Automation Opportunities Across the Insurance Value Chain
The digital revolution is transforming how work gets done across the entire insurance value chain. Technologies including artificial intelligence (AI), robotics, Internet of Things (IoT0, Blockchain, edge computing, and machine learning all have a part to play. However, for insurers to seize the opportunity, they need to understand the enablers of automation through their value chain, from sales to underwriting, claims, and payments.
The insurance industry is one that is desperate for innovation. Traditionally, the sector relies on face-to-face interactions, human call centers, paper-based documents, static pricing, manual underwriting, and standardized products.
However, adopters of new technology are steadily improving operations in all areas of the insurance value chain.
The change has come as industry incumbents are wary of the threat from big players like Google and Amazon and changing customer demands. In 2018, Alphabet invested heavily in a health insurance startup, signifying its intention to break into the market. In the US, Millennials are the most extensive market buying their insurance. The younger demographic demand personalization as a result of changing expectations that deliver "Amazon-like" experiences. For example, product recommendations, dynamic pricing, mobile experiences are standard retail offerings.
To successfully compete in the new landscape, insurance companies need to revitalize the value chain through automation.
Insurance Process Automation
McKinsey estimates that 30 or fewer manual processes account for 40% of an insurer's cost of doing business and 80% of customer activity. In digitizing or automating these processes, firms can eliminate up to 25% of manual labor costs. Insurance Process Automation is now essential across all functions of the value chain.
One of the most significant pain points in insurance from the customer perspective is claims processing. Similarly, it involves various manual processes for human stakeholders, incurring cost and time. For example, imagine if somebody steals your belongings during a night out. Typically, you need to find your insurance documents, call the claims line, fill in some forms, validate the items you own, file a police report, make a few more calls, and you may receive a payout.
However, automation can revolutionize the end-to-end claims process. In 2017, US insurer Lemonade claimed it set a world record in paying a claim within 3 seconds of receiving the details. The customer could describe the situation via their phone camera, hit submit without paperwork, and that was it.
The Lemonade claim bot runs algorithms on the details to validate fraud, and if everything meets the terms of the insurance contract, payouts are instant. AI and Blockchain technology create an automated flow that makes it all possible.
Insurers can take advantage of robotic process automation (RPA), machine learning, and human expertise to speed up their claims operations. When notification of loss is received, a bot can use RPA to extract the information and automatically enter it into claims systems. Natural language processing can map the details of the claim to the insurance contract, checking the validity.
If the claim is valid, payment is authorized, else it is sent to a human agent who can handle the exception. The amount of manual work is significantly cut down, improving customer experience and staff satisfaction by removing repetitive tasks.
When talking about dynamic pricing, the premium for insurance changes depending on the context of a sale. For example, a policy is cheaper for a low-risk customer and more expensive for high-risk customers, looking at several factors. In motor insurance, a car that frequently drives on motorways is a higher risk than one which sits in a garage 90% of the time.
A pricing algorithm that uses machine learning ingests vast amounts of data to create automated and intuitive pricing decisions. Back to the motor insurance example, it could look at the car model, driver demographics, and mileage and what competitors are doing, operating costs, and claims risk. All this information together returns a price at a point in time.
Autonomous pricing allows insurers to quickly adapt to new risk profiles, staying flexible amidst changing scenarios. Consider the recent Covid-19 pandemic. Insurers can add rules to their algorithms that influence pricing in areas at risk of spreading the coronavirus, rather than manually deploying new models. Speed to market makes them efficient while retaining profitability at an unprecedented time.
Policy Management and Servicing
There are various applications of automation to manage better and service insurance policies. AI, combined with RPA, can handle routine tasks, removing the manual human element. Productivity increases, costs reduce, and error rates decline.
Examples of automation could be downloading, classifying, compiling data from documents, sorting and analyzing emails, or integrating legacy systems.
Know Your Customer (KYC) processes are a fundamental and legal part of insurance. Companies can now conduct a KYC process accurately and efficiently without adding to the workload of their teams. For example, a manual process involves checking public databases, due diligence, and anti-money laundering. Each of these can take a substantial amount of time, heavily dependent on human monitoring.
KYC automation uses RPA and AI to ingest data and reduce or eradicate the manual processes associates with the tasks. For example, machines can collect and analyze data to give an instant picture of any client, considering numerous sources of information. Reports show that onboarding costs are reduced by as much as 70%. Checks can be run 24/7 and are very scalable as new data sources and procedures prevail.
Conversational chatbots are becoming commonplace within insurance to provide customer service and manage policies. A chatbot offers automatic responses to customer queries, using a set of rules. Standard questions within a policy wording are answered by a bot, rather than a costly phone call to an agent. Customers get responses whenever they need it in a fast and efficient manner.
The Sensely chatbot assists insurance plan members and patients with the services and healthcare resources they need. Insurance companies use the platform to provide global assistance to their customers.
As well as dynamic pricing, there are other ways in which insurance automation can assist the underwriting process.
Operations teams can spend much time checking policies for errors or mismatches, slowing down the underwriting process. RPA bots can streamline the checking process and evaluate against checklists and set rules. As well as this, in some insurance verticals, there will be a need to check the creditworthiness of customers.
A credit check will look to external agencies like Experian, TransUnion, or CIBIL Limited, to understand the financial risk of a customer. These services can be integrated into existing systems, providing an automatic assessment, feeding directly into policy underwriting.
Databases such as CUE store masses of information on claims for motor and home insurance customers. The database's data can be part of the underwriting process, negating the need for requesting additional details and manual checking.
Insurance companies have not been as fast to adopt automation as other sectors, due to legacy systems, complex processes, and vast amounts of unstructured data. However, new technology like AI and RPA provides the industry with an opportunity to innovate right across the insurance value chain. Automation is a way of transforming services and creating an experience worthy of the 21st-century customers. Although cost saving is a big part of that, it should not be the primary reason for automation, as firms look toward customer-centric frameworks.
Automation brings in a new age of insurance, with an exciting future.
Manish Garg is the co-founder and chief product officer of Skan.ai, a leading process intelligence platform.