The insurance industry runs on vast and expanding reservoirs of data. Sources of information and the techniques to manage data are multiplying exponentially. As physical, digital and biological data and processes converge, technologies such as Robotic Process Automation (RPA), Artificial Intelligence (AI), the Internet of Things (IoT), nanotechnology, and quantum computing are emerging and becoming mainstream tools.
Even in an age of ubiquitous data, many insurance processes carried out by people are mundane and repetitive. Nonetheless, the tasks require precision and attention to detail. There is growing interest in robotic process automation (RPA) in insurance because of gains such as the establishment of digital workforce technology, freeing up resources for more valuable and strategic tasks, increasing throughput, reducing turnaround time and improving overall accuracy and efficiency in insurance operations.
RPA fills in effectively as a “virtual resource” performing repetitive, rule-driven tasks, driven by software and through existing applications. Digital robots act on business rules that are fed to them. Robots can work unattended for back office types of operations, without requiring input from users. Or, they can work in a mode requiring human intervention — typically in front office operations or in interactional and transactional situations.
Cognitive or machine-based learning is another self-training capability associated with robots. For example, cognitive bots can be set up to observe human behaviour and judgements, and learn from them to execute similar tasks independently. Such bots can help in an analytical environment, involving human judgement or decision-making. This technology can help in automating financial computations, analysis and data interpretation, leading to more enriched and insightful results in the business of insurance.
RPA can be particularly effective in Property/Casualty, Healthcare, Travel, and other insurance lines which handle large volumes of repetitive business data, requiring similar kinds of processing, based on established operational procedures. This is especially significant considering inefficiencies in the current methods of doing business. For example, in the London Market, insurers typically have 30-40 percent of staff who are revenue generators, while 60-70 percent are predominantly in support functions. Other insurance markets experience similar trends, including in North America and often among insurers with a preponderance of legacy platforms.
Many insurance companies are exploring RPA to enhance their business processes, with the goal of reducing operational costs, gaining efficiency and improving overall accuracy and customer experience. Using robotics, underwriters and actuaries working on premium coding, pricing and financial analysis, can expect more accurate output at a quicker pace, enabling them to write business faster, with greater precision and visibility on risk parameters.
In a highly competitive market, the traditional ways of doing business often prevent companies from delivering timely customer service, meeting growth and profitability expectations, and maintaining competitive differentiation. RPA is showing great promise as a cost-efficient technology, as an enabler of more refined policyholder service, and as a potential competitive differentiator.
Xceedance recently completed an in-house RPA proof of concept with impressive and successful results for an insurance client. RPA was applied to policy processing tasks, and the initiative dramatically cut the time to issue policies and significantly increased accuracy — so that insureds quickly receive correct and comprehensive policy forms. There was a dramatic improvement in productivity and reliability. Among the findings of the RPA initiative, the time to issue a policy was cut in half, there was 100 percent accuracy in formatting, and it took 90 percent less time to validate the policy information.
Adoption and implementation of RPA across insurance operations is becoming easier, as RPA experts help companies to become self-sufficient by providing expert guidance and training. Robotics can be phased in and applied thoughtfully across an organization, thus minimizing conflict with existing processes.
To successfully master and implement RPA, insurers do not require highly sophisticated programming expertise. Mainly, this is because RPA is designed around the concept of script-less programming augmented by intuitive tools, making it relatively straightforward to develop and customize the technology. In fact, robotics development relies heavily on screen capture or scraping and motion recorders, providing flexibility and cost-efficiency in automating complicated rules-based processes.
However, there are also challenges, ranging from technical issues — such as establishing and controlling screen resolutions that drive RPA programming, to more strategic and relationship oriented matters — such as managing frequently changing client requirements in the deployment of RPA.
While adopting RPA, insurers need to be aware of several parameters that could impact the cost-benefit equation, including implementation strategy, training, environment support and ongoing professional services.
Due to the process flexibility and improvements that RPA can deliver, its adoption by insurance companies will surely increase; and the financial benefits can be reinvested in other emerging and digital technologies, to bring even more benefits and agility to the business.
With RPA, there is a good chance mundane tasks will gradually dissipate in the insurance industry. Professional staff will find it easier to focus on value-add functions driven by analytical decision making in support of enterprise growth and enhanced policyholder services.
Gaurav Mathur is senior manager, operations and Vinay Sabharwal is manager, underwriting services at Xceedance.