How AI is Reshaping the Treasury Analyst Role
What you’ll learn
- The roles, responsibilities, and KPIs of a treasury analyst.
- Traditional vs. digital way of cash forecasting.
- Treasury analyst can focus more on strategic analysis than manual tasks.
The Traditional Routine
An analyst takes part in the four core activities of cash forecasting. This is how the key components look like when an analyst follows the traditional approach:- Data Gathering:
- Most hectic and time-consuming process.
- Includes extracting data from disparate data sources, systems, and teams.
- Consolidation:
- After data aggregation, analysts need to consolidate all the data in one place.
- Manually consolidating spreadsheets consumes plenty of time and increases the scope for errors.
- Decisions:
- Helping in making decisions that revolve mostly around debt or investments.
- Making monthly or quarterly reports to share with managers, treasurers, and CFOs to make decisions.
- Variance Analysis:
- Variance analysis compares the forecasts to the actuals. This process is important for identifying errors and taking informed decisions to hone business performance.
- The manual processes make it next to impossible to perform variance analysis for more than one duration due to time crunches. So, it ends up being performed either weekly or monthly.
The Routine of Treasury Analyst with Traditional Process
The traditional routine makes the treasury analyst appear like a number cruncher. The low-value work makes an impression of the analyst as a low-value employee.
To earn the seat of a high-value employee, the analyst needs to adopt the digital routine.
The Routine of Treasury Analyst with Digital Process
Upgrading to the digital way of forecasting cash reforms the roles and responsibilities of a treasury analyst, hence reforming all the four processes:
- Data Gathering:
- A centralized system collects and stores real-time data automatically from multiple banks, ERPs, TMS from different regions.
- Consolidation:
- All the useful data is integrated at a single place so drilling down on entities becomes easier.
- The data can be viewed according to the user’s privileges.
- Decisions:
- The decisions on investments and debts can be made more accurately for the short-term and long-term.
- Since automation improves forecast accuracy over multiple horizons, analysts can make decisions on improving data quality more confidently.
- Variance Analysis:
- Automated data aggregation saves enough time, making it possible to run multiple variance analyses across multiple business units, categories, regions, and multiple durations.
With digital routine, the quarter-ending cash position is predicted more accurately and reporting becomes more data-driven.
Effect on the Role of an Analyst
Instead of a number cruncher, the treasury analyst is considered as an analysis specialist and a go-to person who provides better insights, adds depth to the information, and is proactive towards challenges. Hence, making an impression of the analyst as a high-value employee.
Making a difference with Automation
Technology shifts the process-focused skill sets to value-focused skill sets, for instance:
- Accounting skills are shifted to Technical skills
- Analytical skills are shifted to Critical Thinking skills
- Financial Literacy skills are shifted to Leadership and Management skills
Analysts focus on problem-solving and manage resources with the help of technology, so they add more value.
KPIs of Treasury with Automation
Since AI takes care of manual tasks like data gathering and consolidation, treasury analysts can focus more on the quality of reports and the accuracy of forecasts. Automation transforms the KPIs to be more forward-looking and upward-looking. A treasury analyst’s performance KPIs include:
- Average weekly forecast variance
- Tracking the percent of visibility across multiple accounts
- Tracking percentage of account balances reported automatically
- Tracking forecast accuracy
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