8 Use Cases Of Hyperautomation Across Industries

If you work in the IT industry or you’re someone who is passionate about technologies, you must have probably heard about “hyperautomation”. Hyperautomation is one of the most talked-about terms these days and is most likely to remain a hot technology trend in the coming years. A leading research and advisory firm predicts that the global hyperautomation market will reach almost $600 billion by 2022, and rightly so.

What is hyperautomation?

Hyperautomation, in its true essence, brings together a few segments of automation instruments and advancements to upgrade the capacity to computerize work, which will be a vital factor in achieving success for companies and organizations in the present and the future.

In the post-COVID landscape, the pace of hyperautomation adoption has been more than ever as businesses across industries race for digital transformation. According to a study by McKinsey,?respondents indicated that their companies were able to adapt to digital changes atleast by 25 times faster; in the area of remote working solutions were implemented 40 times faster,?than they would have expected in the pre-pandemic condition.

The main objective of hyperautomation, like every other modern piece of tech is to make lives easier and reduce human labour. Hyperautomation does so by automating a large portion of the everyday processes of a business to promote better employee engagement, provide improved proficiency, and a better return on capital invested. By doing so, it shapes an imperative segment of the digital transformation of current organizations while liberating assets and capital for the company that can be utilized in other avenues and hence, helping in saving loads of money.

There is no single automation tool that can cover all aspects of human labour. So, this is where hyperautomation expects to combine multiple technologies such as?Business Process Management,?Robotic Process Automation, OCR, and AI, among others to make that happen. With the assistance of AI-fuelled decision-making capabilities, hyperautomation can facilitate organizations to envision their operational efficiencies, analyse key execution markers, and perceive how their processes coexist to derive anything of value. This, surely, can prompt business process innovation and open new opportunities that currently remain unexplored.

What is the difference between automation and Hyperautomation?

RPA vs Intelligent automation vs Hyperautomation

Before settling on hyperautomation, it is also important to understand the types of automation and the differences between them. Robotic process automation (RPA) is the use of software bots to automate huge volumes of highly repetitive tasks that mostly do not require human skills and can be executed based on rules. Consider payroll processing. It involves several simple, yet key processes like validating timesheet entries, attendance, earnings, reimbursements, taxes and resignations, etc. When rules are specified for each, RPA bots can manage payroll records, validate timesheet entries, manage employee leaves for pay, etc. accurately and quickly.?

Intelligent automation on the other hand involves technologies like artificial intelligence (AI), business process management (BPM), machine learning (ML), natural language processing (NLP),?and RPA to execute business processes without human intervention. Unlike RPA bots, intelligent automation applies AI and ML algorithms to analyse and make decisions on the process. For example, consider managing emails of the customer service department. Intelligent automation can categorize every email, summarize the content, distribute personalized correspondence, and route communications to the right people. Whereas RPA bots can read and reply to emails and download attachments based on set rules.?

Which Industries Use Hyperautomation??

There’s no dearth of opportunities for innovation. Hyperautomation can be applied across several industries from banking, insurance, healthcare, manufacturing, retail, education, and more. There are some generic processes, which may be applicable irrespective of the sector.?Some hyperautomation examples include

Reading emails:?Consider the customer service desk that receives hundreds of emails on any day. Imagine the time taken by an individual to respond to every email, if it has to be done manually. Natural language processing (NLP) can be used to detect the language, determine the tone, understand the context and the emails can be automatically routed to the respective user.?

Document processing:?Businesses handle a sea of documents such as receipts, invoices, medical records, purchase orders, business letters, transmittal pages, etc. With OCR capability, hyperautomation can assist in end-to-end automation of document management, including data extraction, validation, and sorting/classification on the desired format.

Data entry:?It is common for an organization to manage volumes of data. Whether it is capturing customer data from emails, order requests, etc onto a central application or validating the data with machine learning models to flag suspicious accounts, businesses can count on hyperautomation to perform these tasks accurately.

Here, we bring to you the popular hyperautomation top 8 use cases that are really practical.

1. Hyperautomation in Banking And Financial Services

Banking industry has an extraordinary potential of fairing well because of hyperautomation. Some of the sectors that can take the most advantage of hyperautomation, include regulatory reporting, marketing, sales and distribution, bank servicing, payment operations, lending operations, back-office operations, enterprise support, among others. For example, in eKYC measure, an intelligent character recognition solution allows manually written multipurpose KYC forms to electronic data in suitable fields of KYC portals. This information is additionally populated in other related systems.

Smart Automation systems powered by Al calculations can productively screen the exchanges and proactively recognize fraudulent and malicious exercises. AI-based machine learning model fabricated utilizing advanced modeling techniques can anticipate the likelihood of malicious exchanges, and hence, minimize or eliminate risks. Numerous anti-money laundering (AML) systems today influence hyperautomation innovation stack for forecast and prevention.

Numerous banks and other financial organizations have begun using advanced analytics in application screening, for surveying the reimbursement ability of a client by taking a gander at different boundaries which is normally incomprehensible through manual screening. This reduces the likelihood of non-performing assets in future if it is separated in the application stage itself.

2. Hyperautomation in Insurance

If there’s one industry that was most busy and flourishing during the global crisis, then it has to be insurance. Insurtechs had to ensure customers get a seamless, digital experience round the clock. Forrester’s prediction, “2022 is a year to be bold. The old ways of working no longer work. The future is up for grabs”, exactly suits insurance, which is plagued with extensive, manual and monotonous processes.

To read more, visit our website : https://www.vuram.com/blog/5-use-cases-of-hyperautomation-across-industries-in-2021/

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