Eliminating Data Inconsistencies Across Multiple Systems
The Challenge
The organization faced inconsistent and fragmented data across multiple systems, which created both operational and strategic challenges:
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Customer Data Discrepancies: Mismatched information across CRM and banking platforms led to errors in communication, service delivery, and customer engagement.
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Financial Reporting Inconsistencies: Internal accounting systems were often out of sync with transactional data from various branches, resulting in unreliable reports.
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Operational Inefficiencies: Teams spent significant time manually reconciling and verifying data, delaying decisions and reducing productivity.
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Compliance Risks: Regulatory reporting required accurate, consistent, and auditable data—difficult to achieve with fragmented systems—exposing the organization to potential penalties and operational scrutiny.
These challenges highlighted the need for a unified, automated, and intelligent data management solution that could synchronize information across platforms, enhance operational efficiency, and ensure regulatory compliance.
Root Causes Identified:
- Lack of a centralized data governance framework.
- Multiple legacy systems without standard integration or synchronization.
- Manual data entry and corrections increasing human errors.
- Inconsistent update cycles across different platforms.
Solution Implemented
To address the client’s challenges, Amantra implemented a comprehensive Agentic AI-powered data consistency and automation framework that streamlined processes, ensured data integrity, and reduced manual intervention.
Key Solution Components:
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Centralized Data Repository: Consolidated all transactional and customer data into a single, governed repository, creating a “single source of truth” for reporting and operational decisions.
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Automated Data Reconciliation: AI-powered bots continuously monitored multiple systems, flagged inconsistencies in real time, and executed corrective actions automatically wherever possible.
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Integration Layer with APIs & Intelligent Agents: Connected legacy systems via modern APIs and UI automation, while intelligent agents leveraged NLP to interpret unstructured data from documents, emails, and portals.
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Continuous Monitoring & Reporting: Real-time dashboards provided visibility into data health and reconciliation status, with automated alerts for anomalies or mismatches.
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Reduced Manual Intervention: Human efforts were reserved only for complex exceptions, improving efficiency and accuracy across the organization.
This solution ensured clean, consistent, and actionable data across all systems, enabling faster decision-making, operational efficiency, and improved business outcomes.
Results & Impact
- Data Accuracy Improved by 95%, ensuring reliable reporting and decision-making.
- Operational Efficiency Boosted with 60% reduction in manual reconciliation efforts.
- Faster Regulatory Compliance, as automated processes maintained auditable records.
- Enhanced Customer Experience, as customer data became consistent across all touchpoints.
Key Takeaways
- Multi-system data inconsistency is a critical operational risk, especially in BFSI.
- A combination of centralized data governance, AI-based reconciliation, and system integration can effectively resolve data inconsistencies.
- Intelligent automation not only reduces errors but also frees human resources for higher-value strategic tasks.