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AI, ML could shape Liquidity Management

RBI can do a world of good to create a more robust environment for NBFCs on liquidity side:

AI, ML could shape Liquidity Management

The world over there have been increasing 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. It is a common knowledge that stable environment offers a stable and predictable liquidity scenario, stable interest rate scenario and offer lower risks both on liquidity and interest rates. However, this is a mirage one should stop chasing. With each passing day, financial world of the whole universe is getting more and more interlinked and interdependent.

Considering the world, all are living in, it should be safely presumed that only certainty is uncertainty. The world is moving from a bipolar world to multipolar world and over and above that on one thing on whom no one has any control is ‘Mother Nature’. Kishore Lodha, Chief Financial Officer, U GRO Capital, hints: “So, we should remain prepared for more and more global conflicts, small- and large-scale wars and over and above that small-scale and large-scale natural calamities. It will always keep the cashflow, liquidity and interest rates in an uncertain zone.”

Thankfully central banks across the globe are taking steps to minimize the risks of any of such eventuality. Central banks, and more specially RBI, are far more prepared now to deal with any geopolitical event or a natural calamity. A case in point is covid. The way world across, entire cash flow system, liquidity and interest rates were managed by most of the impactful countries, speaks volumes about preparedness of the world to deal with such eventualities. Kishore alerts: “Collateral damages are bound to happen, and it will keep the whole economic environment vulnerable, uncertain, complex, and ambiguous.”

Robust Environment For NBFCs

The whole financial system which has been designed by RBI in consultation with GOI is far more robust compared to most other countries in the world. Now NBFCs are dispensing roughly about 20% of total formal credit in India. RBI has taken many steps in recent years to make the system stronger for NBFCs as well. Scale based framework, LCR (liquidity coverage ratio) requirement, ALM (Asset Liability Management) monitoring on a regular basis are some of the steps taken by RBI that will reduce the liquidity risk for NBFCs but all of it will come with a cost.

Today most NBFCs are far more aware about ALM mismatches then 5 years back. A clear sign is lower reliance of NBFCs on commercial papers. Though it offers a good rate arbitrage, it is negative on ALM. Kishore points out: “Hence overall CPs for NBFCs have gone down from 15% in total borrowing to about 7% now, in a matter of only 4 years.”

But regulator probably is expected to do a little more for NBFCs. Kishore suggests: “If a window can be opened by RBI to freely park surplus funds on NBFCs with a designate intermediary and also allow to make temporary borrowings against free assets, it will do a world of good to create a more robust environment for NBFCs on liquidity side.”

Data Quality For Forecasting

Many kinds of risks can be forecasted with available data. It is necessary to know about quality and quantity of data available for liquidity forecasting. Kishore informs: “Availability of data helps in predicting the overall market scenarios on liquidity and interest rates. A lot of data is freely available these days, which gives good indication of what is in store in near future. It helps financiers to make their strategies accordingly and also mobilize their resources to mitigate any potential risk.”

Too Much Data Not Required

There are certain aspects of liquidity risk management, which are algorithmic and other aspects, which are expertise/wisdom. Algorithmic approach towards liquidity risk management is good to hear, but those who have spent some time in these markets are generally aware that what is coming on liquidity and interest rate side unless it’s a covid kind of situation. Central bank, world forums like IMF, monetary policies of respective countries and a lot of raw data on availability of liquidity, sectoral allocations and foreseeable events are freely available to anyone who wants to see it, go through it and make some plans around it. Kishore firmly feels: “It does not require too much data science at the entity level to gaze market outlooks.”

As of now the major challenge on these sides from a financier’s point of view are governance, eye on ALM management and creating some buffers, even at the cost of sacrificing some profits. Kishore believes: “For an NBFC, if governance is good, ALM is genuinely well managed. To have the appetite to create a reasonable buffer of liquidity, it does not require too much of external or internal intelligence to manage liquidity risk.”

Role Of AI & ML For NBFCs

It is interesting to understand how AI (artificial intelligence) and ML (machine learning) are improving liquidity risk management. As of now liquidity risk management in NBFC space in India is mostly driven by Board driven policies, quality of governance, and a clear and transparent view on ALM. Kishore observes: “AI and ML are not playing much role for NBFCs in liquidity risk management. However, for larger universe, for people who are involved with monetary policy of the country, both AI and ML does play an important role.”

There are several macro and micro factors involved in overall liquidity management, where AI and ML can play very important role. Let us take a broad example. Kishore further cites: “A prediction of monsoon is also a machine learning and artificial intelligence, which has a huge impact on the overall economic scenario and also has impact on the overall liquidity management of the system.”


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