Enhancing Customer Experience with robotic process automation

Given the importance of actuarial science in insurance, robotic process automation (RPA) is the right fit for this industry

Maneesh Jhawar, Founder & CEO, QualityKiosk

 

The insurance industry is passing through testing times. The ever-changing regulatory framework, operational difficulties due to complex business processes and rising expectations of digitally savvy consumers are challenging the carriers to push their limits. Insurance providers have already been embracing core insurance technology systems covering functions such as sales, policy issuance, and administration. They have also been investing in enterprise mobility to improve customer engagement. However, rapid advancements in business technology and entry of nimble insur-tech rivals with low overheads are prompting the traditional insurers to explore new business methods. Given such dynamic scenario, robotic process automation (RPA) can prove to be a useful technology for insurance companies to stay relevant and win competitive edge.

 

Known for its ability to speed up laborious IT-enabled tasks, RPA is generally sold as ready software development packs. With RPA, carriers can build software robots to automate time-consuming business processes and improve operational efficiency. Let's explore two examples where RPA can prove highly beneficial to insurance companies not only in terms of achieving high-productivity goals but also enhancing the customer experience.

 

Use case 1: Life insurance

In case of life insurance, RPA can deliver improved operational efficiency in the new policy issuance process. This is a product where the probability of repeat purchase is high. Customers tend to buy a new life insurance policy within a few years of purchasing the first policy. When a customer purchases a new product, an application form needs to be filled up. If the applicant is an existing customer, the software robot can recognize the customer and populate several fields automatically. Similarly, in case of a new customer, it can populate the fields with Aadhaar details automatically. A bot can also validate the data with application form data nearly instantaneously. Thus, under normal conditions, RPA can perform these functions with high speed and accuracy, delivering 50 to 70 percent savings in time required for application processing and initial due diligence.

 

Use case 2: Health insurance

Claim processing in health insurance involves multiple steps. Upon treating a patient, a hospital sends detailed claim documents including healthcare service expenditure statement to the insurance carrier. The information includes current procedural terminology (CPT) and international classification of diseases (ICD) codes that help accurately describe the nature of disease and treatment provided. The patient’s demographic data, disease, diagnostic and treatment history, and insurance details are enclosed with this document. The claim data is passed on to internal teams who examine the claim and validate it based on a set of predefined rules. Traditionally, it would take anywhere from a day to a fortnight for a health insurer to evaluate and settle the claim amount. With entry of RPA, this time can be brought down to hours if not minutes.

 

In health insurance space, the claim settlement time is of critical significance to patients as it’s directly proportional to the potential hardships experienced by a patient and the family members. An insurance company’s success (or failure) depends on how fast it is able to settle claims and make the customers (and their families) happy. By speeding up this entire process, RPA can bring about a transformational change in the insurance company's brand perception.

 

RPA can be of great relevance to health insurers for another reason. Given the seasonality in disease occurrence, there may be spikes in number of claims an insurer receives for short durations. Hiring claim processors for a short period (and firing them later) may not be feasible. RPA renders the issue irrelevant as bots can be freely commissioned (or decommissioned) as and when needed.

 

Use Case 3: Automobile insurance

A similar process can be traced in the automobile insurance space wherein a speedy claim approval (or rejection) can help boost customers’ confidence in the insurance organisation. Insurance companies receive thousands of claims from automobile repair shops and OEMs. The claim documents also contain photographs of vehicle and parts that need repair or replacement. With RPA, claim processing which is largely a manual and time-consuming exercise, can be completed within a few minutes.

 

Going a step ahead, the image recognition capabilities of software can help an insurance carrier to estimate the level of damage from the images provided. The carrier can make a realistic estimate of the possible cost of repair/ replacement and compare it with the costs mentioned on the invoice. This way, the insurer can not only save  money by curbing the incidence of bloated claims but also play watchdog protecting the customer from being overcharged (by repair shops).

 

Why insurers must start early on

The experienced claim processing officers possess knowledge gained over several years of work. They are able to detect a fraudulent claim even when it apparently fulfils most of the predefined conditions.

 

As bots process the several claims rejected by experienced (human) officials, they develop an intelligence about which claims need to be rejected or at least be notified for review. This makes it essential for carriers to adopt RPA early on. Earlier they invest in RPA, greater information can be fed to bots over the years. Starting early on can thus help insurance carriers to exploit the full potential of AI that RPA promises to deliver and improve their profitability.

 

About the Author:

A  B.Tech from IIT Kanpur, Maneesh has also completed a program on “Transforming Professional Services Companies” from Harvard Business School. He started his career with Procter and Gamble in 1990 and started QualityKiosk Technologies in the year 2000, after sensing the need-gap in the software testing industry. 

 

 


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