International Journal on Science and Technology

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Facilitating ILST and ILMS with a Robust Data Lake Architecture

Author(s) Bhanu Chander Vallabaneni
Country United States
Abstract Implementing robust data lake architectures for Internal Liquidity Stress Testing (ILST) and Internal Liquidity Monitoring System (ILMS) represents a transformative approach to liquidity risk management in financial institutions. This comprehensive solution integrates S3-compatible storage, Apache Spark, Hive catalog, Apache Iceberg, and Dremio to address persistent challenges in data volume handling, integration complexity, and analytical limitations. The architecture enables financial organizations to centralize disparate data sources, enhance analytical capabilities, facilitate real-time monitoring, support historical analysis, and strengthen regulatory compliance. Through systematic implementation following established methodologies—including thorough assessment and planning, robust data modeling and governance, phased deployment, seamless system integration, and comprehensive automation—institutions can maximize the benefits while effectively mitigating challenges related to data security, technical complexity, change management, data quality, and performance optimization. The evidence demonstrates that financial institutions implementing such architectures experience significant improvements in operational efficiency, analytical depth, risk management effectiveness, and regulatory reporting accuracy, ultimately strengthening their overall liquidity risk management capabilities in increasingly complex and regulated financial environments.
Keywords Liquidity risk management, data lake architecture, financial data integration, regulatory compliance, real-time monitoring
Field Computer
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-04
Cite This Facilitating ILST and ILMS with a Robust Data Lake Architecture - Bhanu Chander Vallabaneni - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3010
DOI https://doi.org/10.71097/IJSAT.v16.i2.3010
Short DOI https://doi.org/g9dpgm
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