Matt: the intelligent matcher

Automate the reconciliation process

What is the reconciliation process?

The reconciliation between two different streams is a very common operational activity in many business processes. Matt can help you reconcile the data by greatly increasing the efficiency of these processes.

Use cases
  • verify consistency between transfers made vs supplier invoices
  • check delivery notes vs carried out orders
  • check sales catalog prices vs supplier catalog
  • check warehouse stock with entries and exits of goods
  • verify micropayments with bank movements
Case study: the order to cash process
What is the order to cash process?

The Order to cash (OTC) process corresponds to the whole of the activities related to the customer cycle, said also client process, and consists of 6 phases:

  • Customer order
  • Order processing
  • Preparation of the order
  • Shipping and forwarding to the customer
  • Billing
  • Collection
What are the possible complications during the activity of manual reconciliation?

The most common can be:

  • duplication of voices
  • multiple transactions
  • payments of several games with a single transfer
  • absence of indication of the reference invoice in transfer
  • unrecognition of the payer
Is it an operational activity with an high margin of error?

For employees this kind of activity is usually long and burdensome, and themargin of human error is very high, a cause of the repetitiveness of tasks.

The advantages of
an intelligent matcher

Matt is the first robot able to associate and update the data directly on the company ERP

Connected to the data flows, Matt can read the documents relating to orders and orders takings. Thanks to its advanced cognitive skills (OCR) it’s able to manage and analyze easily even the most complex documents, such as PDFs received by e-mail and fax. Once identified, Matt is the only robot on the market that can bind data and update them directly on the company ERP without the need for direct intervention by the party of the operator. Moreover, through a Machine Learning mechanism Matt automatically learns and solve some of the most common problems, such as associating a bank transfer with a subject unrecognizable or identify in the absence of precise indications which matches the customer is paying among those still open.