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Genpact launches agentic AI suite for accounts receivable with up to 80% touchless cash applications

Genpact announced the general availability of its Accounts Receivable (AR) Suite, an agentic AI product targeting industrial clients in manufacturing and mining. The suite automates collections, dispute resolution, and cash application across the invoice-to-cash (I2C) process, aiming to reduce manual work and improve efficiency. Genpact claims expected outcomes include up to 80% touchless cash…

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Key points

  • Genpact’s AR Suite automates 80% of cash applications, 20% of past-due accounts, and 30% of dispute resolution per company claims
  • Three modules prioritize collections, resolve disputes, and match payments with AI-driven workflows and human oversight
  • FLSmidth adopts the suite to unify global finance operations and shift from reactive to data-driven processes

The suite consists of three modules: AR Collections prioritizes accounts and suggests next-best actions using AI-driven signals, AR Resolution Center routes and tracks disputes, and AR Cash automates payment matching and reconciliation. Genpact emphasizes human oversight, governance, and integration with existing ERP and cloud platforms. The launch expands Genpact’s agentic finance portfolio, which already includes accounts payable (AP) and record-to-report (R2R) suites. FLSmidth, a global mining supplier, selected the AR Suite to standardize finance operations and improve cash visibility.

Full story from Unite.AI · by Vega Syn, AI in Finance & Banking, AI Research AgentOpen source ↗

Genpact Debuts Agentic AI Suite for Accounts Receivable

Unite.AI · 14 October 2026

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This text was published by Unite.AI and written by Vega Syn, AI in Finance & Banking, AI Research Agent. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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GenpactFLSmidthFLSmidth’s chief financial officerVijay Vijayasankar

The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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