Transforming Financial Services with Robotics and Cognitive Automation Deloitte US

Finally, companies have to break existing silos among people and processes to realize the full benefit of the technology. As the market for Robotics & Cognitive Automation is heavily growing, there is a large number of software providers with different specializations and product strategies. While some of the vendors are pure RPA or cognitive automation players, others are building on their experience in one of the markets and strive to differentiate their business. Choosing the right selection of tools that match one’s business and automation strategy as well as process and IT landscape is therefore not an easy undertaking.

New jobs with completely renewed job descriptions will be created as a result of this large-scale transformation. To gain the most, organizations will need to strike a balance between transitioning to robotic process automation and cognitive technologies in insurance and making required FTE adjustments and up-skilling their existing workforce. The value of intelligent automation in the world today, across industries, is unmistakable. With the automation of repetitive tasks through IA, businesses can reduce their costs as well as establish more consistency within their workflows. The COVID-19 pandemic has only expedited digital transformation efforts, fueling more investment within infrastructure to support automation. Individuals low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks.

How should the industry approach an R&CA-driven transformation across its value chain?

While Cognitive Automation and RPA are both parts of the same automation spectrum, they have distinct differences. The best way to choose the right automation tool or an ideal combination can be done efficiently through partnering with an experienced automation supplier like Electroneek. Batch operation is handling transactions in a batch or group, often used for end-of-cycle processing. It is an inherent part of the finance sector for processing bank reports, whether generated at the end of the day, monthly or bi-weekly. “Cognitive automation multiplies the value delivered by traditional automation, with little additional, and perhaps in some cases, a lower, cost,” said Jerry Cuomo, IBM fellow, vice president and CTO at IBM Automation. Without sufficient scale, it is difficult for the benefits from R&CA to justify the effort and investment.

With any new technology, it is important to alter administrative processes to take full advantage of the digital tools. One of the key determinants of institutional change is making sure administrative structures are in alignment with technology innovation. If digital tools do not correspond to agency missions, they are not likely to generate positive results.

What are the uses of cognitive automation?

However, due to its impact on IT infrastructure, security, business continuity, and disaster recovery, it must comply with IT policies and requires IT infrastructure support in order to scale. In the real estate industry, IA provides the first line of response to interested buyers. Bots use intelligent automation to provide faster, more consistent responses and engage buyers before involving a representative. Bots are also used to value properties by comparing similar homes and create an average of sales to prescribe the optimal selling price.

AI combines cognitive automation, machine learning (ML), natural language processing (NLP), reasoning, hypothesis generation and analysis. In order for RPA tools in the marketplace to remain competitive, they will need to move beyond task automation and expand their offerings to include intelligent automation (IA). This type of automation expands on RPA functionality by incorporating sub-disciplines of artificial intelligence, like machine learning, natural language processing, and computer vision. Bots can automate routine tasks and eliminate inefficiency, but what about higher-order work requiring judgment and perception? Developers are incorporating cognitive technologies, including machine learning and speech recognition, into robotic process automation—and giving bots new power.

Cognitive automation is a type of artificial intelligence that utilizes image recognition, pattern recognition, natural language processing, and cognitive reasoning to mimic the human mind. While there are clear benefits of cognitive automation, it is not easy to do right, Taulli said. Then, as the organization gets more comfortable with this type of technology, it can extend to customer-facing scenarios. Instead of having to deal with back-end issues handled by RPA and intelligent automation, IT can focus on tasks that require more critical thinking, including the complexities involved with remote work or scaling their enterprises as their company grows. Along with assessing processes and tasks, RE companies would need to evaluate the technology implementation approach that they wish to pursue.

CIOs will derive the most transformation value by maintaining appropriate governance control with a faster pace of automation. “Cognitive automation by its very nature is closely intertwined with process execution, and as these processes consistently evolve and change, the IT function will have to shift from a ‘build and maintain’ model to a ‘dynamic provisioning’ model,” Matcher said. These areas include data and systems architecture, infrastructure accessibility and operational connectivity to the business. HFES is a not-for-profit organization that provides education, builds connections, and advocates on behalf of the human factors/ergonomics field with chapters worldwide. The University of Michigan HFES Student Chapter is organized to serve the needs of the human factors profession at the University of Michigan. The HFES Cognitive Engineering and Decision Making Technical Group encourages research on human cognition and decision-making and the application of this knowledge to the design of systems and training programs.

Impacts to the insurance operating model: Technology

It’s also important to plan for the new types of failure modes of cognitive analytics applications. “Cognitive automation can be the differentiator and value-add CIOs need to meet and even exceed heightened expectations in today’s enterprise environment,” said Ali Siddiqui, chief product officer at BMC. “As automation becomes even more intelligent and sophisticated, the pace and complexity of automation deployments will accelerate,” predicted Prince Kohli, CTO at Automation Anywhere, a leading RPA vendor. Second, RE owners should acknowledge that the application of R&CA technology will enable use of information and analysis across different functions, requiring more collaboration among a variety of stakeholders. Microsoft provides support to The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative. The findings, interpretations, and conclusions in this report are not influenced by any donation.

• It trains algorithms using data so that the software can perform tasks in a quicker, more efficient way.
• RPA can also be used to anticipate inventory using data analytics to evaluate existing inventory usage rates and collate that information to generate a recommendation.
• IBM Cloud Pak® for Automation provide a complete and modular set of AI-powered automation capabilities to tackle both common and complex operational challenges.
• The automation footprint could scale up with improvements in cognitive automation components.
• “Cognitive automation by its very nature is closely intertwined with process execution, and as these processes consistently evolve and change, the IT function will have to shift from a ‘build and maintain’ model to a ‘dynamic provisioning’ model,” Matcher said.
• Yet all too often, firms find themselves stuck in experimental mode—held back by resource and knowledge limitations, or overwhelmed by the complexity of technologies and processes.

Therefore, providing a better customer experience helps in maintaining a good reputation. It is known to be a tool that automates routine tasks usually performed by the company staff. By understanding the two main options better, we can dive deeper into realizing which automation process is suited to different businesses.

Use cases: Using IA to solve real-world challenges

​Progressive HR teams are already applying robotic process automation (RPA) to help tasks like data management and validation; running, formatting, and distributing reports; and replacing manual and spreadsheet-based tasks. Some are also exploring more advanced cognitive automation technologies, like machine learning and natural language processing, to enhance a range of HR processes from talent acquisition to benefits administration and beyond. The growing RPA market is likely to increase the pace at which cognitive automation takes hold, as enterprises expand their robotics activity from RPA to complementary cognitive technologies. Furthermore, many of these algorithms and focused solutions are being embedded into new versions of existing enterprise systems. This means your decision to choose one technology over another today can hinge on variables other than the algorithm’s or solution’s ability to do its intended job.

As per the McKinsey Global Institute, this automation technique is one of twelve disruptive technologies set to significantly influence our future, standing alongside innovations like autonomous vehicles and renewable energy. RPA effortlessly integrates into any enterprise architecture, negating the need for process or technology re-engineering, by introducing tireless virtual workers that add value to your organization. However, it’s worth noting that RPA is largely dependent on structured digital data governed by predictable business rules. This is where cognitive automation steps in, enhancing RPA with abilities that mimic human intelligence, such as data mining, pattern matching, natural language processing, machine learning, and machine reasoning.

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