Analytics That Lower Spending on Claims

The secret is to unlock the potential of the large quantities of unstructured data streaming through the claims function.

By finding opportunities for digital transformation within the claims function, insurers may reduce costs and increase performance.

The reduction in profitability due to rising claims rates is creating pressure. In 2019, gross claims payments in the U.S. alone accounted for around $1.57 trillion, 5.5% more than the gross claims payment in 2018. However, price competition has made insurers reluctant to increase premiums. Insurers must instead look for opportunities to reduce indemnity spending by going after fraud cases, leakage, supplier costs and recovery performance. 

Traditionally, claims processing requires a significant amount of manual labor, complicated workflows and incomplete and disorganized data. The secret is to unlock the potential of the large quantities of unstructured data streaming through the claims function. By closely analyzing internal procedures, in-house and third-party data sources and turning raw data into usable information, insurers can produce insights that improve both claims quality and efficacy.

The approach to reducing indemnity spending can be divided into four strategic pillars focusing on data, processes, people and technology.  

Sourcing the Right Data

Claims organizations must assess the quality, accuracy and availability of their data. Through sharing the correct datasets across functions, ensuring that data definitions are consistent and resolving any specific privacy issues, organizations can create useful insights from these data.

Insurers have large volumes of internal data, including reports on customers, quotes and prices, policy specifics, claim details and past examples of fraud. This can be used to make informed, easier decisions and streamline the claims workflow. For internal data that is unstructured and resides in the form of PDFs, conversation recordings or emails, insurers need intelligent programs that leverage algorithms such as natural language processing (NLP) or optical character recognition (OCR) to convert them into usable formats. 

External data -- such as public domain information on demographics and weather -- can be sourced and linked to the claims dataset, enriching the available information and increasing decision accuracy. Third-party proprietary data sources, such as ISO ClaimSearch by Verisk, also offer specific data for sale in areas including claim analysis and fraud detection.

See also: Claims Development for COVID (Part 1)

Application of the Correct Process

Traditional claims systems focused primarily on the experience of claims handlers, with minimal use of data and analytics. With improved availability of better evidence, machine learning and AI, three types of templates can help handlers make better decisions.

  • Estimation models forecast maintenance costs, treatment costs, legal costs and other fields. Insurers may use these forecasts to help track success and focus their efforts. 
  • Classification models provide binary or multi-class decision flags to group similar claims. Insurers can then devise strategies for these specific groups of claims. 
  • Propensity models predict the probability of an event occurring. These models typically provide a probability percentage that can be used for preparing to take the appropriate action for likely future events.

Apart from these three types of models, some improvements within the recovery process could also boost efficiency and cycle times. An optimized chase policy can be accomplished by bilateral agreements with third party insurers. In addition, changes should be made to the fraud detection process. Through applying analytics, insurers can detect subtle or non-intuitive trends, increasing accuracy and coverage and maximizing referral rates.

Engaging the Right People

The insurers with lower claims indemnity are the ones with the right resources and adequate data training infrastructure. Even if the insurers have to pay a premium, they get the best data engineers, modelers and business analysts. These insurers also have robust upskilling, cross-training and retention programs to create a multi-skilled talent pool that fuels the carrier’s data and analytics capabilities.

Technology enablement

Using the right technology enables the claims process to operate at maximum potential and generate valuable data.

  • Analytical tools can improve areas including data management, model development, business intelligence, reporting and visualization. 
  • Claim-specific tools can bring built-in advanced analytical models trained on proprietary third-party data, but will often lack insurer-specific information. 
  • IOT devices for loss prevention and claim avoidance can reduce claims frequency and severity using real-time monitoring, through smart devices such as water damage sensors, home exterior sensors and connected cars. 

How to Optimize Indemnity Spending  

Insurers should use a project prioritization framework that accounts for competing factors when transforming claims processes. This would measure the efficiency of an initiative across several areas. For each project, a detailed analysis should be performed on the potential ROI. The model and analysis should be clear, and the ease of implementation can be calculated on the basis of complexity, the need for data assets and other variables.

See also: Surging Costs of Cyber Claims

Other considerations include the length of time it would take an intervention to deliver the desired outcome. It is also necessary to examine the scalability of an intervention, with special consideration given to the achievement of long-term objectives while accounting for short-term goals. Synergy with existing initiatives also needs to be analyzed.


The transformation of the claims process requires factoring in the different decision metrics, dependencies and process flows. This analysis helps to ensure that the transformation process does not harm existing claims processes. With the right data and analytical strategy in place, insurers can achieve significant compensation savings.

Swarnava Ghosh

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Swarnava Ghosh

Swarnava Ghosh is the senior engagement manager, analytics, at EXL Service. He is a dedicated analytics professional with more than nine years of experience.

Mayank Mahawar

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Mayank Mahawar

Mayank Mahawar is senior analytics consultant at EXL Service. He is an experienced professional with experience ranging from data extraction, data cleaning and data manipulation to end to end model development and deployment.

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