Scalable Data Quality Frameworks for Record Keeper Aggregation in Financial Platforms - Proposes a framework to standardize and enhance the quality of financial datasets across heterogeneous record keepers

Аннотация

With the rapid development of financial services, recording and data aggregation need to be efficient so information from varying record keepers (banks, custodians, pension administrators) can be aggregated. The downside of various data formats in disparate datasets coming together into one unified place for the sake of being in one place is glaring in the data format, standards of how it will be reported, and lack of metadata. Inaccuracies, timeliness, and unreliability cause financial data to threaten business operations and compliance requirements. Based on financial datasets, it frames an aggregation framework for producing a scalable, standardized, and improved data quality aggregated dataset. Such data quality can be addressed by modular architecture, real-time validation, and centralized monitoring provided by the architecture.Using grace with the metadata-driven rule handling and automation via ETL pipelines to guarantee integrity and compliance with data, the framework also leverages the framework. A case study of a multi-manager pension platform using the proposed framework is further demonstrated, leading to improved data consistency, reporting timeliness, and reduction of reconciliation errors. The paper ends by discussing ethical issues, explaining how to practice the framework, and looking at two future trends employing AI for predictive error models, blockchain for data lineage and audibility, and how regulators can use RegTech to automate the reporting process with compliance. Considering all this, the above-proposed framework provides the perfect overall solution for financial institutions, fintech platforms, and asset managers to make the operation more efficient and build trust between financial data in the industry. KEYWORDS Data Aggregation, Record Keepers, Data Quality Framework, Compliance, AI and ML, Blockchain.