Insurance Industry – Can You Bring Your Own Bot?
Imagine a scenario when a straightforward insurance claim is processed in two minutes instead of two days. To the astonishment of many, we can complete both settlement and closure of the claim within 20 seconds. This is one of the use cases of robotic process automation (RPA) across the insurance value chain. While looking for automation possibilities for the key insurance business processes, mostly the repeatable, the cynosure of the insurance industry is shifting toward macro and micro components of the value chain.
Use of RPA in the Insurance Industry
Grandview Research (October 2016), the US marketing and consulting firm, reported that the RPA market is anticipated to exceed $8 billion by 2024, a significant increase from $125 million in 2015.
According to a McKinsey official, RPA has the potential to offer 30% to 200% of ROI in the first year. In a panel discussion held by the same company on “Automation at Scale is Driving Transformative Change Across Insurance,” the following were the key observations:
- Analysis of call-center data shows that employees spend 30% to 40% of their time in documenting transactions
- In the back office, 20% to 30% of the time is related to documentation
Top industry estimates suggest that a personalized bot can potentially reduce up to 80% of the manual efforts involved in documentation.
The Organic Evolution of RPA – A View from the Business Processes Perspective
RPA market is anticipated to exceed $8 billion by 2024, a significant increase from $125 million in 2015.
Initially, robotic process automation in insurance used screen-scraping processes and is now moving toward end-to-end automation.
RPA brings about BOM (Business Operating Model) Disruption in Insurance
The insurance operating model has already shifted from acquiring customers to products. Now, with RPA, there is an increased focus on understanding and scrutinizing the needs and risks of the customers at a more granular level for customizable and personalized products.
And while RPA is an initiative towards saving costs , it also leads to more productive reallocation of FTEs and minimizes errors.
Areas where RPA can be implemented
This is the process selection criteria for RPA:
- Tasks that have a fixed process flow or are based on fixed rules
- Tasks where multiple system access is required to complete tasks
- Error-prone tasks or tasks that lead to more reworks
- Programmed or fixed pattern inputs with less or no human intervention
The Power of an Insurance Chatbot
When a personalized bot is envisaged for the insurance industry, the most prevalent thought among the industry experts and users is that insurance chatbots can substitute human service. This, however, is not entirely correct. Chatbots are perpetually there to enrich human service and not replace it.
The Acumen of an Insurance Bot – Market Anticipation
Attributes an insurance chatbot should contain:
- Conversational maturity
- To understand the context of the conversation right at the inception of the chat with the help of in-built intelligence
- Accuracy in first-time response to evade frustration among the end users which wrong response may give rise to
- Asking questions if the conversation is not clear with a polite and pleasant attitude by defining a personality for the chatbot
- Retaining the context from previous conversations wherever applicable with the help of domain knowledge and intelligence
- Retaining input from the previous conversations wherever applicable with the help of conversational intelligence
- Omnichannel capabilities
- Converses seamlessly across channels and retains data and context for a seamless experience. Say, for instance, a conversation took place halfway in one channel (chatbot on the company’s website) and then continued in another channel from the point where it is left previously (say, on Facebook Messenger)
- Emotionally Intelligent
- Understands the sentiment of the user and delivers personalized experiences through friendly greetings or polite messages when the user is dissatisfied
- Escalate to a live agent wherever necessary instead of truncating the chat with an “I cannot help” response
- Capability to process both structured and unstructured data
- Capability to produce unstructured data as a response to a query which could be text responses or a document download
- Capability to upload data in an unstructured format
- Autonomous reasoning or self-learning
- Ability to infer solutions based on case histories by deploying self-learning techniques
- Domain knowledge
- Trained for brand or industry-specific knowledge, concepts, or terms
- Preconfigured to resolve common or repeated customer requests of specific industries (for instance, insurance)
Most of the insurance transactions in the personal line of businesses are automated with chatbots which make them more secure. But, on the contrary, insurance transactions under commercial lines of businesses are very nominally converted to insurance chatbots. Apart from enquiries and help-related questions, the other transactions are yet to be fully automated. With the help of machine learning and artificial intelligence, very soon the commercial insurance transactions and conversations will be defined under various patterns (with the help of historical data analysis). Chatbots can then complement those transactions which will benefit both the insurers as well as the insured.