AI in Treasury: Automating Intercompany Loan Pricing

The interest in AI has been increasing at record pace, with new technology releases every month and an impressive number of new use cases for chatbots or agents. However, many treasurers still struggle to translate this hype into value-added implementations for their business. These upcoming blog series explore practical use cases for AI that delivers concrete value to treasurers. This article specifically shares an implementation of AI agents to automate the pricing, review and documentation of intercompany loans.

The challenge

One of the core functions for many treasury departments is the distribution of cash in their group. Intra-group loans are one of the tools used by treasurers to distribute cash, but their use comes with challenges. The interest rate on intercompany loans has a significant impact on the tax expense incurred by the subsidiary and taxable income received by the head-office and therefore should be based on an arm's length approach.

Arm’s length pricing of intercompany loans requires a detailed analysis of the credit standing of the borrower, the influence of the group on the borrower’s credit risk, and a review of the debt market to determine a price. Due to the tax implications, this process should be documented in detail including sources and models. Regulations surrounding transfer pricing of financial transactions have been expanding in many countries in the past years, posing greater challenges for treasury teams to perform a complex process with high compliance requirements. As a result, many have resorted to advisors or build complex Excel models to perform the recurring pricing analyses.

The solution

The main challenge when automating the pricing of intercompany loans is the access to data and models. A compliant process relies on the periodic retrieval of benchmark transactions to set an interest rate – for example with bonds, loans, or a Credit Default Swap (CDS). On the other hand, an objective calculation of the credit risk requires a model that is explainable and is filled with company data.

AI Agents are a powerful tool that can be used to automate the retrieval of data and execution of tools in a complex process with changing inputs. In the diagram below, an AI Agent is coupled with a credit rating model, a tool to gather benchmarking data, and a documentation method. In our implementation, we have used the modules in the Zanders Transfer Pricing Solution to provide this functionality to the agent. Next to the tools, it is important to provide the Agent with sufficient context on what role it should play and how it should execute a pricing analysis. The agent should understand the compliance requirements and the tools before it can reliably run the process. For this use case, this is done through context engineering by providing example cases, differences in methods, and checks that the agent should perform.

In the resulting setup, the Agent can automate the full pricing step: identifying entities, calculating a credit rating, and running a pricing benchmark. As a result, the Agent provides a report that is generated according to our template.

With the same tools, we can extend the system even further, by reviewing past calculations versus policies and tax regulations to spot high risk cases or evaluating the credit position of entities to set borrowing limits or update credit ratings. By automating these processes, treasurers can move away from the operational hassle of pricing intercompany loans and instead focus on managing the funding mix of their group. Agents take care of automatic handling of loans and the importing or exporting of data, where treasurers set the objectives of the pricing approach and funding.

AI in Treasury is no longer a futuristic possibility - it's a practical, high-impact technology that Treasury teams can start leveraging today. Do you want to explore intercompany pricing? Let us know in this contact form below.

If you are interested in more use cases, read more here.