How AI is Reshaping the Role of a Treasury Manager

The traditional way of cash forecasting

Generally, In a manual environment, the four core subprocesses are:

1. Data Gathering: It involves extracting data from multiple sources, country controllers, multiple departments, which is laborious.

2. Consolidation: Manually consolidating data into spreadsheets is time-intensive, cumbersome, and error-prone.

3. Decisions: Decisions based on long-term forecasts such as how much to draw on the revolver, or decisions on deleveraging to pay long-term debts at low-interest rates, or short-term forecasts to manage daily cash positions.

4. Variance Analysis: Forecast is compared to actuals, but it is performed usually for just one duration due to time constraints.

Manual-based cash forecasting is not just painstaking but comes with a lot of hurdles.

The Enduring Cash Forecasting Challenge

Despite most enterprises adopting a TMS, or a cash forecasting module, cash forecasting remains to be the biggest issue across industries.

According to the surveys at HighRadius, it has been noticed that Accounts Receivable and Accounts Payable are the toughest cash flow categories to forecast, but they are also the most important cash flows since they measure a firm’s revenue.

Since the forecasts have been inaccurate, treasury managers need to take remediation steps such as:

• Increasing cash buffers: This might not be a good decision for the long term, since proper actions can’t be taken, and overborrowing is detrimental to liquidity.
• Borrowing at higher costs: Not keeping an eye on the revolver, and borrowing with just two weeks of notice or on the same day can cause great losses.
• Paying penalties: Not releasing A/P or delayed payments that cause penalties.
• Delaying investments: Poor decisions due to low confidence hinder a firm from seizing the opportunities like investments that are beneficial. Besides, idle cash affects financial health negatively.

These challenges can be minimized by high margins by adopting robust and scalable technologies like Artificial Intelligence.

Cash Forecasting with Artificial Intelligence

The AI-based approach simplifies cash forecasting subprocesses:

1. Data Gathering: Using a cash forecasting tool, data is extracted directly from the sources and systems, and presented as a single source of truth.

2. Consolidation: The system automatically consolidates data into regional and global views.

3. Decisions: Accurate data leads to accurate forecasts, which support finer decision-making.

4. Variance Analysis: Variance analysis can be carried out more frequently over multiple time horizons, hence performance can be measured frequently, and improved accordingly.

5. Scenario Analysis: Running scenario analysis is easier by changing multiple parameters and checking how they impact cash flows to take precautionary/ recovery steps in cases of distress.

AI-powered cash forecasting is not just time-saving but also foolproof as it guarantees a higher degree of accuracy.

Strategic Transformation of Treasury Manager with AI

With more forecast accuracy, richer reporting, and better decision-making through automation, the treasury manager is seen as a process specialist, who performs high-value tasks and is seen as a highly valued employee.

KPIs of a treasury manager with Automation

Technology transforms these KPIs into higher value KPIs that helps in closely keeping an eye on the future by handling manual tasks. The most common KPIs of a treasury manager involve:

• Average monthly bank fees
• Counterparty exposure by counterparty
• Average monthly/quarterly forecast variance
• Average hours to forecast and reforecast