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Artificial Intelligence Can Solve Back Office Paperwork

By Victor Thomson , Feb 21, 2017 03:44 AM EST
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Artificial intelligence seems to offer unlimited potential. After winning against humans at sophisticated games such as go and chess, AI's next step appears to be conquering our workspace in various industries, including the boring back office paperwork.

AI Takes Jobs In Various Industries

Today's enthusiasm about deep learning and machine learning has been anticipated much earlier than we would think. According to Forbes, the visionary mathematician Alan Turing was anticipating today's deep learning by intelligent machines as well as the concerns about the impact of artificial intelligence on jobs even since the 1950s. AI technological advances are propeling what some experts are calling a "new industrial revolution."

After functioning as chefs, layers, surgeons, financial analysts, translators and customer service reps, tech experts predict that AI would soon take over the enterprise back office. This won't be much of a surprise, since artificial intelligence can run well office tasks that are often boring and tedious. Government agencies, insurance companies and large financial institutions have to process huge volumes of paperwork that can be relatively easily automated and passed to AI.

According to Venture Beat, compliance sheets, employee records, medical forms and insurance claims are just some examples of the common documents processed through regulatory filing, auditing and data entry. Startups focusing on robotics, work automation and AI are already designing systems that can do these tasks for us. For instance, late last year, a provider of AI solutions for enterprises and government institutions called HyperScience has been able to raise $18 million in series A funding.

The company is focusing on developing back office automations that are using image recognition and computer vision technologies in order to process data. Their solutions can fill in all the data fields into a company's enterprise system, scan forms and recognize handwriting with higher-than-human accuracy. HyperScience's solutions raise the bar on analytical prowess and data accuracy, aside from significant reductions in time and energy costs.

AI Can Work On Back Office Paperwork

Among the company's solutions, the one called HS Evaluate can analyze complex claims and applications such as those used in government agencies and insurance companies. HS Evaluate can parse through the human language in an application form or claim and determine relevant and irrelevant information. In the end, the application can highlight all the information needed in order to support making correct assessments and taking the right decisions.

According to HyperScience founder Peter Brodsky, this capability delivers great value for service providers, as well as their customers. Among the service providers that can benefit from using this app are medical, health and insurance companies. HS Evaluate helps expediting the resolution period for medical treatment, insurance claims and coverage applications.

Thousands of expense reports are processed daily by large enterprises and such volume of transactions can become overwhelming. Another important player in the back office AI market is a company called AppZen. Their applications also use natural language processing (NLP) and computer vision to automate and audit a company's expenses.

AppZen offers a complete solution that automatically captures expenses and analyzes credit card transactions, receipts and travel bookings. These data is then audited, catches costly human error and ensuring that everything complies with regulatory standards. By helping companies to meet compliance standards, AI can go far beyond just trimming down back office load.

Larger enterprise systems like NetSuite and Oracle can be integrated with many of these AI available solutions. This way, it becomes cost-effective and much easier for most companies to automate their back office tasks. By doing so, they ensure overcoming the challenges posed by massive quantity of non-core data, becoming free to focus on the data that really matters in their effort to push business forward.

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