How much automation can finance processes achieve?

How much automation can finance processes achieve? | ABBYY Blog Post

Despite noticeable efforts by enterprises to automate their work processes, tasks such as entering invoices and orders into financial systems are surprisingly often based on manual, error-strewn data-entry methods. However, organizations need quick and easy access to information, paired with a high degree of process automation to be able to innovate and keep pace with the competition. Automating finance processes brings an array of benefits to businesses. With data-capture driven solutions leveraging robotic technologies and machine learning algorithms, an automation rate of up to 80 percent can be achieved for invoice operations. Finance processing automation reduces costs, strengthens internal controls, enables the optimization of cash flow and has a positive effect on supplier and customer satisfaction. In addition to accelerating business processes, automation frees up employees from repetitive, time-consuming and error-prone tasks, allowing them to spend more time on important tasks that bring value to the business.

Read the full article here: Finance Digest.

Accounts Payable Finance & Accounting Intelligent Capture
Marlene Wolfgruber ABBYY

Dr. Marlene Wolfgruber

Director of Product Marketing - Mobile

Dr. Marlene Wolfgruber is Director of Product Marketing Mobile and responsible for driving ABBYY’s mobile portfolio and the go-to-market strategy. Her current focus is to continuously improve mobile implementations in order to drive enhanced productivity and better customer experience.

Prior to her role global role, she was responsible for the product marketing strategy and driving the Data Capture portfolio on the European market. Marlene gathered several years of experience in product management and marketing having worked for a variety of technology companies in the realm of information and document management and data capture, e-mail management, and natural language processing.

Marlene holds a university degree in computer linguistics and a Ph.D. on the topic of Sentiment Analysis and lives in Munich.

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