AI-Powered Claims Processing: Reducing Cycle Times and Improving Accuracy
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P113Keywords:
Artificial Intelligence, Claims Processing, Insurance, Machine Learning, Natural Language Processing, Robotic Process Automation, Optical Character Recognition, Fraud Detection, Predictive AnalyticsAbstract
The insurance industry is characterized by huge volume of claims processed which in most instances leads to inefficiencies, lag and error due to manual processing. Artificial Intelligence (AI) was introduced, which automated many potent processes, increased the efficiency of decision-making, and reduced the cost of operations. The paper gives an overview of AI-based claims processing system and its ability to cut down on cycle times, improve accuracy and customer satisfaction. The AI means that simplify the claims workflows, extract, confirm, detect fraud, and predict the processes of the data are Natural Language Processing (NLP), Machine Learning (ML), Optical Character Recognition (OCR), and Robotic Process Automation (RPA). The paper is a discussion of AI implementation in insurance claims with technical implementation and implications of operations in mind. The procedure of modifying the existing claims procedures to incorporate the AI is recommended with a methodology of incorporating data preprocessing, model development, system deployment, and performance evaluation. Also, the study offers a comparative analysis of the traditional manual processing and AI-supported workflows with the emphasis on the decrease in the processing time, the rise in the accuracy rates, and the possibility to process an increased number of claims. Some examples of the case studies of the insurance companies leaders are reviewed in order to determine the value of AI applications. Challenges related to the implementation of AI that the paper includes are the privacy of data, adherence to regulation, and interpretability of the system. Methods to address these issues such as data anonymization, compliance auditing and explainable AI models are addressed. Also, the analysis is focused on new trends, including AI-based customer care chatbots, predicting the severity of claims, and combining with Internet of Things (IoT) devices to validate claims in real-time. Hard numbers prove AI-based systems can save up to 50 percent of the average time spent in claim processing and 30 to 40 percent in terms of accuracy than the traditional ones. The precision and recall, F1-score, and mean processing time are used to measure AI models, the results of which are provided in tabular and graphical formats. Finally, the paper concludes by outlining the prospects of the AI in the future in claims processing, and advises where research can be conducted to investigate deep learning, real-time analytics and cognitive automation. The research article validates the premise that artificial intelligence is a disruptive technology within the insurance claims industry, and it brings efficiency, accuracy and scalability
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