Harnessing Artificial Intelligence to enhance the mobile insurance claims management process

Kandregula, Prasanna Kumar (2025) Harnessing Artificial Intelligence to enhance the mobile insurance claims management process. International Journal of Science and Research Archive, 15 (2). pp. 712-722. ISSN 2582-8185

There's a good deal of change in the world of insurance. The growth of the Digital Era has created open doors for technology-driven insurance claims management mechanisms. At the same time, mobile technology and AI have been used to be shared within the insurance industry. Tradition has undergone great change: one could not have imagined nor even envisioned any sort of internet-based and smartphone interfaces. The digital presence is improving the competence, transparency, and promptness of the claim’s management process in insurance. Traditional backlogs-sorry for saying it: whining about paper documentation, physical verification, not keeping commitments, and complete unresponsiveness to the insured's claims-have brought trouble to the industry; particularly in the non-adhesive insurance sector, it leads to customer dissatisfaction and fineness. Customers have become increasingly impatient for those on-demand or real-time operations they can get through mobiles, which mandate insurance companies to restructure claims-filing processes so as to fit their expectations and without diverging into numerous regulatory violations or risk intents. This research paper introduces a prominent AI-driven mobile insurance claims management frame that holds a range of sophisticated machine learning models, computer vision methods, and natural language processors entirely for a user-friendly mobile app. These design efforts come to help enable policyholders to submit claims through their smartphones using structured and unstructured data types-which could be in the form of images, videos, voice commands, or potentially narrative-type description texts. Essentially, Optical Character Recognition is utilized to digitalize and extract information from receipts and checks, while and CNNs extract visual damage from accident photos to help with Claims assessment. For the enhancement of the robustness and accuracy of claims adjudication, the system involves AI fraud detection mechanisms using past empirical data and anomaly detection techniques. An MVP mobile app was therefore rolled out using a cloud-native architecture and AI microservices while hosted on scalable infrastructure. Experimental validation was carried out on not-identified datasets struggle from the data of insurance providers as listed in the automobile and health insurance sectors. Metrics evaluated during experimentation comprised claim-processing duration, data extraction precision, fraud-detection accuracy, and user-simulation workflow satisfaction. Outcomes grasped from the vs. standard operating process showed that the AI-driven system brought about a 60% reduction in the average duration of the claims process, 35% improved accuracy in fraud detection, and 45% improved user satisfaction. The AI facets would underline that at the level of different insurance verticals, the great strength seen in their adaptability, thereby bringing the possibility of exploitation to full fruition. This paper makes a stunning contribution to the continuum of the InsurTech theme by examining an instance of how AI and mobile technologies can be brought together to offer practical, scalable, and efficient responses to the traditional obstacles to claims resolution. The study also brings forth some key considerations for implementation; these embrace interpretability of models, ethical policing of AI usage, data privacy regulations, and possibly how these systems are being integrated into legacy systems. In order to maintain their competitive edge in an ever-evolving digital economy, insurers ought to implement mobile-software applications backed up by AI for faster work on claims processes, coming naturally from the drive for accomplishing greater operational efficiency, along with adopting measures to reduce the propagation of fraud and increase trust in their insureds