From an overflowing inbox to targeted follow-up
In finance, a significant portion of communication with suppliers and customers still takes place via email. Suppliers inquire about the payment status of an invoice, report a missing order number, or send a reminder. Customers ask questions about invoices, dispute an amount, announce a payment, or request a copy of a document. Such messages often end up in shared inboxes managed by accounts payable and accounts receivable teams.
Processing begins with three key questions. What does the message say? A brief summary should outline the question, relevant context, and required action. What is the message about? The message can be categorized as, for example, a payment status inquiry, an invoice dispute, a master data change, or a payment breakdown. How quickly does it need to be handled? The email must be routed to the appropriate place in the work queue based on urgency and financial context.
UiPath Communications Mining, now part of UiPath IXP, was developed to convert unstructured communication into structured process data. A model can predict organization-specific labels and extract data such as invoice numbers, order numbers, amounts, dates, and requested actions from the text. A single message can contain multiple labels and requests. Each prediction is assigned a confidence score.
Summarization is a separate generative step. UiPath offers the Summarize Text activity, among others, for this purpose . A summary can quickly show an employee what has been requested, agreed upon, and is still pending in a long email chain. The original email remains the authoritative source. Attachments are not processed by Communications Mining by default; document processing, such as Document Understanding, can be added to the same workflow for this purpose.
Where traditional automation falls short
Traditional inbox rules work well with recognizable senders, fixed subject lines, and unambiguous words. For example, a message with “payment reminder” in the subject line can be moved to accounts payable. However, as soon as suppliers and customers phrase the same question differently, reliability decreases. A request for “the current status,” “expected payment date,” or “outstanding invoice” may essentially be the same request without a single shared keyword.
Furthermore, finding a specific word does not automatically mean the message is about that topic. An email might quote a previous reminder, deny that an invoice is outstanding, or combine multiple topics. Furthermore, long chains of replies contain old dates, amounts, and agreements alongside the current inquiry. Fixed rules often struggle to distinguish between these different contexts.
Prioritization is even more challenging. The word “urgent” says something about the sender’s wording, but not necessarily about the financial priority. Conversely, a neutrally worded message may actually require immediate attention because a payment deadline is approaching, a large amount is being disputed, or a critical supplier is threatening to suspend a delivery. Conversely, an emotional complaint does not always need to be handled first by a financial specialist.
Generative summarization does not automatically resolve this issue either. A summary may omit or misrepresent a reservation, a date, or a commitment that has not yet been confirmed. It should therefore not be the sole basis for a payment, a change in bank details, or any other financial transaction.
How intelligent automation can improve things
Intelligent automation can look beyond keywords and relate the meaning of a message to previously learned examples. Communications Mining uses an organization-specific taxonomy for this purpose. For example, an email can simultaneously be tagged as invoice disputed, delivery not received, and urgent. The extracted invoice and order references then make it possible to link the message to the correct supplier, customer, and transaction.
Based on confidence thresholds, the workflow can determine what happens next. A clearly recognized standard query can be automatically routed to the appropriate team or process. In cases of low model confidence, conflicting signals, or a high-risk category, human assessment takes over. This ensures that uncertainty isn’t hidden behind a single, seemingly definitive label.
The final priority is determined by two types of information. The intelligent layer recognizes, among other things, the request, potential urgency, sentiment, and specified deadlines. Fixed process rules add verifiable facts, such as the age of the message, SLA, due date, invoice amount, hold status, and the importance of the relationship. The model interprets the communication; the workflow calculates the handling priority based on defined criteria.
A generated summary can then be placed above the task as a guide, along with the recognized categories, relevant data, and a link to the original message. This allows employees to see what’s going on more quickly, without losing nuance or traceability.
Intelligent automation summarizes emails and recognizes requests and levels of urgency; fixed process rules then determine the routing, priority, and permitted follow-up actions.