Technology has brought in significant game changers in the insurance industry that requires executives to implement intelligent automation in their businesses: –

• Customer satisfaction – For any business, the customer is the king, and the insurance industry is no separate from this fact. The sole revenue source of the insurance industry is the premiums being paid by the customers. Technology has changed how customers operate and transformed their expectations. The customer demands unique customer experience, 24-hour access, faster and hassle-free service, relevant product and pricing information tailored to their needs. Insurers need to innovate to buy customer loyalty. High customer satisfaction driven by the improved service and faster processing times that automation delivers is a driver of profit through increased customer retention.
• Insurtech – What fintech has done to the finance business, insurtech intends to do the same in the Insurance business. Stringent regulations, large balance sheets of the incumbents coupled with customers tendency to not switch the service providers made it hard for the new entrants. But, the flexibility in business model and inclusion of automation and machine learning has forced the venture capitalists to induce money in Insurtech. The exhibit 1 shows the investment and confidence put in by the venture capitalists in these new startups which surely is a risk on the market share for the traditional insurance service providers.

• Risk Prevention – Digital technologies that give rise ever-increasing amounts of data and ever penetrating insights make for more accurate pricing of risk, but they also help mitigate the risk and hence reduce the premiums. For Instance – self driving cars and newest safety measures in the automobiles reduces the number of accidents and hence the value of the insurance policies. Similarly, for home insurance, the fire sensors can provide early warning and hence reduce the damage and overall insurance policy value. This logically would mean that insurers would pay more for the devices that help risk prevention and less for the insurance policies, hence decreasing the revenue of the insurance company.

Use Cases for Intelligent Automation in the Insurance Industry

Underwriting

Underwriting is the most critical element in the insurance business. The assessment of risk and align it against a fee requires access to loads of data, data analysis coupled with human decisioning, for instance:

• Health risks – Mortality charges and hence premiums will be increase for the smokers especially when weighed against the applicant age
• Financial limits – If the net worth of the applicant is $X, their insurance coverage cannot exceed $10X
• Creditworthiness – What is the credit rating of the applicant as per the credit bureau agencies and then formulating the decision
• Duplicate policies – Is the applicant holding another policy in their name

Intelligent automation coupled with robotics process automation and machine learning can be of value in automating the underwriting process. The implementation of intelligent automation to automate underwriting can significantly reduce the processing time and assist in making the accurate decisions: –

• Data collection from external and internal sites
• Pre-population of data fields in internal systems
• Intelligent OCR to extract data from the scanned documents
• Assessment of loss runs
• Reviewing the customer history and claims and producing recommendations

• Policy Management

Another common use case for intelligent automation is the whole cycle of policy management operations, including policy issuance and updates.

Upon the underwriting decision, the policy must be issued, and the information needs to be updated in the internal systems and communicated to the customer. This activity is highly manual and prone to error as the dependency on the legacy systems is very high and nature of job is highly data entry. Intelligent automation can be used to automate the insurance policy issuance thus significantly reducing the amount of time and manual work required.

Existing policy holders can submit service requests for updates of the communication address or update of the bank mandate. Robotics process automation and machine learning can be utilized to extract policy updates either from voice transcripts, emails or other sources to make the relevant changes in the internal systems.

• Claims Processing

Fast and efficient claims processing is paramount to success for insurance companies — yet it is often a time-consuming, highly manual process that’s frustrating for both insurers and customers. Typically, claim processing takes several days as insurance agents must gather and check data from multiple sources, such as:

• medical certificates and reports (in case of life insurance or health insurance claims)
• photos of damaged baggage and flight boarding passes (in case of a travel loss claim)
• police reports, driver’s licenses and vehicle damage photographs (in case of an auto claim)

It may take even more time due to human errors, like mismatched financial data or customer details. Such delays may result in the loss of customers and other financial and reputational damage to the company.

Implementing an automated claims processing workflow, including claims intake, assessment, and finally, claims settlement, eliminates friction and cost by combining robotics process automation, machine learning, and human expertise to streamline and speed up claims-related operations.

Benefits of Intelligent Automation in the Insurance Industry

RPA coupled with machine learning can bring massive benefits to any insurance company. The major benefits being the return on investments, enriched customer experience and much happier employees (as they do not have to go through the ordeal of repetitive work)

Unstructured Data Processing

Every day, insurers must deal with massive volumes of data in various paper and electronic formats. To process a claim, an agent must gather data from multiple sources and enter it into a database. The process is manual and time-consuming. Repetition of the same routine tasks repeatedly hinders human concentration, resulting in errors and creating serious inconsistencies in company records. Robots, on the other hand, excel in this aspect.

Machine learning with robotics process automation can process a wider variety of documents more precisely, and can automate insurance processes end-to-end:

• Download, classify, compile data from external sources
• Read, sort, analyze and route emails
• Extract and analyze similar data from different sources (email attachments, transcripts, scanned agreements, etc.)

• Integration with Legacy Applications

Insurance companies still rely heavily on legacy apps and various programs and systems for managing business operations. Implementation of new software, such as BPM or ERP systems, often requires replacement of the existing hardware and employee retraining — which are significant investments of time and money. Due to these difficulties, many insurance companies are forced to stick to the old systems, although they no longer provide the support required for company development.

Automating operations in legacy applications are great robotics process automation use cases in insurance. Robotics process automation bots can use existing user interfaces, which means there is minimal or no need to change current legacy systems. Robotics process automation bots can imitate human clicks and keystrokes, which makes them easy to implement in addition to the existing software and hardware. Robotics process automation bots create links between legacy and new systems without coding. They switch between various systems and applications and conduct claims processing, underwriting, customer service, onboarding, and other operations — all at the same time.

• Additional benefits

Successful implementation of an intelligent automation program can be incredibly beneficial for an insurance company, potentially allowing an enterprise to:

• Reduce the operational costs and increasing the efficiencies
• Improvisation in the accuracy
• Faster execution of transactions
• Increasing overall business productivity and profitability
• Increase the regulatory compliance to 100%

Conclusion

The true power of machine learning and automation to change the insurance industry is just starting to be felt. The global analysis forecasts that down the road, these technologies will empower insurers to identify, assess, and underwrite emerging risks and identify new revenue sources automatically, with little human interference required, making insurance a potentially semi-automated industry.

However, the journey begins with a pilot model: develop a proof of concept, test the derived benefits, and extend deployments once successful