7 Best Use Cases of Cognitive Automation

RPA is a huge boon for the likes of the contact centre industry, with their focus on large volumes of repetitive and monotonous tasks that do not require decision-making. By automating data capture and integrating workflows to identify customers, agents can access supporting details on one screen and avoid the need to tap into multiple systems to gather contextual information. The promise of shorter call durations and an improved experience for customers and agents alike. Both RPA and cognitive automation allow businesses to be smarter and more efficient. It takes unstructured data and builds relationships to create tags, annotations, and other metadata. It seeks to find similarities between items that pertain to specific business processes such as purchase order numbers, invoices, shipping addresses, liabilities, and assets.

Besides conventional yet effective approaches to use case identification, some cognitive automation opportunities can be explored in novel ways. Upgrading RPA in banking and financial services with cognitive technologies presents a huge opportunity to achieve the same outcomes more quickly, accurately, and at a lower cost. Explore our enterprise software products, open source solutions and accelerators on EPAM SolutionsHub.

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Agents no longer have to access multiple systems to get all of the information they need resulting in shorter calls and improve customer experience. With RPA, structured data is used to perform monotonous human tasks more accurately and precisely. Any task that is real base and does not metadialog.com require cognitive thinking or analytical skills can be handled with RPA. Generally speaking, RPA can be applied to 60% of a business’s activities. In banking and finance, RPA can be used for a wide range of processes such as Branch activities, underwriting and loan processing, and more.

What’s important, rule-based RPA helps with process standardization, which is often critical to the integration of AI in the workplace and in the corporate workflow. For example, cognitive automation can be used to autonomously monitor transactions. While many companies already use rule-based RPA tools for AML transaction monitoring, it’s typically limited to flagging only known scenarios. Such systems require continuous fine-tuning and updates and fall short of connecting the dots between any previously unknown combination of factors. Upon claim submission, a bot can pull all the relevant information from medical records, police reports, ID documents, while also being able to analyze the extracted information. Then, the bot can automatically classify claims, issue payments, or route them to a human employee for further analysis.

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Predictive analytics can enable a robot to make judgment calls based on the situations that present themselves. Finally, a cognitive ability called machine learning can enable the system to learn, expand capabilities, and continually improve certain aspects of its functionality on its own. Let’s consider some of the ways that cognitive automation can make RPA even better.

The solution, once deployed helps keep a track of the health of all the machinery and the inventory as well. FutureCFO.net is about empowering the CFO and the Finance Team to take on the leadership position in the digitalization of the enterprise. State-of-the-art technology infrastructure for end-to-end marketing services improved customer satisfaction score by 25% at a semiconductor chip manufacturing company. You should expect AI to make its way into every industry, every product, every process. But do keep in mind that AI is not a free lunch — it’s not going to be a source of infinite wealth and power, as some people have been claiming. It can yield transformational change (like driverless cars) and dramatically disrupt countess domains (search, design, retail, biotech, etc.) but such change is the result of hard work, with outcomes proportionate to the underlying investment.

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Digitised processes not only run at significantly increased speeds, but can also operate at significant scale, error-free. Cognitive automation is a blending of machine intelligence with automation processes on all levels of corporate performance. IBM’s cognitive Automation Platform is a Cloud based PaaS solution that enables Cognitive conversation with application users or automated alerts to understand a problem and get it resolved.

Finally, the world’s future is painted with macro challenges from supply chain disruption and inflation to a looming recession. With cognitive automation, organizations of all types can rapidly scale their automation capabilities and layer automation on top of already automated processes, so they can thrive in a new economy. Having emerged about 20 years ago, RPA is a cost-effective solution for businesses wanting to pursue innovation without having to pay heavily to test new ideas. It can also be implemented more quickly than traditional automation systems, freeing up time for employees to tackle an increased number of cognitive and complex tasks. Its ability to address tedious jobs for long durations helps increase staff productivity, reduce costs and lessen employer attrition.

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It helps banks compete more effectively by reducing costs, increasing productivity, and accelerating back-office processing. New insights could be revealed thanks to cognitive computing’s capacity to take in various data properties and grasp, analyze, and learn from them. These prospective answers could be essential in various fields, particularly life science and healthcare, which desperately need quick, radical innovation. These are some of the best cognitive automation examples and use cases. However, if you are impressed by them and implement them in your business, first, you should know the differences between cognitive automation and RPA. One of the most important parts of a business is the customer experience.

Currently there is some confusion about what RPA is and how it differs from cognitive automation. This Automation Anywhere eBook offers 6 proven steps to boost your chances of successfully  deploying cognitive automation. Until now the “What” and “How” parts of the RPA and Cognitive Automation are described. Now let’s understand the “Why” part of RPA as well as Cognitive Automation.

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In fact, they represent the two ends of the intelligent automation continuum. At the basic end of the continuum, RPA refers to software that can be easily programmed to perform basic tasks across applications, to helping eliminate mundane, repetitive tasks performed by humans. At the other end of the continuum, cognitive automation mimics human thought and action to manage and analyze large volumes with far greater speed, accuracy and consistency than even humans. It brings intelligence to information-intensive processes by leveraging different algorithms and technological approaches. Based on the Type, The market is bifurcated into Robotic Process Automation and Intelligent Automation. Robotic process automation technologies influence market development by reducing the amount of repeatable human interactions with business applications.

• Digitised processes not only run at significantly increased speeds, but can also operate at significant scale, error-free.
• There are a number of advantages to cognitive automation over other types of AI.
• Since it has proven effects on saving time and effort, all while cutting down costs, it is expected that healthcare RPA will become a staple in the healthcare industry.
• Additionally, these models have the ability to continually learn and improve through ongoing training with new data, making them even more effective over time.
• “One of the biggest challenges for organizations that have embarked on automation initiatives and want to expand their automation and digitalization footprint is knowing what their processes are,” Kohli said.
• It improves the care cycle tremendously and streamlines much of the time-consuming research work.

We help brands reach new levels of operational efficiency and improve business process quality by bringing intelligence to information-intensive tasks. Softtek’s intelligent automation services allow organizations to streamline business workflows beyond the scope of traditional automation technologies. With the help of deep learning, digital image processing, cognitive computer vision, and traditional computer vision, Cognitive Mill™ is able to analyze any media content.

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Alternatively, Cognitive Automation uses artificial intelligence (AI) and machine learning to mimic human thought and actions to help solve more complex problems and gain key insights from data. In the insurance industry, cognitive automation has multiple application areas. It can be used to service policies with data mining and NLP techniques to extract policy data and impacts of policy changes to make automated decisions regarding policy changes. It can also be used in claims processing to make automated decisions about claims based on policy and claim data while notifying payment systems.

Even though there has been a dramatic increase in digitization, we still use a lot of paper, particularly in heavily regulated industries such as banking or healthcare. OCR is the mechanical or electronic conversion of images of typed or handwritten or printed text into machine-encoded text whether from a scanned document, or a photo of a document. It is widely used as a form of data entry from printed paper data records including invoices, bank statements, business cards, and other forms of documentation.