An insurance claim has always been a testing process for customers because it can take two days or more to fulfill them. But today, it will be a shock for many to find out that both settlement and closure of claims can happen within 20 seconds. This positive development in the insurance value chain is just one of the many uses of the robotic process automation (RPA).
While automation possibilities are plenty to further improve key insurance business processes, particularly those that are repetitive in nature, the macro and micro components of the value chain have become the focus of attention in the insurance industry.
Use of RPA in the Insurance Industry
The worth of the RPA market is forecasted to exceed $8 billion by $2024 based on the published report of U.S. marketing and consulting firm Grandview Research, a dated October 2016. The figure is a significant increase from $125 million in 2015.
In addition, a McKinsey official said that in terms of ROI, the RPA has the potential to offer 30% to 200% just in the first year. The same company held a conference on the “Automation at Scale is Driving Transformative Change Across Insurance.” During the panel discussion, the following key observations were raised:
- After completing the analysis of call-center data, it was revealed that 30% to 40% of employee time is spent on documenting transactions
- About 20% to 30% of the time, activities in the back office are all documentation-related.
Based on Top industry estimates, it is suggested that an 80% reduction of the manual efforts in documentation is possible with the use of a personalized bot.
The Organic Evolution of RPA – A View from the Business Processes Perspective
At first, the robotic process automation in insurance utilized screen-scraping processes. However, the move now is toward end-to-end automation.
RPA brings about BOM (Business Operating Model) Disruption in Insurance
A salient feature of the new insurance operating model is the shift from customer acquisition to product acquisition. But with the RPA, there is a greater focus on understanding and analyzing the needs of and risks to customers in a more detailed manner in order to produce customizable and personalized products.
And while RPA is basically a cost-saving initiative, it also allows for balanced FTE reallocation to improve productivity while minimizing errors at the same time.
Areas where RPA can be Implemented
Hereunder are the process selection criteria for RPA:
- Tasks that have a fixed or established process flows or are fixed-rules based
- Tasks where completion is anchored on multiple system access
- Error-prone tasks or tasks that are disposed to more reworks
- Programmed or fixed pattern inputs with minimal human intervention or none at all
The Power of an Insurance Chatbot
There is a misconception among industry experts and users that the presence of chatbots in the industry is likely to substitute human service. However, that notion is only partly true. The vision is actually to use chatbots and make them perpetually available to assist human personnel to improve customer service rendition and not replace them.
The Acumen of an Insurance Bot – Market Anticipation
Every insurance chatbot should possess the following attributes:
- Conversational maturity
- Capable of understanding the context of the conversation right at the onset of the chat with the help of in-built intelligence
- Accuracy in first-time response since any wrong response could lead to end-users’ frustration which should always be avoided
- Asking questions to clarify points in a conversation that are unclear in a polite manner and with a pleasant attitude that typifies a chatbot’s personality
- Ability to retain contexts from previous conversations wherever applicable with the help of domain knowledge and intelligence
- Equal ability in retaining inputs from previous conversations wherever applicable with the help of conversational intelligence
- Omnichannel capabilities
- An impeccable conversation prowess across channels while having the capability to retain data and context for that seamless experience. As an example, a conversation took place halfway in one channel (chatbot on the company’s website) and then continued in another channel from the point where a conversation previously transpired like on Facebook Messenger.
- Emotionally Intelligent
- Understands and empathizes with the user and rendering personalized experiences through friendly greetings or polite messages whenever the user is discontented
- Escalate matters to a live agent wherever necessary instead of retorting with an “I cannot help” response
- Capability to process both structured and unstructured data
- Capable of producing unstructured data in response to a query which could either be text message responses or documents download
- Capable of uploading data in an unstructured format
- Autonomous reasoning or self-learning
- Able to infer solutions based on case histories by deploying self-learning techniques
- Domain knowledge
- Well-trained for a brand or industry-specific knowledge, concepts or terms
- Preconfigured to resolve common or repeated customer requests of specific industries (insurance i.e.) consistently
The advantage of automating the personal line of businesses and using chatbots is that insurance transactions are more secure. But regarding insurance transactions under commercial lines of businesses, there is nominal conversion to insurance chatbots. In addition to inquiries and help-related questions, other transactions have yet to be fully automated.
But with the help of artificial intelligence and machine learning, it won’t take long before commercial insurance transactions and conversations will be defined under various patterns derived from and supported by historical data analysis. Thus, chatbots can complement those transactions which could redound to more benefits for both the insurers and insured.