Transforming Fund Administration with AI: How a Global Asset Manager Achieved 90% Processing Time Reduction
- Python
- Terraform
- Azure Microsoft
- GitHub Actions
- Power BI
SERVICES
EXPERTISES
Project Context
Solution
Outcome
About Client
Our client is one of the leading asset management firms, managing over $44 trillion in assets under custody and administration with $2.4 trillion in assets under management.
With 50,000 employees across 35+ countries and $16.6 billion in annual revenue, they provide comprehensive investment management services to corporations, financial institutions, governments, and individuals worldwide.
Business challenge
The reality: 15,000 financial reports, 400 questions each, all manual
The client's Fund Administration department faced a critical operational bottleneck that was threatening their ability to scale and maintain a competitive advantage:
- Massive manual workload: Processing 15,000 annual, semi-annual, and quarterly financial reports manually against Excel checklists containing 300-400 validation questions per report
- Compliance risk: Each report required meticulous validation against regulatory standards, with human error potentially leading to costly compliance violations
- Resource drain: Highly skilled analysts spending 90% of their time on repetitive validation tasks instead of strategic financial analysis
- Scalability crisis: Growing client demands outpacing manual processing capacity, creating delivery delays and operational stress
The trigger came during organizational restructuring when leadership realized that the fund administration team couldn't expand operations effectively without automation. At the same time, the rapid adoption of LLM models across the organization demanded immediate modernization.
The stakes:
Manual processes were not just inefficient but unsustainable in a competitive market demanding faster, more accurate financial reporting.
Approach
Binariks implemented a strategic, phased approach designed to deliver rapid time-to-market while ensuring enterprise-grade reliability:
1. Team Assembly & Requirements Analysis
We assembled a specialized 8-person team including ML/AI engineers, data engineers, cloud architects, and financial reporting SMEs who underwent rigorous technical and domain-specific interviews to ensure deep expertise in both AI/ML and financial services.
2. Problem Framing & Solution Architecture
Our team conducted a comprehensive analysis to understand the client's unique validation requirements, regulatory constraints, and integration needs. We established clear project phases with defined deliverables and success metrics.
3. Risk-Mitigation Strategy
Recognizing the critical nature of financial reporting, we designed a stage-wise implementation approach that would allow for continuous validation and refinement, ensuring each phase delivered measurable business value before proceeding to the next.
4. Agile Delivery Framework
We structured the project around rapid prototyping and iterative development, enabling the client to see tangible results within weeks rather than months, building confidence and momentum for the full solution rollout.
Implementation
Intelligent Document Processing Pipeline
Our solution transformed the client's manual validation process into a fully automated, AI-driven system built on Microsoft Azure:
Core Architecture Components:
- Advanced data extraction: Integrated Azure Text Analytics and Form Recognizer to automatically extract text, key-value pairs, and tables from PDF financial reports with 99%+ accuracy
- Intelligent validation engine: Deployed fine-tuned ChatGPT 3.5 models specifically trained on financial reporting patterns to automatically answer validation questions with contextual understanding
- Orchestrated workflow management: Implemented Azure Logic Apps and Functions to create seamless data processing pipelines from ingestion through final validation
- Enterprise data lake: Established Azure Data Lake v2 as the central repository for processed data, ensuring scalability and enabling advanced analytics capabilities
AI/ML Model Development:
- Custom model training: Developed proprietary ML models trained on historical financial reports and corresponding validation checklists to understand sector-specific patterns and requirements
- Continuous learning architecture: Implemented model retraining capabilities to improve accuracy over time and adapt to changing regulatory requirements
- Quality assurance integration: Built comprehensive testing frameworks, ensuring model outputs meet financial industry accuracy standards
Visualization & User Experience:
- Interactive Power BI dashboards: Created intuitive dashboards allowing fund administration teams to review validation results, track processing status, and identify exceptions requiring human intervention
- Automated reporting: Designed system-generated summary reports highlighting key findings, discrepancies, and compliance status
Infrastructure & DevOps:
- Infrastructure as code: Utilized Terraform for reproducible, version-controlled infrastructure deployment across environments
- CI/CD pipeline: Implemented GitHub Actions for automated testing, deployment, and model updates
- Monitoring & alerting: Deployed Azure Monitor for real-time system health tracking and proactive issue resolution
Value Delivered
The solution delivered a proof of concept in under 6 months and achieved both primary objectives:
- 90% processing time reduction: Report validation that previously required days now completes in hours
- 75% error elimination: Automated validation removed human errors while identifying mistakes in previously processed reports
Cost-Effective Operations:
The solution costs $220,000 annually – equivalent to 1.5 analyst salaries – while processing more than required 15,000 reports per year.
Unexpected Quality Improvements
The AI system detected errors in historical manual validations, revealing process gaps that had previously gone unnoticed. This discovery led to enhanced reconciliation mechanisms improving overall validation quality.
Strategic Organizational Impact
The project enabled the fund administration team's expansion plans, turning a processing constraint into operational flexibility. Additionally, successful AI/ML adoption opened opportunities for broader technology implementation across the organization.
The fund administration department now processes its entire annual volume with consistent accuracy while analysts focus on strategic financial analysis rather than repetitive validation tasks.
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