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Why Intelligent Automation should be a top priority for enterprises in 2022



By Jason Cripps, former Global Head of Intelligent Automation at Zurich Insurance, explores the market shift to Intelligent Automation and the impact it will have on businesses in 2022 and beyond.

Over the last 10 years, Robotic Process Automation (RPA) has catalysed many businesses’ digital transformation journey. Replacing manual rule-based processes and activities with bots frees up employees’ time to focus on activities which require human intervention or high-level decision making.

However, RPA was never able to truly extend to the full, end-to-end, customer journey, but instead focused more on the simple high-volume processes in the back office or low-volume assisted automations, desk-side. 

This is because, in hindsight, RPA technology was too basic to conduct automation for more sophisticated and complex tasks. But, in recent years, Intelligent Automation – the next natural evolutionary form of RPA – has emerged and can be deployed across an enterprise at scale. It combines RPA with natural language processing (NLP), optical character recognition (OCR), and machine learning (ML) to deal with complex, unstructured inputs, which many real-world processes require. But Intelligent Automation does not require deep understanding of the technology or the mechanics behind it, nor does it need qualified data scientists or specially trained IT professionals to use it.

As a result, Intelligent Automation is the top priority for enterprises in 2022, with the events of the past two years exponentially driving adoption. A recent report revealed that the intelligent process automation market is expected to reach $13.75 billion by 2023, nearly double what it was in 2018.  The Life Insurance market is no exception to this trend as the ability to apply these tools across the full customer journey can deliver improved outcomes for customers, intermediaries, and the insurers. 

The benefits of automating mundane, non-value-added tasks are clear. Automation enriches employee activity, optimises legacy tech, and improves customer experiences, whilst enabling businesses to scale without linearly increasing cost. To name a specific case example of proven Return on Investment, one of India’s largest medical groups, operating 14 hospitals with more than 2,300 healthcare professionals, has used Laiye’s IA technology to reduce turnaround time for claims by up to 75%, bring the error rate down to 0%, and save $10 million. Other companies have seen similar success using the Laiye IA platform to automate processes across industries.

Within highly regulated environments, Intelligent Automation provides improved processing speed and accuracy of Fraud Detection, KYC (Know Your Client), ID and Verification of clients. By using tools like Image Detection, Artificial Intelligence, and Machine Learning to capture and access data available from various sources, the paper chase of documents for intermediaries should reduce with copies (scans / photos) being digitally sourced and verified.  

Prior to Covid-19, many industries traditionally resisted the move to complete digitisation, relying heavily on paper and face-to-face contact. Furthermore, business continuity plans focused on the recovery of physical locations, such as buildings and people, in the event of a disaster more so than continuity of the customer experience.

However, Covid-19 has changed industries, work patterns, and processes forever. Practically overnight, people and businesses realised the need to work effectively from home or from remote locations. Businesses needed to accelerate digitisation as paper-based processes and in-person meetings were simply not an option under social distancing guidelines and intermittent lockdowns.  This digitisation transformation brings about higher business resilience as a secondary outcome.

Furthermore, digitisation supports the use of Intelligent Automation to extract relevant data from insurance documents and systems to be fed into an analytics engine. This can identify areas of potential risk as well as enable auto reporting of the various reports required by the regulator in these changing times. 

The transition from RPA to IA

With this significant change in the landscape and the need to be able to service customers and businesses in different, but still remote ways, it became apparent that RPA alone was insufficient to truly transform business processes or deliver greater business resilience across the enterprise.

Great customer service for Life Insurance policies is critical given the length and nature of the various assurances provided. Life Insurance policies change over time, good examples being the sum assured, change of address, length of cover, partial surrender or switching of underlying investments. 

Intelligent process automation can automate many routine insurance policy management tasks, such as with models that pull change of address requests from emails and transcripts of voice calls – and immediately start processes to complete the request. Intelligent models can also automate tasks such as processing of loss run reports, analyzing statement of value reports, and providing surrender values or event transfer values on pensions.  

With Conversational AI allowing for 24/7 availability, customer service can handle more and more complex customer (or intermediary) requests. Conversational AI can interact, understand, verify identity, and execute processes as humans would with speed and high accuracy.

Intelligent tools, however, truly enhance the capability of pure RPA to realise sustainable higher value outcomes and benefits across the enterprise. An effective Intelligent Automation platform adapts and grows to meet the continuing challenges imposed by changing customer expectations, technologies, and the competitive landscape.

Automated life insurance underwriting is a great use case for Intelligent Automation, given the many and varied types of documents involved. To assess the potential risk, data must be collected on health risks, client identity, net worth, and creditworthiness and much more. Collecting all this information and pulling out appropriate data is time-consuming and error prone. Using AI for life insurance underwriting can dramatically speed up functions including document assessment and data extraction, assessment of loss runs, and review of the customer’s claim history – all important factors in an underwriting decision.

Intelligent Automation tools that can handle more complex business processes were available before Covid-19, but the acceleration and investment in these tools and capabilities over the last few years has been exponential.

Intelligent Automation technology also provides the framework to build solutions that solve specific business problems and deliver a better employee and customer experience beyond pure RPA. For example, an Intelligent Automation platform should seamlessly combine RPA, OCR (Optical Character Recognition), NLP (Natural Language Processing) and NLU (Natural Language Understanding) for a more powerful capability with higher accuracy, due to its ability to “fill in the blanks” with information from multiple perspectives of continued learning. 

There are many different providers of the components of Intelligent Automation. Laiye, in particular, integrates Conversational AI, Intelligent OCR, Process Mining, Machine Learning, Natural Language Processing, Natural Language Understanding, and Cognitive Services within a single scalable platform.

Life Insurance companies are highly regulated and subject to a constantly changing regulatory landscape and the failure to comply may mean fines as well as operational and reputational damage. Intelligent Automation can help companies stay in compliance by automating many routine tasks, dramatically reducing human error, and provide a full log of all actions. Automation can likewise help with generating regulatory reports and compliance checking processes.

How IA can be deployed to enhance data & document processing

Many organisations have moved to digital mailrooms with all paper mail scanned and routed digitally on arrival; everything is delivered to a central location, scanned, and put into a workflow, with links emailed to the appropriate teams. 

Apply Natural Language Processing (NLP) and Natural Language Understanding (NLU) to that process, and you can start to automatically triage and respond to documents. If you have a medical or legal file that might be hundreds of pages long, a good NLP/NLU engine can extract and highlight the relevant information before securely passing it on to an assessor.

The machine doesn’t make the decision, but it does a ‘pre-read’, which is a huge help to the assessor (who still has access to the full file), who can then spend more time where the real value is, in assessing the information provided. It removes the low value administrative work, and the NLP is continually learning and updating, based on changes made by the assessor.

Consider the application of this within Death Claims or Disability Claims. The ability to process death claims without the need for lengthy claims forms or documents from customers is a reality. The use of data and links through APIs to the death register enables a straight through light touch simple process that delivers the promise to customers in an empathetic and quick way at the most needed time in our customers’ lives. For disability claims, the ingestion, understanding and assessment of medical information, reports, or interactions with the customer (either direct or through Conversational AI) can improve the claims handling experience for all involved. Furthermore, the data made available enables the correlation between underwriting decisions and claims outcomes to further inform and improve the risk assessment and acceptance of future risks.  

Businesses can’t ignore the advancements in Intelligent Automation as its uptake by competitors drives higher expectations from customers. There are many opportunities and challenges ahead for businesses who truly want to transform the future of work for their enterprise.

Addressing the skills gap

The greatest challenge possibly facing businesses right now is the skills gap with regard to digital jobs.  Recent estimates forecast that the UK will need three million jobs requiring digital skills by 2025. However, 52% of the UK workforce still does not possess such skills according to a Lloyds Bank report

This creates a significant gap in our workforce of the future – addressing this issue by identifying new skills, and training existing workforces on how to employ them is critical for business success and business resilience.

Underpinning IA is a layer of new skills and roles that are emerging, and teams should look to reskill so that they can move into slightly different roles that will enable them to work alongside technology. This, in turn, will help them make better decisions and focus on higher-value tasks and do what humans excel at: building relationships and making decisions based not just on data but on empathy and compassion.

Alongside this, realising the power of IA to provide attended and augmented support to employees drives greater satisfaction outcomes for customers as well as employees.

This is true for Insurers as well as Intermediaries as Intelligent Automation should be considered throughout the full value chain.    

Intelligent Automation must therefore be a top priority for businesses who urgently need to reduce the volume of repetitive tasks that prevent them from improving vital services and delivering truly differentiated customer experiences.

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