AI for insurance underwriting for predictive insured data analytics
AI For Insurance Underwriting
Underwriting involves review and judgements from large volume and variety of policy documents, illustrations, EMR data and accumulation of unstructured text insured information and compliance data from multiple carriers. Time to explore and depend on technologies like text analytics and Natural Language Processing (NLP) to extract meaningful classification, modelling, duplicate detection and fraud detection analysis.
Difficult to manually review and classify open-ended EMR data from multiple brokers and carrier platforms and internal Case Management systems across the enterprises. The manual reading, understand and classify text need to have someone to read the text, note the contents, extract important information out of thousand of pages of the case. However, this is not scalable to the real-life millions of data generating every day.
Using AI and Natural Language Processing (NLP) and predictive insured data analytics, trained models leading to the accuracy of insurance underwriting. AI guided underwriting largely depends on extraordinarily complex process, analyzing multiple data sources EMR, optical character recognition or OCR technologies, recognizing image, face, character and AI trained models and algorithms are building blocks to build AI driven underwriting systems.
KEY CUSTOMER BENEFITS
Large volume of Electronic Medical Records or EMR are used for data modelling, classification, defining and maturing rules for pattern match, which are more than static but trained models leading to the accuracy of insurance underwriting. AI guided underwriting largely depends on extraordinarily complex process, analyzing multiple data sources EMR, optical character recognition or OCR technologies, recognizing image, face, character and AI trained models and algorithms are building blocks to build AI driven underwriting systems.
Risk less case processing
Processing cases with AI trained models, that helps quick application quality check, saves time of underwriters, without manual review errors, ensuring 100% extraction of all medical history and examination reports, inspection reports, information from the Medical Information Bureau.
Efficient EMR Processing
A rules-based workflow, using Artificial Intelligence for managing and supporting Electronic Medical records, physician statements, Lab reports, review, extractions, cross checking, dramatically reduce operational costs, increase efficiency.
Trained Actuarial Tables
Actuarial Tables are key for efficient underwriting case processing, using AI trained models-based training of actuarial life expectancy tables increase in overall process efficiency and productivity.
Trained document models and AI based cross checking of insured records leading to the accuracy of insurance underwriting process with major benefits like fraud detection, duplicate detection, drill down and review during the processing and review stages, automated report generation for duplicate and fraud detection.