Amit Gupta, Group CFO at Arka Fincap, explains how the wave of data and AI/ML aids liquidity risk management:
Ravi Lalwani: What are your concerns about cash flow liquidity risk and market liquidity risk in the volatile global scenario marred by war, natural calamities, supply chain disruptions, geopolitical rivalry, etc.?
Amit Gupta: Liquidity risk has become a critical concern in today’s volatile global environment. It is evident that liquidity within the ecosystem is tightening, with a marked reduction in the surplus liquidity that was once in circulation. The availability of funding is no longer as accessible as it used to be. Geopolitical tensions, along with unfavorable global circumstances, have created significant uncertainty around liquidity. Additionally, global interest rates have steadily risen over time, with no correction post-covid. The inflationary pressures and external economic challenges have prompted the RBI to hold interest rates steady in India as well.
In the Indian context, liquidity in the debt market has been drying up. To control inflation, the RBI has employed various measures to manage liquidity. Investors’ focus has also shifted – moving away from deposits towards other fixed-income instruments and equity markets. While banks have increased deposit rates to attract more funds, the credit-deposit ratio remains a challenge. This has led to a spike in short-term lending rates, pushing Commercial Paper (CP) and Certificate of Deposit (CD) rates higher.
Globally, the ongoing volatility and uncertainty hinder the ability of developing economies to take aggressive steps toward growth. If liquidity does not improve and interest rates remain high, it could create challenges for the overall credit market.
What is your observation about liquidity risk and its handling by individual NBFCs vis-à-vis regulators in India?
I believe, liquidity is the lifeblood of lending institutions – it is the only raw material that keeps business operations running. Over time, mid-to-large-sized NBFCs have come to realize the importance of liquidity in sustaining their business models. Key events such as demonetization, the collapse of certain financial institutions, the pandemic, and banks moving to the Prompt Corrective Action framework have provided invaluable lessons to treasurers across the industry.
Corporate treasuries have since become more cautious, ensuring surplus liquidity is available at all times. This surplus is strategically deployed into capital-protected assets to avoid any capital losses. NBFCs are regulated by the RBI, which has tightened norms and increased monitoring mechanisms to ensure liquidity sufficiency across the sector. Measures such as the introduction of the Liquidity Coverage Ratio (LCR) and the growing responsibility of the Asset Liability Management Committee (ALCO) have been welcome steps toward strengthening liquidity risk management.
Many kinds of risks can be forecasted with available data. What are your observations about the quality and quantity of data available for liquidity forecasting?
There is a wealth of data available today that can aid in forecasting liquidity and the associated risks. Both macroeconomic and microeconomic data points are essential in this process. Data such as fiscal deficit, current account deficit, inflation trends, credit offtake, credit-deposit ratios, mutual funds’ AUMs (Assets Under Management), and their sectoral exposures are all valuable in forming a liquidity outlook. Additionally, the commentary and decisions from the RBI’s Monetary Policy Committee (MPC) play a significant role in shaping views on liquidity, interest rates, and overall financial strategy.
What aspects of liquidity risk management are algorithmic and what aspects are expertise/wisdom-based?
Liquidity risk management is a blend of both algorithmic processes and human expertise. It is often difficult to draw a clear line between the two. Many decisions rely on expertise and experience, which can be supported and enhanced by data-driven insights. The use of algorithms for liquidity forecasting allows for faster and more accurate data analysis, but the strategic application of this information, especially in high-stakes situations, requires a deep understanding of market behavior, regulatory trends, and corporate risk tolerance.
Can you share examples of how AI & ML are improving liquidity risk management?
AI & ML technologies have revolutionized liquidity risk management in the financial sector. By analyzing large datasets and identifying complex patterns, these technologies offer actionable insights that help optimize liquidity strategies. Their implementation not only saves time but also improves overall productivity. Here are some key examples:
Cash Flow Prediction: AI algorithms can analyze vast historical datasets to forecast future cash flow trends with high accuracy. This enables businesses to anticipate liquidity needs and plan for contingencies proactively.
Automation of Routine Tasks: AI-powered automation streamlines routine liquidity management tasks, reducing errors and freeing up resources for more strategic activities.
Real-time Risk Assessment: Machine learning models assess various risk factors in real time, predicting potential liquidity challenges. This allows organizations to identify and mitigate risks before they escalate, safeguarding their financial stability.
Scenario Simulation: ML algorithms can simulate different financial scenarios, enabling proactive decision-making and enhancing risk preparedness.
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