Empowering Financial Success Through Streamlined Cash Flow Analysis

Using Querri AI for Cash Flow Management

Cash flow, the lifeblood of any business, is especially critical for smaller and growing enterprises where the margin for error is minimal. Profitable companies can still fail if they do not manage cash flow effectively. Querri can be an invaluable tool for a business to better understand and improve cash flows.

Throughout this series, we will explore various aspects of the cash conversion cycle and showcase examples of how Querri can take available data, leverage AI and ML tools, and provide valuable insights into that data.

Cash Conversion Cycle: Accounts Receivable Analysis

Understanding payment behavior from customers is a first step to being able to improve receivables timing. Surprisingly, nearly half of all Business-to-Business invoices in the United States are paid after the due date. Causes for this range from businesses posting wires at the due date to strategic manipulation of cash positions for key period ends to struggling customers having difficulty making payments.

Querri empowers you to quickly identify differences in customer behavior, enabling you to focus on addressing payment issues and managing risks associated with potential non-payments.

For a demonstration exercise, Querri was given a sample payment history file with 1000 rows of manufactured data, as seen here.

Using simple prompts, we asked Querri to analyze the data to find customer groupings associated with payment behavior. Querri took the informal language of the prompt, restructured it as shown here, and then generated a K-means clustering.

It then went a step further and provided insight into the clusters, as seen here:

While this is a very simple example based on manufactured data, it illustrates the ability of the AI to find and understand different customer groupings, providing information that could then be used to address the different situations of the different customer groups.

Payments can also show a lot of variation based on the time of year. While the data here was not large enough for the AI to pick up clear historical trends, it was able to provide some information. Importantly, it was also able to recognize that it did not have sufficient data to give a good answer and called that out. This can be very beneficial, as AI and people both often have a hard time giving an answer of “I don’t know,” and it’s valuable to get that response when appropriate.


Querri can enable businesses to quickly gain insights on payment history from customers and trends over time. These insights can enable the business to take steps to improve receivables and better forecast the timing of payments.


  • Cash Conversion Cycle: Accounts Receivables Management (ARM)
  • Cash Conversion Cycle: Accounts Payable Management (APM)
  • Cash Conversion Cycle: Inventory and Assets
  • Cash Conversion Cycle: Overall Forecast
  • Implementation Considerations: Time, Cost, and User-Friendly Solutions

Beyond the core steps of the Cash Conversion Cycle, we'll delve into the practical considerations of implementing AI solutions. While custom AI and ML solutions can be time-intensive and costly, we introduce user-friendly solutions like Querri, designed to reduce the time and cost required to apply AI to the cash cycle. By enabling users to interact with their data, Querri transforms the complex relationship between technology and people into a symbiotic partnership.



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