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Effect of Optical Character Recognition on Insurance Claims Data Accuracy

Abstract

Optical Character Recognition (OCR) technology enables insurance organisations to convert information contained in scanned documents, forms, invoices, medical reports, receipts, and other paper-based records into machine-readable digital data. Accurate claims data are essential for insurance claims processing, actuarial analysis, reserving, pricing, and financial reporting. The use of OCR can improve the speed of data capture and reduce manual entry requirements, but recognition errors may affect the quality and accuracy of information extracted from claims documents. This study will examine the effect of Optical Character Recognition on insurance claims data accuracy. It will assess how the use of OCR technology influences the accuracy of information captured from insurance claims documents and compare OCR-generated data with manually entered claims information. The study will also examine the types and frequency of errors that may arise during automated document recognition and their potential implications for claims data management. The study will focus on Optical Character Recognition, insurance claims data accuracy, automated data capture, claims processing, document digitisation, data extraction, claims records, data quality, recognition errors, and insurance information systems. Statistical and data quality techniques will be used to evaluate the accuracy, completeness, consistency, and reliability of claims information captured through OCR systems. A quantitative research approach will be adopted for the study. Selected insurance claims documents will be processed using OCR technology and the extracted information will be compared with verified source documents and manually entered records. Descriptive statistics, accuracy rates, error frequency analysis, comparative analysis, correlation analysis, and sensitivity analysis will be used to assess differences between OCR-generated and verified claims data. The study will also examine how document quality, text format, and information complexity affect OCR performance. The study is expected to reveal that OCR technology may have a significant effect on insurance claims data accuracy. Effective OCR systems may improve data capture accuracy and reduce errors associated with manual data entry, while poor-quality documents, complex layouts, handwritten information, and unclear characters may increase recognition errors. The magnitude of the effect may depend on document quality, OCR system capabilities, data format, processing procedures, and the level of human verification applied. The study will be useful to insurance companies, claims managers, actuaries, data analysts, information technology professionals, financial managers, regulators, and researchers. It may provide useful information for evaluating automated claims data capture, improving claims processing efficiency, strengthening data quality controls, and reducing errors in insurance records. The findings may also assist insurers in determining appropriate levels of human verification when using OCR technology for claims documentation. The study concludes that Optical Character Recognition is an important technology for improving the digitisation and management of insurance claims data, but its effectiveness depends on the accuracy of information extracted from source documents. It is therefore recommended that insurers implement appropriate OCR validation procedures, regularly assess recognition accuracy, maintain human verification for critical information, and use suitable document-processing standards to improve the reliability of digitally captured claims data.

Keywords: Optical Character Recognition, insurance claims data accuracy, OCR technology, automated data capture, claims processing, document digitisation, data extraction, claims records, data quality, recognition errors, insurance information systems, claims documentation, automated processing, data validation, insurance claims management.