Harnessing Data with AI in Finance for Accurate Decision-Making

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Treasury has long been viewed as an operational cost due to the manual nature of populating and manipulating spreadsheets. Learn how harnessing data with AI can finance teams to make accurate decisions. 

Chapter 1

What is treasury’s current state of data management using spreadsheets?

Chapter 2

The Role of AI in Effective Decision-Making for Treasury

Chapter 3

Establishment of Digital Footprints in Harnessing Data with AI in Finance 
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Chapter 01

What is treasury’s current state of data management using spreadsheets?

One in every five major corporation treasurers reported losses due to spreadsheet errors, and such mistakes can be costly. While large corporations can endure such financial hardships, spreadsheet errors can cripple small to mid-sized businesses.

Every day, 2.5 quintillion bytes of data are produced on average. Organizations are now faced with the problem of gathering, managing, and extracting value from such data. One can see exponential growth in the data that businesses can access if they compare the data from the last few years with the available data. This data exceeds the amount that can be calculated, saved, and stored along with being kept. Managing this data presents a challenge.

This takes us to the underlying difficulties with using spreadsheets for data management, such as:

Difficulties with using Excel for data management

  • High volume of data: Large organizations might find themselves using tens of different business solutions, each with its data repository, including databases, CRM, ERP, and other tools. Evaluating and handling this much data presents a significant challenge.
  • Low data quality: Using spreadsheets affects data quality and poses challenges while gathering or processing data. With multiple individuals working on a single spreadsheet and numerous revisions and calculations happening simultaneously, spreadsheets are prone to errors. Excel allows data input from any source, making it inaccurate and of compromised quality. Businesses must eliminate unnecessary data while ensuring the presence of high-quality and accurate data the company would require to function efficiently.
  • Difficulty in data integration: This is the most common challenge for managing data in spreadsheets. Data integrity is at risk due to Excel’s inability to monitor real-time changes, especially when multiple users are in charge of a single dataset. The ultimate goal of having quality-ready data is to make it available for further analysis and processing by other business intelligence tools so executives can receive it and use it to make better decisions.
  • Erratic disaster data recovery: Using excel allows for coming in contact with various corrupted files despite regularly backing up the files. Excel recovery tools behave unpredictably when attempting to fix damaged worksheets, and recovered data only sometimes incorporates your most recent updates.
  • Unstructured to structured data transition: Almost the maximum number of data collected in spreadsheets is unstructured and dispersed. Businesses need to mine through and evaluate such data to extract value from it.
  • Limited data security and compliance: With an increasing number of regulators urging businesses to take reasonable actions to prevent regulatory breaches from occurring, companies must guarantee that their systems meet compliance standards. Excel’s absence of corporate security and compliance standards is a cause for concern, as using Excel makes it challenging to ensure regulatory compliance as the data can be susceptible to fraud and errors. An organization may not know who has accessed data on shared network drives, which is a security concern. Access to spreadsheets on these devices can also be unregulated.

The above challenges can be solved by implementing AI in workflows. According to Gartner, businesses are experiencing digital disruption due to the overwhelming amount of data they must manage. But with the help of AI, it’s feasible to integrate all that data into concrete results, from supply chains and sales and marketing to demand planning.

Chapter 02

The Role of AI in Effective Decision-Making for Treasury

How can AI in finance be applied to decision-making using data?

The potential impact of AI on the world economy is enormous. Data segmentation, validation, and processing are made easier by AI. Businesses and organizations can enhance their decision-making processes’ speed, accuracy, efficacy, and consistency by utilizing AI-powered datasets. AI can perform error-free analysis of large datasets in contrast to human analysis.


Making decisions today requires using technology to analyze tons of data. Due to its massive data processing and outcome prediction capacity, AI has established itself as a significant aspect of strategic decision-making.

What features in AI Cash Flow Forecasting Software helps to overcome the following challenges in harnessing data?

With cash flow forecasting software, CFOs and treasurers can comprehensively view their current cash balances and the tools and knowledge required to foresee volatility, use advanced management analytics, and modify the company’s risk management strategy accordingly.

The features that help the executives to overcome the mentioned challenges are as follows:

  • Having a single source of truth for data by removing data silos and consolidating data from multiple sources should be an organization’s top priority. 
  • Having adequate data quality monitoring standards ensures that decision-making is supported by reliable and high-quality data.
  • Having the ability to integrate data seamlessly will speed up the processing step to make informed decisions.
  • Safeguarding the data using AI to know how and by whom this data can be accessed would help avoid data breaches.
  • Analyzing large volumes of data to enable the company to import and temporarily manipulate data so it can be evaluated according to predetermined parameters.

As companies grow, forecasting cash flows becomes a top priority for making accurate decisions.

Chapter 03

Establishment of Digital Footprints in Harnessing Data with AI in Finance 

AI, as opposed to humans, can accurately and promptly evaluate massive datasets, allowing the staff to concentrate on other tasks. Businesses are also catching on; according to 66% of decision-makers, AI technologies are assisting them in boosting revenue and achieving their objectives. 

Introducing AI into a business decision-making process helps in:

  • Making better and instant decisions for businesses by quickly analyzing large datasets.
  • Boosting sales and marketing campaigns by recognizing how customers interact with their brand, language, and the appropriate tone to make them more appealing.
  • Understanding their customers better by providing businesses with a more in-depth insight into their customers’ pain points, expectations, and satisfaction levels. 
  • Making decisions that take into account vast amounts of complex data as AI is perfectly positioned to assist in making sense of massive amounts of data, particularly where a clearly defined outcome is measured.

AI is excellent at navigating complexity, examining large data sets, and quickly identifying trends. On the other hand, humans excel in comprehending external aspects and making more creative decisions.

How does AI cash flow forecasting software help finance leaders prioritize and make the right decisions?

Below are the ways AI cash forecasting software improves decision-making: 

  • AI cash flow forecasting software assists finance leaders in prioritizing and making the best decisions at every stage, from planning to implementation. It aids in processing project data and identifies trends affecting the project’s execution.
  • AI cash flow forecasting software powers functions ranging from planning to data gathering and tracking to reporting, and it can forecast outcomes based on many data points such as project size, contract type, and project management competency.
  • Using AI cash flow forecasting software to automate and optimize project data sets enables firms to maximize project investment value and generate savings for product development and organizational growth.
  • AI helps predict defects or redundancies early in projects, as every project is prone to risks. As well as assisting with overall risk analysis and reduction.
  • AI in finance assists leaders in problem-solving by delivering data-driven insights. Businesses may acquire a lot of data using AI and utilize it to make better decisions. 

For example, suppose a company is trying to choose which products to sell. In such a situation, AI can gather data on customer purchasing patterns and then harness data for recommending which products to sell.

To summarize, AI is constantly improving, making it more accessible and inexpensive to organizations of all sizes. By harnessing data with artificial intelligence, treasurers and CFOs can make better decisions, increase productivity, and promote creativity. 

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