distributed platforms. Despite the critical importance of high-quality data for compliance, fraud prevention, analytics, and reporting, there is no standardized and explainable framework to measure enterprise data quality.
Most existing tools generate static reports, requiring manual review and intervention. This reactive approach increases regulatory risk, operational inefficiencies, and audit complexity.
Proposed Solution
DQS-AI is an open-source, Agentic AI system designed to autonomously evaluate, score, and improve data quality in payments ecosystems.
The system:
Ingests structured datasets
Evaluates them across seven standardized data quality dimensions
Generates a composite Data Quality Score (0–100)
Produces explainable AI-driven insights
Recommends prioritized remediation actions
Unlike traditional profiling tools, DQS-AI acts as an autonomous governance agent, transforming raw data checks into actionable intelligence.
Core Capabilities
Multi-Dimensional Scoring
Each dataset is evaluated across:
Completeness
Accuracy
Consistency
Validity
Uniqueness
Timeliness
Integrity
Each dimension is scored using deterministic, rule-based validation logic to ensure transparency and reproducibility.
Composite Data Quality Score (DQS)
All dimension scores are aggregated into a single, interpretable enterprise-grade metric.
GenAI Insight Engine
The AI module:
Translates technical results into plain-language explanations
Identifies regulatory and operational risks
Suggests prioritized remediation actions
Agentic Workflow
Detects anomalies autonomously
Highlights high-risk dimensions
Provides decision-support recommendations
System Architecture
User Interface (Dataset Upload & Dashboard)
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Data Quality Orchestrator Agent
Metadata Extraction
Rule-Based Scoring Engine
Composite Score Calculator
GenAI Insight Module
Privacy & Open-Source Philosophy
No raw sensitive transaction data is stored
Only metadata, computed scores, and insights are retained
Designed for transparency, auditability, and extensibility
Built to be modular and community-driven under FOSS principles
Impact
DQS-AI enables:
Standardized enterprise data quality benchmarking
Reduced manual audits
Improved compliance readiness
Trustworthy AI-driven analytics in fintech ecosystems
By combining deterministic validation with explainable GenAI insights, DQS-AI bridges the gap between technical data profiling and autonomous governance.