How Intelligent Document Processing (IDP) is Transforming Insurance Claims Handling

Insurance organizations today are focusing on enhancing user experiences through faster onboarding, policy disbursement, claims processing, while reducing cost-to-serve. Achieving these optimized process led-objectives is not an easy matter since insurance is a heavy document-reliant business. Meaning that, every phase of the process largely relies on physical and digital documents, typically in image, handwritten, or PDF formats. Processing these documents manually produces the risk of errors, interruptions, and false perceptions, which can bring about significant concerns.

Intelligent Document Processing (IDP) has emerged as an effective equalizer that can overcome challenges introduced by unstructured data in multiple types of documents. This helps insurers modernize tedious workflows like claims processing. Intelligent Document Processing for Insurance uses a form of Machine Learning known as Natural Language Processing (NLP) and deep learning to apprehend and process the data present in unstructured documents. In short, IDP tools greatly reduce or even eradicate manual review of documents by automating claims processing.

Challenges With Manual Insurance Claims Processing

As mentioned, insurers sticking with manual claims processing may face challenges that are adverse and delimit the digitization capabilities. Challenges can be attributed to:

• Slow Claims Processing
  Policyholders often rely on insurers to provide timely assistance when they experience a loss or need financial support. In such situations, slow processing of claims can lead to frustration and dissatisfaction, impacting customer loyalty. In many jurisdictions, insurance regulators impose specific timeframes within which insurance companies must process and settle claims. Failing to meet those regulatory standards can result in huge penalties and legal consequences for insurers.
• Human Errors
  With manual processing, insurance staff need to enter customer data (policy numbers, claim amount, date, and contact details) into multiple systems. This can introduce data entry errors and lead to issues with tracking and processing claims. Staff may also overlook important documents or inadvertently include duplicate copies of the same document in the claim file.
• Data Inconsistency
  Errors in the documentation of medical reports, repair estimates, or other supporting documents can lead to inconsistent data in the claim file. Investigating and reconciling data inconsistencies may require additional resources and staff time, increasing the operational costs for insurance companies.
• Data Retrieval Issues
  Insurance companies often rely on third-party sources, such as medical facilities or witnesses. Delays or unresponsiveness from these sources can hinder data retrieval. Failure to retrieve necessary data results in incomplete documentation and non-compliance with regulatory requirements.
• Data Security
  With manual processing, the protection and confidentiality of sensitive information is poorly managed. This makes it vulnerable to intervention by malicious hackers. Insurance companies may face significant financial repercussions when they must respond to data breaches.
• Limited Data Analysis
  The resource constraints or inefficiencies of manual processing enable insurers to perform a fundamental analysis of data related to claims. With manual processing and analysis, insurers fail to identify patterns, trends, potential fraud indicators, etc., which are easily detectable by leveraging automation and intelligent processing tools.

The Need for Intelligent Document Processing in Insurance Claims Handling

Keeping in view the common difficulties, here are some points on how Intelligent Document Processing can benefit insurers and introduce automation in the insurance workspace:

1. Faster Data Accessibility

Intelligent Document Processing systems allow insurers to automatically extract claims-related data from various types of documents, including invoices, medical statements, police reports, and others. This ensures that important claim information is accurately captured from diverse sources.

IDP systems use Optical Character Recognition to convert scanned or printed text into machine-readable content. NLP capabilities enable insurers to understand and interpret the context of the extracted data. This means that massive unstructured data can be retrieved and processed at once. With IDP in place, insurers can accelerate processing times and improve customer satisfaction. Furthermore, IDP systems’ algorithms improve accuracy over time by learning from historical data and user interactions. This reduces the errors and discrepancies in claims processing.

2. Faster Processing of Claims

IDP systems can seamlessly integrate with the insurance company’s claims management system. This enables an automatic population of claim forms and databases with the extracted information. The integration ensures that all relevant data is readily available to claims processors without the need for manual data entry or transfer.

Intelligent Document Processing integration helps in classifying different types of documents, such as accident reports, medical bills, or policy documents, automatically. This ensures that each document is routed to the appropriate department or person for processing without manual sorting. IDP systems can automate various steps in the claims processing workflow, such as data validation, approval routing, and communication with third parties. This supercharges the insurance claims process with automation and minimizes delays.

3. Claims Validation

Claims validation is the process of verifying the accuracy and legitimacy of insurance claims. Insurance companies can define specific validation rules within the IDP system. These rules can include checks for discrepancies, inconsistencies, or suspicious patterns in the extracted data. When the system identifies discrepancies, it can flag them for further review, helping the claim adjuster focus on specific areas of concern.

IDP helps ensure that claims validation processes are consistent and compliant with industry regulations and internal policies. By applying predefined rules and guidelines consistently, the risk of overlooking important validation steps is reduced.

4. Manage Data Complexity

Claims data can be intricate and varied, involving a wide range of documents, formats, and information. IDP helps manage this complexity by simplifying data capture, extraction, and processing while guaranteeing accuracy and consistency. By using IDP, insurers can cross—reference claim data with historical records, policy information, and external data sources. This analysis helps identify complex patterns or relationships within the data. Thereby, facilitating more strategic decision-making on intricate claims.

In addition, IDP tools maintain a detailed audit trail of all actions taken on claims data, ensuring compliance with industry regulations. This is essential for managing the complexity of regulatory requirements associated with claims processing.

5. Integrated Operation

Intelligent Document Processing tools facilitate collaboration by enabling real-time access to shared claims data. Claims adjusters, underwriters, and other stakeholders can collaborate more effectively, making well-informed decisions and expediting the resolution of claims. Automated communication features also ensure that all parties are kept informed throughout the claims process.

6. Resilience and Adaptability

During spikes in claim submissions, such as natural disasters or public health crises, IDP can handle a huge volume of claims without requiring an immediate increase in human resources. This scalability ensures that the insurers can maintain efficient operations during surges in claims.

The regulatory landscape in the insurance sector often changes. IDP systems can be trained and adapted to stay in compliance with evolving regulations by adjusting document recognition and data extraction parameters. This ensures that claims processing remains accurate and compliant.

7. Increased Accuracy

IDP systems leverage OCR, NLP, and Machine Learning to substantially increase the efficiency of claims processing. These technologies are also capable of accurately capturing text, numerical data, and even handwritten information, reducing the risk of manual data entry errors.

IDP ensures that data is consistently extracted and entered into the claims processing system, reducing variations caused by different data entry personnel. This consistency helps insurers maintain a high level of accuracy.

8. Proactive Fraudulent Claims Detection

Intelligent Document Processing employs Machine Learning algorithms to identify anomalies or unusual patterns in claims data. These anomalies may include unexpected values, atypical claim amounts, or unusual claimant behavior. The system can flag these anomalies for further investigation. Natural Language Processing can be used to analyze the narrative descriptions provided in claims documents. Linguistic analysis can identify language patterns or keywords associated with fraud, deceit, or exaggeration.

Conclusion

The future of Intelligent Document Processing insurance holds significant promise, with ongoing advancement and increased adoption. Most initiatives taken by insurers to eliminate manual bottlenecks associated with claims processing through various techniques turned out to be a failure. However, with IDP, insurers can leverage Machine Learning models, NLP, and Computer Vision to process complex claim data of almost any document format. This technology will enable insurance companies to deliver more efficient and customer-focused processing and stay competitive in the market.