How automation and AI can reshape corporate treasury in India

Discover how AI, automation, and agile practices are transforming corporate treasuries in India for faster, data-driven decisions.

Key takeaways

Many corporate treasuries still rely heavily on spreadsheets and fragmented systems.
This poses significant risks to timely decision-making and managing multi-geography complexities.
Success in treasury transformation can be measured by faster turnaround times, sharper data-driven decision-making and integrated risk and control analytics.
Data standardization and integration would be prerequisites to deploying AI.
More efficient treasury operations require mindset change, agile approaches and continuous leveraging risk and control analytics.
Coding skills are highly relevant at present in front, mid and back offices of corporate treasuries.

Adopt intelligent agents to address time-consuming problem statements. The key KPI should be reducing turnaround times for decision-making, execution and review. Establishing a Center of Excellence within a GCC is a proven option.

Hemal Shah

Partner, Consulting Services, EY India

For your convenience, full text transcript of this podcast is available below.

Pallavi: Welcome to EY India Insights podcast. Today, we are exploring the future of corporate treasury in India, drawing insights from our latest report India Corporate Treasury Survey 2025. We will discuss how AI and automation are transforming treasury functions, and what companies need to do to stay ahead.

Joining us is Hemal Shah, Partner, Consulting Services, EY India. Hemal brings deep experience of over 25 years in helping organizations with various problem statements related to treasury and commodity trading functions and also transforms their finance and treasury operations. Hemal, welcome to our podcast today.

Hemal: Thank you!

Pallavi: Thank you Hemal. To begin with, many treasuries in India still rely on spreadsheets and have weak reporting dashboards. What risks does this pose for decision-making and agility?

Hemal: The risks are quite significant in this case and maybe I can explain this thing with a couple of examples. We have seen large companies with global operations having multiple functional currencies interplay with various currency pairs. It is very difficult for a group CFOs of such organizations to form a big picture of consolidating what the true risks are and then decide what should be the appropriate hedging decisions.

Imagine if such type of information is lying in disaggregated spreadsheets or systems in different entities, it will be a nightmare for the CFO to take the right decision at the right point in time and we have typically seen that such type of exercises are conducted on spreadsheets and the time delay it takes to take decision is enormous. Let me also give another example. One of our clients — they also had multi-geography operations, they were operating in different geographies — was having different businesses and different entities. Each of the businesses and entities was taking funds from the respective banking partners in those regions. It was very difficult for the group CFO, based out of India, to build a big picture in terms of what their average cost of funds was, how much funds had they taken from the banks, whether this borrowing was commensurate with the net working capital needs of those respective businesses. So, it was very difficult for the group CFO to draw an appropriate decision.

Such activities also happen on spreadsheets – they are disaggregated in nature – and it creates a lot of chaos for stakeholders at the right level to form the big picture.

Pallavi: Focusing of the EY report – AI and automation are becoming critical for future of treasury – which use cases, like cash forecasting, effects exposure prediction or anomaly detection, are proving the most valuable and where are the organizations struggling to scale?

Hemal: Before AI comes data standardization and integration. If data is harmonized and not disaggregated or disintegrated, all types of prompt-based decision-support AI-enabled tools are possible. For example, if you have data sets to predict the realization of your cash flows or collections based out of different customers in different regions having different terms of payment, and if you are able to use a pattern and use it for your accurate decision making, it will help you solve the problem statement.

Imagine a scenario wherein there are multiple clauses in a business contract, and you would need to find inconsistency. You are able to do such type of task-oriented activities in a far more lucid manner.

The third example I would want to give is simple correlation of business financial performance with the financial markets and the treasury policy decisions or transactions which you have taken. If you have these three things integrated over a period of time, it will help you to take sharper decisions in future.

Pallavi: Thank you, Hemal. How should treasuries rethink their technology stack and operating models, whether through cloud-native solutions, ERP integration, or hybrid setups like shared services and GCCs?

Hemal: My take after observing many treasuries for many years is to keep the stack as simple as possible – do not over-customize it, do not over-engineer it, however, keep it integrated, so that there is data consolidation happening seamlessly. Adopt agents to solve problem statements which are taking a lot of time. Your KPI has to be to reduce the TAT or the turnaround time to take or execute or review decisions related to the transactions. A Center of Excellence within a GCC is a great practice. With the simple objective of continuous improvement in all the sub-functions of treasury processes, you are able to achieve these objectives.

Pallavi: Thank you, Hemal. Now upskilling in data coding and automation has become essential for many organizations. So, how can treasuries close this talent gap and build teams that are ready for the future?

Hemal: This is very important. Traditionally, we have seen that treasurers have been a very smart community. They have always adapted themselves very well, and they have always made their spreadsheets smarter. However, now they need to institutionalize these spreadsheets or integrate them so that their prompts get smarter. Coding skills are imperative and very important and an integral part of today's treasury setup. Change management and program management skills are also equally important, and that is the reason you will see in today's treasury setup mid office, back office, front office individuals possessing skills related to Python or C++ or angular or Alteryx or JavaScript. All of this has become very relevant nowadays.

Pallavi: Thank you, Hemal. As we have discussed the automation aspect, we would like to know the challenges as well. So, what are the biggest roadblocks to modernizing treasury – such as legacy systems or data quality, or even regulatory constraints? And how can organizations overcome them?

Hemal: The first thing is the mindset, the fear of managing change or the ambition of achieving everything in a conventional IT architecture, or not willing to experiment with agile or dynamic setups, or maybe continuously being exposed to huge costs on account of inflexibility. Over a period of time, as treasurers see more success stories or proof of concepts, it will only help them build comfort and confidence.

Agile CoEs are the way to go. Agile operating models are the way to go, and these things in the functional setup will make things easier and faster for a lot of treasury teams.

Pallavi: One last question. Looking ahead, how will success in treasury transformation be measured, and what are the practical steps should companies take today to lay a strong foundation for AI and automation?

Hemal: I will answer the second question first. The strong foundation is – simplicity of data, harmonization of data, integration of data. The KPIs are as follows and very simple:

(a) We are doing all these things for faster turnaround times; our TATs have to be faster.
(b) Our decision support has to be far sharper. Each of our prospective decisions need to be backed by appropriate data and insights software so that we are taking better decisions.
(c) Our data lakes need to be far more insightful because they are integrated with our ERPs, banking systems and market systems.
(d) We need to ensure that in a treasury setup, there is a continuous process of identifying smarter agile digital brakes, which will help in reduction of manual activities.
(e) At a principle level, to reduce dependency on spreadsheets, institutionalize them, integrate them.

And the final thing would be, after integrating, how are we doing these things on a continuous risk and control and how are we leveraging continuous risk and control analytics to help us run faster, better and efficient treasury function. So these are some of the KPIs that are on top of my mind.

Pallavi: Thank you. Thank you for joining and sharing these insights with us. It is clear that AI and automation are no longer optional for treasuries they are critical to resilience, agility and strategic decision-making.

Hemal: Thank you.

Pallavi: That wraps up for this episode of EY India Insights podcast. To read the full India Corporate Treasury Report Survey 2025, please do visit our website. Until next time, this is Pallavi signing off.

Presenters

Hemal Shah

Partner, Consulting Services, EY India

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Duration

10m 45s

India Corporate Treasury Survey 2025: How AI and automation are redefining treasury

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How automation and AI can reshape corporate treasury in India

Key takeaways

Presenters

Hemal Shah

India Corporate Treasury Survey 2025: How AI and automation are redefining treasury

The India Corporate Treasury Survey 2025 explores how operating models, technology and automation are shaping the future of treasury in India.