To be successful in an evolving regulatory landscape, financial institutions need to be aware of the exact link between risk and profitability. Banks should develop a holistic view of risks existing within their balance sheet to help them better deal with regulatory pressures and scarcity of capital.

Regulations and innovations are driving the need for real-time LRM in the trading and banking book coupled with the need for analytics and data management systems that help banks to effectively manage their liquidity risk globally. Firms should have the required technology and infrastructure to quantify their liquidity costs accurately and also include these costs in product pricing and performance measurement under various scenarios.

Based on Intellect’s experience in this area, it identifies that the challenges faced by Treasurers, CFOs, CROs and Funding Managers in the area of Liquidity Risk Management can be broadly categorized into four categories:

  • Liquidity and Funding Optimization: Need to develop strategies to make the best use of liquid assets and minimizing dependence on costly contingent funding.
  • Capital Adequacy: Finding the right balance of long and short-term funding sources; Managing LCR and NSFR; Analyzing the impact of Asset and liability behavioural changes
  • Profit Improvement: Forecasting and protecting revenue margins through stress testing; Optimizing funding through balance sheet structural analysis; Improving bottom line through IRR and NIM sensitivity analysis
  • Regulatory compliance: Leveraging BCBS reporting tools effectively to manage exposure and optimize liquidity; Monitor and automate liquidity reporting across systems and currencies

Modern-day banks require platforms with analytical and data management capabilities, which can effectively manage liquidity risk across both trading and banking books.

Proper 'Data Management' is critical to ensure that the above mentioned technological changes/innovations are incorporated without any hassles. Aggregation of data from multiple systems/regions/levels is essential to support cash flow forecasting, liquidity management, regulatory reporting and stress testing frameworks. The aggregated data needs to be represented intuitively, through visual mechanisms, such as dashboards, to facilitate decision making by C-level management. According to an estimate by an AITE-Sybase survey, only 5% of liquidity risk management data is gathered through automated processes and systems.

There is a huge potential for liquidity risk management systems that can centralize and aggregate huge volumes of liquidity data across dimensions, such as business verticals, product lines, currencies etc., and present the same in intuitive formats to enable strategic and timely decisions.