Exploring The Potential Of Analytics In Improving Insurance claims processing and customer experience

Exploring The Potential Of Analytics In Improving Insurance claims processing and customer experience

By Dipak Singh April 20, 2023 - 98 views

Insurance claim processing is a tedious job, at least from a conventional perspective. At the same time, customer experience is another pain point for most insurers, not just with regard to claims processing and other procedures, but also several accompanying aspects. The digital evolution and transformation of insurers have led to several positives, including higher accuracy, efficiency, and speed for almost every division.

Highly advanced predictive and data analytics enable insurance companies to take decisions driven majorly by data. AI has already empowered insurers with the necessary insights for discovering new opportunities, tackling threats, and ensuring better productivity at multiple levels. Data analytics has thoroughly transformed every aspect of the process, right from customer communication and engagement, to claims, applications, underwriting, and also pricing.

With proper data visualisation and analytics, underwriting procedures are automated and made more accurate by insurance companies. They thus identify customer risks through advanced predictive modelling and other tools. At the same time, this enables fixing premiums or pricing for at-risk customers based on prevailing guidelines and slabs of the insurance company.

The insurance industry has only scratched the surface of the vast analytics-driven possibilities for future progress and prosperity. Here is a closer look at how it enables improved claims processing and enhanced customer experiences in turn.

Claims Processing & Other Aspects- How They Can Be Improved

Data analytics can contribute greatly towards various aspects, right from fraud detection to smooth processing of claims. Here are some of them:

  • Automated Claim Payments- Many insurance companies often require extensive inspections and assessments of damages for claims. This leads to longer wait times for customers and a drop in their satisfaction levels. Retention rates may also go down as a result. Data analytics and other AI tools can help in streamlining claim payments based on an accurate and speedy assessment of damages.

  • Claim Development Modeling- Predictive modelling is what insurance companies can use for building a more accurate and automatic system for understanding how much any claim will eventually cost. This is necessary, since claim amounts may sometimes change from the initial filing till the complete payment. The prediction of the final claim is important since it has a direct impact on the company’s financial statements. Companies can view which policies get more customers and their segmentation, along with claim trends, and trending reasons for the same.

  • Detecting Fraudulent Claims- Fraud detection is another area where insurance companies witness the benefits of deploying data analytics. These claims are expensive for companies and they do not often have resources for the investigation of each and every one. Accurate and effective predictive modelling can help in prioritising and identifying any fraudulent activity for action. Insurers will find the age group from where most frauds are reported. Insurance companies can accurately detect fraudulent activity and plug the gaps to minimise their losses, without hampering customer experiences through challenging claims which are innocent or genuine.

  • Intelligent management of cases- Insurance claims processing becomes a breeze with automated and intelligent management of individual cases. Automated and digital customer claim management and journeys with AI and analytics help greatly in lowering manual processes and touch-points, while enabling higher satisfaction and retention with a quicker claims process.

  • Better front and back-office processes- Data analytics and other AI tools can help greatly in transforming both front and back-office systems. These include calculation, underwriting, loss value estimation, reporting, verification of damage or repair estimates, invoicing, and so on.

  • Better customer communication- AI can be used to fully automate customer communication throughout every stage of the claim process, thereby enhancing customer experience greatly.

Hence, analytics can enhance the entire spectrum of processing claims and the whole customer journey/experience associated with it. AI can help in inferring claim possibilities including litigation, losses, fraud, and so on. Algorithms can also categorise or segment claims based on complexity and several important attributes.

AI can also support the optimal handling of claims in several cases. Hence, it is fast becoming imperative for insurance companies globally today.


How can analytics solutions help insurance companies detect and prevent fraud?

Analytics solutions can help insurance companies identify fraudulent behaviour patterns and activities, narrow them down, and detect such transactions swiftly before or after completion. This helps insurance companies plug gaps and take swift action. 

How does it improve customer experience?

Analytics and big data have a huge role to play in enhancing customer experience through automated and timely communication, quicker processing of claims through automated verification and assessment, and also intelligent management of cases. 

How do insurance companies protect customer data collected by IoT devices?

Insurance companies take several measures to safeguard data gathered from IoT devices. These include encryption of data and there are several types that companies may pick from. Insurers also emphasise proper data storage and governance under applicable regulations. 

What is the potential impact of analytics on insurance premiums and pricing?

Analytics has a profound impact on pricing and premiums. It helps insurance companies estimate customer risks and then automate premium/pricing calculations accordingly. Data is analysed from several sources in order to gather insights on segment-wise risks and the best possible pricing for the same, keeping the policy type and other aspects in mind.

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