Automating data verification for quicker insurance processing

According to Chris Wyard, former Head of Technical Data at Allianz Insurance: “The greatest challenge facing (the insurance industry’s) data leaders is the quality and integrity of data.”
This concern is backed up by a report by Corinium Intelligence that suggests only 24% of insurance representatives are confident in their data. 

Accurate data is crucial for assessing risks, determining appropriate premiums, and making claim decisions. As a result, data verification plays a crucial role in the industry, intending to ensure that information submitted by policyholders is accurate and reliable. 

However, manual data verification has many inherent challenges, including:

• Time-consuming procedures
• High susceptibility to human error
• Increased operational costs

These challenges have several consequences, including premiums that don’t accurately reflect the risks, erroneous claims decisions, and delays in policy issuance and claims processing. 

Automating data verification offers a solution to these challenges by using advanced algorithms and machine learning techniques to cross-reference and analyze data against historical data and established patterns. As we discuss in this post, these technologies give insurance companies a competitive edge, increasing efficiency, lowering costs, and reducing errors while also improving the customer experience.    

The need for automated data verification

Traditional data verification methods in the insurance industry usually involve several manual processes that can be time-consuming and prone to error, including:

• Double data entry
  Two people enter the data twice to check for discrepancies and ensure accuracy. Th
• Proofreading
  A person checks data for typos and obvious errors. 
• Comparison with source documents
  Someone manually reviews data that’s already been entered into the system and compares it to the original source documents.

All of the above methods are slow, labor-intensive, diverting expensive resources from more strategic tasks. 

Traditional data verification methods are also prone to human error, resulting in inaccuracies and inconsistencies in policyholder information. These issues often have a cascading effect that leads to problems downstream, from incorrect risk assessments to faulty premium calculations, resulting in potential financial losses for both the insurance company and its customers. 

Such inaccuracies also provide openings for fraudulent activities, since data discrepancies can be exploited by fraudsters to file false claims or manipulate policy details.

That’s why data verification is integral to the entire insurance workflow, impacting every stage, from policy issuance to claims processing and renewals:

• At the policy issuance stage, data verification is crucial for assessing risk and setting premiums. 
• During the claims process, verifying the authenticity of information determines the legitimacy of the claim and the value of the payout.
• For renewals, accurate information ensures that policy terms remain relevant. 

How automation transforms data verification

Data verification can be automated using several technologies, including artificial intelligence (AI), machine learning (ML), and optical character recognition (OCR). These technologies are integrated into existing workflows and used to automatically extract, analyze, and validate data from source documents and digital inputs. 

Here's an example of how these technologies work together in the context of insurance claims:

• Initial claim submission
  When a policyholder submits a claim, documents (such as invoices or receipts) can be in various formats. OCR technology scans and converts these documents into digital format. 
• Data analysis and pattern recognition
  Once the data is digitized, ML algorithms analyze the information to identify patterns, anomalies, or signs of potential fraud. For example, ML can compare the claim details against historical claims data to detect inconsistencies.
• Automated decision support
  AI systems automate certain decision-making aspects of the claims process, such as validating claim details against policy terms and conditions. AI can also prioritize claims based on urgency or complexity, flagging complex cases for human intervention.
• Continuous learning:
  ML models continue to learn and adapt, improving their accuracy and efficiency over time. This ongoing process allows the system to become more adept at identifying legitimate claims quickly and flagging potential issues for further investigation.

The benefits of automated data verification are profound:

• Speed: Automation allows data to be verified in a fraction of the time it takes to do so manually, reducing processing time for applications, claims, renewals, other insurance workflows. This, in turn, accelerates decision making, allowing insurers to act fast when it comes to issuing and renewing policies, assessing risk, and resolving claims. In addition to boosting operational efficiency, policyholders receive faster, more timely service. 
• Accuracy: Automation systems are designed to follow precise algorithms and criteria when verifying data. This ensures data integrity and reduces the likelihood of the types of errors that often occur with manual data verification. 
• Consistency: Unlike manual verification, where individual interpretations of information are subjective and tend to vary, automated systems apply the same rules and standards to every process and transaction. This helps to maintain fairness in workflows as well as aiding in regulatory compliance. 

Implementing automated data verification

When implementing automated data verification, insurers need to evaluate their current processes. This step is to identify bottlenecks and delays caused by traditional data verification methods. Automation can then be strategically deployed in a way that’s tailored to the needs of each insurer’s processes, ensuring the most significant results. 

The importance of end-to-end data intake and verification

Automated data verification involves more than just adopting new technologies - it requires a shift in how organizations view and manage their data. Insurance companies need to adopt a data-centric approach, ensuring that data is automatically validated and updated during intake, across all processes and workflows. 

According to CapGemini, gathering quality data is a challenge for insurers:

Automated data verification requires an end-to-end approach to data intake. This means that data collected during any customer interaction is immediately and automatically verified. For example, during the initial stages of the application process, digital identity verification uses a variety of methods (such as biometric scanning, face recognition technology, and document verification) to ensure that a customer is who they say they are.  

Another example is data pre-filling. Automatically populating form fields with information that’s already available in the system makes the process far easier and more convenient for customers. According to Alan Luu, AVP of Advanced Digital Analytics, Chubb: “We expect revenues to increase by at least 5% in the first year, just by pre-filling the data”. 

Implementing automated data verification with an end-to-end perspective on data intake improves operational efficiency, reduces costs associated with manual errors and inconsistencies, and enhances the overall customer experience.

Embracing the future with automated data verification

The integration of automated verification methods such as AI, ML, and OCR, isn’t just about adopting new technologies - it represents a wider transformation towards data-centric operations.. For insurance companies looking to stay competitive in a rapidly evolving industry, adopting these technologies isn’t just an option, but a necessity.