AI training for the receivables and collections team

For whom and for what problem

The program is intended for people who prepare reminders, check arrears, or clarify discrepancies with clients. It is useful when information about a payment and a refund remains in different places, and the team wants to use AI to collect and describe the case. The person responsible for accounts determines the correct data; the client's guardian helps assess whether the draft includes the last contact.

Before the session, we select recurring tasks and define participants' experience. New users need practice in citing sources and distinguishing the model's result from the fact. People already using AI can work on longer threads, conflicting information, and comparing several versions of the draft. The number of groups and the scope of preparation are recorded in the offer.

From balance to material for the person approving

The first part concerns input data. Participants determine where the amount comes from, when it is current, and what cannot be confirmed from the received materials. We practice describing the case with reference to the invoice, reconciliation, and correspondence. We do not determine the amount or the assignment of transfers based on a convincing model response.

In a fictional example, the transfer includes several invoices, and one still appears as overdue. The task is to indicate the unconfirmed payment and prepare a question for accounting. As long as there is no reconciliation, the participant should not create a ready payment request for the original amount. This case allows checking whether the person knows how to pause work in the absence of data.

The next part involves correspondence. The participant prepares a summary of the client's objection and a draft of the response or question for the guardian. They separate the client's statement, internal agreement, and the message actually sent. The instructor points out sentences without a source, omitted objections, and a tone exceeding the agreed purpose of the contact. After correction, the material is read by the person who is to take the next action.

What can be automated and what requires clarification

The team outlines the current process: preparing a list, checking reconciliation, gathering correspondence, reviewing, and contacting. For the selected stage, we indicate input data, owner, and conditions for stopping. First, we check what the existing program already handles. The training does not require purchasing a separate agent.

In the example of changing the balance after preparing the message, we discuss why approval must concern the current version of the case. Separately, we analyze the repetition after a sending error: restarting the task should not lead to a second contact. These are requirements for the project and receipt; participation in the session does not confirm that the existing application already meets them.

Demo of debt monitoring can be used to discuss the limitations of automation. It uses synthetic data and does not send messages. There is also no confirmed implementation at the client's side. We do not present it as proof of recovered payment. A broader context of the choice of scope is described in the debt monitoring guide.

How we check independent work

The final task changes the conditions relative to the discussed example: the payment is partial, the client refers to another invoice, or a new agreement appears in a separate thread. The participant prepares the material without step-by-step guidance. We assess the consistency of the amount with the source, the handling of uncertainty, the correct recipient, and the justification for the decision to continue the review.

The instructor receives the agreed review of the difficulties revealed in the tasks and the topics requiring further practice. If the participant orders a later session, we can return to new, prepared-for-training cases and check whether the way of work has remained consistent. The result of the exercise is not a measure of the effectiveness of debt collection or a proof of a lasting change in the team's work.

Data, AI competencies, and legal scope

The company approves the tool, accounts, and materials allowed for training. We can work on completely fictional data; merely removing a name does not always prevent the identification of a person from the remaining correspondence. Access, data protection rules, and storage of results are agreed before the session.

Materials from the European Commission on AI competencies emphasize the importance of the tool's usage context and the experience of people using it. Therefore, the finance program includes incorrect amounts, outdated sources, and supervision of contact. Completing this workshop is not confirmation of the organization's compliance with the AI Act.

Rules for calculating interest, fees, formal demands, and further proceedings are not set by the model. The company provides approved rules, and issues requiring interpretation of the regulations on transaction delays consults with the appropriate specialist. The workshop concerns working with AI and does not replace legal training on debt collection.

Preparation of sessions and next steps

Format online or at the client's location, materials, licenses, and possible subsequent sessions are agreed before ordering. Syntalith does not sell licenses. Connecting the financial system, launching the sending, and building the application are separate scopes; they are not automatically included in the price of the training day.

Describe the stage of preparing a reminder that most often returns for correction. We will select tasks and the involvement of finance and client guardians. Prices and settlement rules can be found in the price list, and the organization of sessions in the team training offer.

How much does the training cost and when does it pay off

The training starts at €1,150 net per day. We calculate the return on one process, without assumptions about the entire company.

According to a study published in Science (2023), the time for writing tasks with ChatGPT has dropped by 40%. Therefore, the scenario adopts a cautious weekly hour and checks it with measurement.