Enhancing Liquidity Risk Management.
:: 410 Words

At DataBooth, I contributed to a large-scale project team tasked with revolutionising a leading Australian bank's global liquidity risk management capabilities. This project showcases how advanced analytics and efficient data processing can transform critical financial operations.

My Role

As a freelance data analytics specialist within the project team, I focused on:

  • Developing the core cash flow engine, a critical component for accurate liquidity assessment
  • Creating user-friendly interfaces for business users to validate the engine's outputs
  • Mentoring less experienced team members, sharing expertise in Python, data processing, and financial analytics

The Challenge

The bank needed to:

  • Upgrade their liquidity risk management system to meet APRA's Prudential Standard APS 210 Liquidity requirements
  • Implement accurate and timely cash flow calculations allowing for data sourced across multiple time zones
  • Enhance reporting capabilities to ensure regulatory compliance

Our Approach

We developed a robust solution leveraging cutting-edge technologies:

  1. Advanced Data Processing: Utilised Python with Pandas, DuckDB, and Polars for high-performance data manipulation.
  2. Scalable Analytics: Implemented PySpark for handling large-scale data processing.
  3. Workflow Automation: Employed Airflow for orchestrating complex data pipelines and calculations.

The cash flow engine we built calculates granular cash flows at the instrument level. This granularity allows for analysis of each payment, providing deeper insights into the bank's liquidity position.

The Impact

Our solution delivered significant benefits:

  • Enhanced Compliance: Ensured adherence to APS 210 Liquidity standards across global operations.
  • Improved Efficiency: Reduced calculation times, enabling more frequent and accurate liquidity assessments.
  • Global Synchronisation: Harmonised liquidity management across multiple time zones.
  • Data-Driven Insights: Provided deeper visibility into the bank's liquidity position, supporting strategic decision-making[2].

This project demonstrates how DataBooth can help modernise critical risk management processes, ensuring regulatory compliance while improving operational efficiency.