Advanced Computing in the Age of AI|Wednesday, July 15, 2020
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New Wave of Fintech Automation Tools Arrives 

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AI market analysts reckon the financial services sector is among those ripest for task automation in areas like regulatory compliance and other manual processes that previously required deep domain knowledge.

In response, new AI tools are emerging to help automate often tedious but essential financial tasks such as accounting operations while also allowing users to automatically extract data from documents that could be leveraged for real-world applications like risk management.

Among the new tools is an upgraded AI platform from fintech specialist AppZen Inc. released this week that covers a growing list of new use cases. In one example, the platform can compare vendor invoices with shipping information to confirm invoice accuracy. The automation tool draws on data points like bills of lading and factors in detailed information like quantities shipped and how much a shipment weighed.

In another example, the system can scan credit card statements rather than poring through individual receipts to confirm that expense items are legitimate. The expense report tool uses semantic understanding to confirm that expenses adhere to company policies.

The AI tool illustrates how platform developers are targeting business processes that previously required domain knowledge and detailed understanding of financial transactions. Automation could, for example, replace human reviewers who scanned expense reports and accompanying receipt images.

A tougher task, analysts note, is automating higher-level business processes that nevertheless promise a greater return on AI investments. Those include risk management, which is seen as among the most challenging business processes to automate, according to a recent study by business consultant McKinsey Global Institute.

Vendors like AppZen assert that automating tasks like compliance, audits, accounts payable and expenses will free financial analysts to switch from scanning individual transactions to look for patterns that could help companies manage risks. Among the goals is helping financial analysts make decisions before transactions take place.

Hence, AppZen said its upgraded platform combines deep learning and semantic analysis with its patented computer vision technology as a predictive tool for reducing costs, achieving efficiencies and complying with financial regulations.

AppZen’s platform incorporates deep learning and semantic analysis with what it claims are thousands of data sources used to train algorithms touted as understanding financial transactions. The San Jose-based vendor said its customers include about one-fourth of the Fortune 500.

The McKinsey report on financial automation notes that current generation IT infrastructure has made it much easier to deploy tasks automation tools. “Where a manager once had to wait for an overtasked IT team to configure a bot, today a finance person can often be trained to develop much of the [robotic process automation] workflow,” the business consultant noted.

Steady improvement in machine learning algorithms and natural language tools means as much as half of standard financial functions ranging from accounting to financial planning and analysis can be automated, McKinsey said.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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