According to McKinsey Global Institute report, 2017, there is 43% of automation potential for Insurance and Finance industries. By 2025, up to 25% of the task force in the insurance industry may be consolidated or replaced, especially in Operations and Administrative Support. The insurance industry is now aggressively looking at use cases for intelligent automation as AI, Machine Learning & Cognitive tools are merged with Robotic Process Automation (RPA) to bring efficiencies into existing processes and reduce operational costs.
Challenges and Opportunities in the Insurance Industry
Traditionally, premiums paid by customers were being invested by the insurance companies into a number of financial instruments to get good returns. However, in today’s low-interest rate scenario, this source of income has dried up.
The online P2P insurers with minimal brick and mortar infrastructure are offering low rates making the market much more competitive and tougher.
In the near future, there looms potential competition from companies like Amazon, Google, and Facebook which are sitting on top of huge personal data to offer personalized insurance products. In fact, Amazon is already hiring insurance professionals looking to disrupt the insurance market in the UK, Germany, France, Italy, and Spain. According to GlobalData 2017 General Insurance Survey, 18% of consumers would buy their motor or home insurance from Amazon.
Therefore, insurers are facing constant challenges to optimize operational costs, improve overall accuracy & customer experience and maximize the highest return on allocated capital.
Now, let’s look at some of the issues/challenges that limit insurers’ ability to meet their objectives:
- The insurance industry runs on vast reservoirs of data, dealing with mixed data formats which include both paper and electronic documents. The manual effort to extract information from these documents and different data sources is not only considerable but also costly and prone to errors.
- The larger insurance companies use a complex IT environment which comprises multiple legacy applications and disparate systems. This results in operational inefficiencies and unnecessary costs spent on administrative functions.
- Besides issuing policies and processing claims, there are tons of backend processes which are manually intensive, time-consuming, repetitive and prone to errors. Some examples of such back-end processes are policy quotes & servicing, underwriting, drafting receivables & payables, renewing premium, identifying premium discrepancies, conducting compliance and legal/credit checks, etc.
- Like any other business, scalability is another issue which comes into play during seasonal peaks in the insurance industry. It gets challenging further during the events of a large-scale catastrophe which requires the claims process to be efficient and accurate to process a large volume of claims.
Use Cases on Intelligent Automation
Before we look at some of the use cases, it is important to consider the following while embarking on an automation journey:
- Not all complex processes are worthy candidates for automation.
It is important to assess whether automating such a process will offer any significant savings, as automating a complex process incurs significant automation costs. It may be better to pick up medium or fewer complex processes which can offer significant cost benefit.
- Start small and expand gradually.
Begin with a small initiative with defined objectives, keeping the larger picture in mind. This will help insurers to evaluate the efficiency of the process with the new solution. Once the results are in-line with expectations, expand the solution on a larger scale gradually.
- Avoid over automation.
Often, organizations would look at automating the entire process, eliminating the need for any human intervention. This may result in significant automation effort and cost. However, it is important to assess if a hybrid approach can be taken where the optimized use of automation and human intervention can be used to achieve the desired objectives. Similarly, if it is just a one-off activity which will not be repeated after its completion, then, automating such activity using RPA may not be useful.
Now let’s look at some of the use cases which may be worthy candidates for intelligent automation in the insurance sector:
- Smart Media Reader: The insurance industry deals with piles of paperwork during policy issuing and claims process. The paper documents are scanned and stored in their digital repository for respective back-office operations teams to manually extract relevant data and enter it into the front-end policy and claims systems. This is inefficient, error-prone and repetitive, which can be streamlined with a minimal human intervention using intelligent automation. A smart media reader solution can be developed using Robotics Process Automation (RPA) which can read and extract relevant data from scanned documents. The solution can use OCR capabilities to read data; use smart references, NLP, and machine learning capabilities to validate & process data; and interface with relevant systems to perform automatic reconciliation. The solution can be integrated with existing processes where there is a heavy reliance on extracting data/information from paper documents.
- Smart Claims Handler: Reducing handling and cycle time of the claims processes to improve customer satisfaction is one of the top priorities for any insurance organization. Therefore, it is important to streamline the claims process where we can create a condition-based RPA solution which will validate and verify the incoming request. The claims requests which meet criteria for automatic handling can be routed to the robotic processing & resolution system (e.g. chatbots); whereas requests not meeting the criteria can be routed for manual resolution. The solution can also have inbuilt rule-based fraud detection capabilities. It can be integrated with smart media reader solution to further bring down operational costs.
- Smart Underwriting Solution: One of the most classic debates of all time in underwriting world is whether – ‘Underwriting is an Art or Science?’ I believe it is both, and therefore, the solution needs to have provision for both. A rule-based RPA solution can be built for underwriting system where it will determine if a submission or renewal is a candidate for automated or a hybrid process. Under the automated process, the rules, predictive models, and machine learning algorithms will evaluate, rate and price a submission. In case of more complex submission, a hybrid process can be used where underwriters will use their art and knowledge, supported by recommendations generated by the solution.
With the constant emergence of challenges driven by disruptive technologies, intense competition, and complex market, the insurance sector is looking at optimizing costs, improving overall accuracy and maximizing returns. Intelligent automation can be used to quickly automate key processes to achieve higher efficiencies and streamline operational costs. This will enable professionals to focus more on value-added functions driven by smart solutions and contribute efficiently to overall organization objectives.
Mindtree uses its proprietary framework – RAPID to maximize benefits from all RPA implementations. To learn more about Mindtree’s RAPID framework and case studies, please refer to https://www.mindtree.com/services/operations/automation/robotic-process-automation.
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