Richard Stewart: Why cognitive automation matters to the insurance sector

cognitive automation examples

In the evolving technological landscape, a fascinating convergence between Automation and Artificial Intelligence (AI) emerges, giving birth to a potent synergy known as Intelligent Automation. This integration leverages the strengths of both technologies, resulting in solutions that are more capable, efficient, and adaptive than ever before. Those statements define whether your business will still exist in the next five years or you will be swallowed by organisations that took steps toward innovative technologies. Individuals are likely to expect that decisions produced about them do not treat them in terms of demographic probabilities and statistics. You should therefore apply inferences that are drawn from a model’s results to the particular circumstances of the decision recipient.

“There is very little doubt that the AI-cognitive space is real,” says Ranjit Bawa, cloud and infrastructure lead and principal at Deloitte Consulting. “We are helping many of our clients use the opportunity to truly transform their business operating model versus going after point solutions that automate a sub-optimal process. Cognitive platforms such as Amelia can be powerful in helping articulate the ‘art of the possible’,” he says. Automation, while efficient in rule-based tasks, can be rigid and prone to errors when faced with deviations from predefined rules.

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Before you invest in a new solution, map out what success looks like for your company, including benchmarking metrics to compare with future results. So, by implementing both AI and automation technologies together, you could ensure your organisation is working efficiently and effectively, with little room for error. A chatbot is a computer program that mimics conversations with users applying AI. Chatbots were initially limited to conversations about a specific topic but they are growing and diversifying with advanced functionalities. In the coming examples, we will see an application in relation to retail clients, however it is easily adaptable for professional clients or eligible counterparties.

RPA is particularly good at handling high transaction volumes, processes with high volume fluctuation (peaks/troughs), and it can support service offerings improvement through being available 24×7. RPA has the potential to reduce resource processing costs by more than 80%, accelerate average handling time (AHT) by more than 90%, and reduce error rates to less than 0.1%. It can therefore free up talent to work on more complex judgement based higher value tasks. There’s no need for you or your team to enroll in robotic process automation courses or even take a hands-on approach to integrating RPA software into your systems.

Global challenges

The potential for value creation is perhaps the largest across industries and use cases. The technology

can help lower costs through efficiencies generated by automation at scale, lower errors rates and improved resource utilization. Additionally, it can uncover new and unrealized opportunities based on an enhanced ability to process and generate insights from

vast amounts of data.

cognitive automation examples

Pharmaceutical companies mine patient data to evaluate the effectiveness of treatments and identify opportunities. Energy & Utility companies use CRM analytics to segment customers for marketing campaigns and equip call centre workers with up-to-date information about callers. They also use it to determine why they are losing customers in a particular region. Manufacturing organisations use it to determine how much to charge for a particular item, or where a new plant should be located. And if you couple analytics with RPA and cognitive processing, the then operational power becomes enormous.

Intelligent Automation Use Cases

Moreover, the client may have some difficulties in understanding some questions, and this can lead to errors in the evaluation of the risk profile. Intelligent automation can drive a customer service chatbot that understands the intent of text or voice questions and offers options. Another example might be a shipping or manufacturing process that uses computer vision to accurately identify objects and help workers make quick decisions on the fly. The hype around cognitive automation can unfortunately often inspire too much initial confidence and scope in some organisations’ RPA pilot projects.

cognitive automation examples

Generative AI mitigates these risks by narrowing the skills gap, unleashing economic potential by adding “human-like” capabilities previously not available through automation. It can generate in milliseconds a brief summary of a document, saving many minutes of human time otherwise required to read, understand, and distil the document content. RPA is rule-based algorithms which can be used to capture, process and interpret streams of data, trigger appropriate responses and communicate with other processes. Robots work with existing applications and systems that an organisation has, which enable fast-tracking to digital transformation.

Intelligent automation encompasses more than just robotic process automation (RPA). RPA is a type of automation that uses software robots to mimic human actions and automate repetitive tasks. Intelligent automation not only automates repetitive tasks but also assists humans in making better decisions by providing insights, recommendations, and predictions based on the analysis of large data sets. As enterprises progress in their automation journeys,  technologies like RPA are now enhancing  themselves with the potential of Artificial Intelligence, giving rise to what is known as Intelligent Automation. By combining automation solutions with AI technologies,

financial services companies can move from automating specific tasks to automating end-to-end processes with embedded intelligence. Intelligent automation (IA) combines artificial intelligence (AI), machine learning (ML), natural language processing (NLP),

and process automation to optimize business outcomes.

Robotic process automation – known as RPA – uses software robots to automate simple, manual and repetitive tasks within a business process. It works by integrating with existing business applications via application programme interfaces (API) or user interfaces (UI) and following structured rule-based scripts that mimic the work behaviour of humans. Both cognitive automation and robotic process automation (RPA) are types of process automation whereby technology is used to carry out tasks normally done by humans. Cognitive automation in context

Firstly, there needs to be a defined process that needs automating. The main tools involved in intelligent automation are business process automation software, operational data, and AI services.

RPA can be a pillar of efforts to digitise businesses and to tap into the power of cognitive technologies. DA is the process of examining data sets in order to draw conclusions about the information they contain with the aid of specialised systems and software. However DA applications involve more than just analysing data, and usually involve a multi departmental team effort. Much of the required work takes place upfront, in collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results.

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Human intervention is reduced by predetermining decision criteria, subprocess relationships, and related actions – and embodying those predeterminations in software or machines. AI and cognitive technologies have a vital role to cognitive automation examples play in this critical and urgent activity. They can help us to optimise power generation and distribution, guide the design of smart homes, and create advanced automated systems for navigating our complex urban environments.

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Gina Shaw- Business Development Manager for Digital Solutions at Acuvate with over 15+ years of experience in the industry. She has helped organizations across the globe in modernizing their workplace with top-notch technologies and empowered them with world-class digital experiences by optimizing their existing systems. With her expertise in business management, she has assisted many global companies to advance towards their vision of being more productive and having a digital-ready environment. At Acuvate, we help clients reduce costs, optimize turnaround time, and enable the workforce to focus on tasks that add value with a wide range of RPA and Chatbot solutions and services.

  • As the role of AI expands, human-AI collaboration becomes imperative to harness the strengths of both entities.
  • In this context, chatbots can be used to improve experience for specific segments such as self-directed and digitally savvy customers.
  • However, creating Cognitive Digital Twins is a complex task that requires significant data processing capabilities and a deep understanding of the various data sources and their interconnections.
  • RPA enables chatbots to retrieve information from these systems and handle more complex and real-time customer/employee requests and queries at a large scale.
  • If you’ve ever kicked yourself for not jumping on a technology trend, this is not one to ignore.
  • The algorithms receive an input value and predict an output, using certain statistical methods.

Intelligent automation incorporates a combination of powerful technologies, namely, artificial intelligence, machine learning, robotic process automation and natural language processing. Together its advanced algorithms, cognitive functions and automation capabilities streamline business processes and create workflows that can think, adapt and learn by themselves. RPA is best suited to automate high volume, repetitive, rules-based financial process tasks, such as general accounting operations that are governed by business logic and structured inputs. Intelligent automation can then sweep up more complex decisions that are out of reach for RPA. Intelligent automation is better suited to more high value, transactional decisions, that typically rely on a human to make a decision, such as the screening of R&D tax claim judgements or payment sanctions. It can be common across departments, and even functions within departments, to operate using disparate technology systems.

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US bank PNC Financial uses the system to automate approvals for certain loan types. The bank combines prescriptive business rules with predictive data modelling to ascertain  customer eligibility for credit. Processes such as customer onboarding and KYC, mortgages, loan application processing tend to have a large volume of documents, replete with complexity and variety. The current global slowdown, with an already

existing remote workforce model propelled by the pandemic, are further necessitating a stronger IDP push.

What are three examples of automation?

Common examples include household thermostats controlling boilers, the earliest automatic telephone switchboards, electronic navigation systems, or the most advanced algorithms behind self-driving cars.

What exactly can be automated within your finance function depends on the nature of the task. McKinsey has illustrated (see below) that up to 42% of financial processes can be fully automated, with about a third of the opportunity for automation captured using task automation technologies such as robotic process automation (RPA). The remainder requires cognitive automation technologies, such as intelligent automation (IA).

As a Microsoft Gold Partner, we help clients implement the robotic process automation (RPA) capability in Power Automate to connect old and new systems and reduce repetitive tasks using UI-based automation with desktop flows. From advanced computer technology, to smartphones, to hotel software – our machines carry out cognitive tasks such as data processing, and even conversation. Smart workflow includes a process-management software tool that integrates tasks performed by groups of humans and machines. This workflow helps users to initiate and track the status of an end-to-end process in real time. Intelligent Automation arises from the marriage of Automation’s precision and consistency with AI’s learning and cognitive capabilities. This synergy amplifies automation’s potential by enabling it to adapt to new situations, learn from data, and make context-based decisions, effectively extending automation’s reach with a layer of intelligence.

cognitive automation examples

Automation spans from basic rule-based actions to complex operations driven by advanced algorithms. In our swiftly evolving digital realm, Automated Intelligence, often referred to as Automation, emerges as a transformative force. It relies on technology to execute tasks with minimal human intervention, streamlining operations and enhancing efficiency. Careers have been made off delivering a fraction of the value that is readily accessible with Superhuman AI Automation and it doesn’t require large investment to prove out.

Is cognitive science related to AI?

Cognitive science has been using artificial intelligence to decode the human mind since the 1950s. Moreover, with recent advancements in AI, deep learning approaches are used in applications such as gaming, object recognition, language translation, and other allied areas.