Intelligent Automation in the Insurance: A Full Guide

Its outdated business models and legacy environment have not been updated for decades.

Compared to related industries successfully advancing on the path of digital transformation, insurance services have so far often failed to meet modern customer tastes. To prevent the situation from worsening, companies began to hastily adopt insurance automation technologies, leading to major changes in the insurance industry. According to a survey by PwC, 41% of consumers are likely to change their insurers due to a lack of digital capabilities.

By 2026, there is a likelihood of consolidation or replacement of the insurance industry, especially in the areas of operational and administrative support. The insurance industry is now actively exploring smart automation options as artificial intelligence, machine learning, and cognitive tools are being combined with robotic process automation (RPA) to improve the efficiency of existing processes and reduce operational costs.

Source: https://appinventiv.com/blog/automation-in-insurance/

Challenges and Opportunities in the Insurance Industry

Traditionally, the premiums paid by customers were invested by insurance companies in several financial instruments to earn good returns. However, in today's low-interest rate scenario, this source of income has dried up.

Online insurers with minimal infrastructure are offering low rates, making the market much more competitive and tight. There will soon be potential competition from companies like Amazon, Google, and Facebook, which have huge amounts of personal data to offer personalized insurance products. Insurers face constant challenges to optimize operational costs, improve overall accuracy and customer experience, and maximize return on allocated capital.

Let us now look at some of the issues/challenges limiting the ability of insurers to achieve their goals:

• The insurance industry works with huge reservoirs of data, dealing with mixed data formats that include both paper and electronic documents. The manual work of extracting information from these documents and various data sources is not only significant but also costly and error-prone.
• Large insurance companies utilize a complex IT environment consisting of several legacy applications and disparate systems. This leads to operational inefficiencies and unnecessary costs for administrative functions.
• In addition to policy issuance and claims processing, there are many internal processes that require intensive manual labor and are time-consuming, repetitive, and error-prone. Some examples of these internal processes are policy quoting and servicing, underwriting, accounts receivable and payable compilation, premium renewals, payment discrepancies, compliance and legal/credit checks, etc.
• As with any other business, scalability is another challenge that arises during seasonal peaks in the insurance industry. It becomes even more challenging during large-scale catastrophe events, which requires the claims process to be efficient and accurate to handle the high volume of claims.

Smart Automation Use Cases

Before we look at some use cases, it's important to consider the following when embarking on automation:

• Not all complex processes are worthy of automation.

It is important to evaluate whether automating such a process will yield any significant savings, as automating a complex process incurs significant automation costs. It may be better to choose medium or less complex processes that can provide significant economic benefits.

• Start small and expand gradually. 

Start with a small initiative with defined goals, keeping the bigger picture in mind. This will help insurers evaluate the effectiveness of the process with the new solution. Once the results meet expectations, gradually expand the solution on a larger scale.

• Avoid over-automation.

Often organizations would like to automate the entire process, eliminating the need for any human intervention. This can lead to significant automation efforts and costs. However, it is important to evaluate whether a hybrid approach can be used where optimized use of automation and human intervention can be leveraged to achieve the desired goals. Similarly, if it is just a one-time action that will not be repeated once it is completed, then automating such an action using RPA may not be useful.

Now let's take a look at RPA guidelines:

Client Adaptation 

Artificial intelligence systems help to manage chatbots that simplify interaction with insurers. In particular, a chatbot can help to draw up documents, advise on the amount of refund or insurance conditions. Chatbots significantly increase the level of customer service, as they provide round-the-clock access to information.

In general, the time between the request and receiving a response is reduced, which increases customer loyalty. It can be said that chatbots have revolutionized the process of interacting with customers, as previously this level of service was only possible in premium segments.

Risk Analysis

Pricing transformation includes the introduction of new approaches to underwriting and risk monitoring. By being able to analyze structured and unstructured data, the application of Intelligent Automation in underwriting improves risk sampling and overall quality of service.

The customer no longer has to wait for an insurance agent to check their documents, and they get a better experience when interacting with the company. Today, many insurers are already using machine learning and advanced analytics to predict fraudulent claims, the occurrence of an insured event, and to identify high-cost claimants

Insurance Claims Settlement

At the moment, claims settlement is mostly manual: a person reports an insured event, collects the required set of documents, and a manager checks the information received and makes a decision on compensation.

This is labor-intensive work that requires checking a large number of documents. A trained neural network can do the same work many times faster.

Obtaining up-to-date data is one of the key challenges insurers face when implementing Intelligent Automation. Neural networks require databases for training, and many insurers store information in outdated, isolated systems. Therefore, to successfully implement RPA, the first step is to modernize storage systems. The next step is to identify scenarios for using Intelligent Automation in your business.

As a rule, it is more advisable to optimize existing business processes first, and then move on to developing new approaches.

To Sum Up, Intelligent Automation technologies provide ample opportunities to improve customer interaction: fast settlement of insurance claims, optimization and personalization of tariff plans, round-the-clock support in chatbots, and so on.

Thus, innovations benefit both consumers and companies, which gain competitive advantages and stable demand.

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