Recent stats show new business in Singapore’s life insurance industry grew by 10% in total weighted premium in the first quarter from January to March 2020. This comes as more consumers are looking to secure their financial future in view of COVID-19’s drastic impact on the global and local markets.

The insurance industry has for a long time now been held back by its reluctance to innovate and adapt due to barriers of regulation, lengthy processes, and product complexities. But as customers expect faster turnaround times and greater personalisation, this is about to change.

With Singapore being a hotbed for startups in the region, the country has welcomed many insurtech players who seek to innovate and collaborate with the insurance industry. Insurtech refers to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model.

And the insurtech scene in Singapore can be best described right now as booming and competitive. With insurance companies looking to move away from the traditional legacy-based operations and increasingly adapting new technologies to compete in a digital marketplace, a perfect storm is created for the two worlds to come together to transform business processes.

The power to transform unstructured data

The insurance industry is one of the most regulated and oldest business service industries in the world. An industry built on trust and customer care that is still reliant on human touch by way of complicated, manual, and paper-based processes. Quite clearly, the industry is still playing catch up on digital innovation and adaptability.

Insurers rely on rich data to gather useful insights they need to develop the right products and services their customers need, as well as to make their business more efficient and agile. Yet most of the data they need to access is unstructured, coming in the form of emails, text documents, research, legal reports, voice recordings, videos, social media posts and more. In fact, nearly 80% of an enterprise’s data is unstructured.

Some companies are already using robotic process automation (RPA) to automate specific tasks. But as they look to scale their RPA initiatives, they frequently encounter the limits of RPA – it does not allow for the end-to-end automation of a business process.

For example, the traditional optical character recognition (OCR) technology employed by most RPA solutions cannot capture data from the majority of documents businesses manage. OCR uses template-dependent or zone-based data extraction methods to obtain information, well-suited for structured data of fixed record length format such as database contents.

According to the Everest Group Research: Intelligent Document Processing (IDP) Playbook, businesses can overcome existing RPA limitations by complementing it with artificial intelligence (AI). By leveraging increasingly sophisticated document processing solutions combined with AI like Cognitive Machine Reading (CMR), organisations can now be empowered to digitise all of their data—overcoming the unstructured data challenge.

Unlike its predecessor, OCR, CMR can read structured, unstructured, image and inferred data, whether it is printed or handwritten text, or simply image data such as notary stamps or signature verifications. Not only can CMR harvest structured and unstructured data, but it also processes the data efficiently, learning it in such a manner that it is able to automate entire business processes without human assistance.  The straight-through processing enabled by CMR can cut manual efforts by 95%, enhancing workforce productivity, and allowing human resources to focus on higher value tasks.
(This was one of the challenges faced by a Fortune 500 insurance company, whose employees had to manually examine more than 27 types of unstructured documents prior to adopting a solution that enables straight-through processing).

There is a clear need for Insurance firms to take a cognitive approach to automation. The sheer volume of business data, mainly comprised of unstructured data that needs to be processed and analysed, along with the complex business rules that need to be followed, point towards a need for innovation and growth.

Insurance companies need to innovate rapidly to improve their process through technology like AI, which has capabilities to help insurance practitioners to do business much faster, more efficiently and with greater security. When it comes to automation, Insurance companies will need to relook at it from an end-to-end business process perspective rather than looking at it from a single business process view.

Boosting profitability through automation

Insurance companies are also adopting and implementing different AI technologies in their operational processes in a bid to not only improve their customer experience but also their bottom line. Many insurers are looking to expedite renewal policy quotes with automation to gain faster and more customer centric results.

In fact, according to a recent McKinsey report, automation can reduce the cost of insurance claims journeys by almost 30%. Intelligent Automation can process large volumes of data and ensure the generation of faster, and more accurate results. One leading consulting firm with considerable business in the benefits space realised its OCR solution was not providing enough automation based on its data extraction capabilities.

The firm has since adopted CMR, now generating quotes 70% faster with 100% compliance, improving its customer experience and, ultimately, making faster business decisions.

It is important for insurance companies to innovate and keep up with times, now more than ever. By employing the right automation technologies, insurance companies can achieve the trifecta – satisfied customers, lower costs, and increased growth.

Identifying the most valuable automation opportunities combined with implementing the right IA tools will enable insurance companies to achieve far greater operational efficiency and resiliency, and stronger revenue streams than ever before.

This article was first published on FutureCIO. Reproduced with permission.