The global insurance sector covers health, life, automotive, property, professional and business-related insurance, and claims processing services. Documentation forms an integral part of the insurance sector. Millions of documents are processed daily across the sector which is in the form of paper or digital copies and often is handwritten with several documents having official approvals. From onboarding documents, till the customer claims are processed, several types of documents are used in the insurance process such as the policy submission form with proof, benefits enrollment, claims, and policy updates documents.
The insurance sector possesses critical information in the form of policy-holders documents. These documents are required to be protected and encrypted so that any external manipulations can be avoided. In this direction, AI-based intelligent document processing is proving highly beneficial and reducing laborious procedures to keep track of individual policies, and improving customer experience with the insurance companies by reducing turn-around time to customers.
AI-enabled Intelligent Document Processing
Artificial Intelligence has turned the table around. With machine learning trained models, AI, today is paving the way for processing through multiple channels. Equipped with cloud storage-enabled storage, insurance documentation has been fully automated. AI programs for the insurance sector can pull documents from multiple channels, extract information and categorize them according to insurance priorities and regulations as per customer insurance lifecycle within a secured ecosystem. Customer documents are stored in a secure way with limited access.
For example, in relation to claims processing and management, AI is helping in getting hold of claims routing, claims triage, audit, and detection of fraud in claims. While, in terms of operability, the AI programs follow a comprehensive approach towards intelligent document processing involving these steps:
Step 1: Data received and is classified as per the neural architecture of the AI
Step 2: Data extraction is carried out with machine learning models; performing data validation and verification
Step 3: Secure data export for multiple use cases in customer insurance
For multiple use cases like underwriting and first notice of loss while the data is validated with industry-standard procedures having dictionaries or databases with ICD codes, DMV info, and provider data information, complying with the EDM or Electronic Data Incharge. Concurrently, deep neural networks are being utilized to improve the data ecosystems by enabling the AI application to convert the unstructured data, extract the information and auto-classify it for further processing. The procedure renders a 96% accuracy rate in claims processing, augmenting customer trust and loyalty and shrinking overall costs spent on document maintenance.
Fraud Detection with Document Management Processing
Aside from procedural improvement and ecosystem efficiency, fraud detection in the insurance sector is considered an impending threat. While common personal and health insurance includes inflation of service costs and coverage of deductibles from the insurance, the auto insurance segment is prone to more frequent fraudulent insurance claims than any other. From the false reports submissions of stolen vehicles to false claims for physical damage to vehicles, insurers try to take advantage of a fiasco by often over-estimating damage.
Herein, AI has significantly added a layer of checks for insurance providers seekers. With Artificial Intelligence applications enabled with data of document annotation, insurance firms are now able to check the credibility of the insurance claim and figure out if the claim matches the regulations and policies established by the state and federal governments. Also, the identity of the claimant is analyzed using trained data used by the AI app, before the actual claim procedure is carried out.
Powering intelligent document processing across levels in the insurance sector, Artificial Intelligence has proven as a boon for internal operational efficiency and boosting investor confidence once again. It has broken several operational barriers in the insurance sector which have made it susceptible to fraud and also false information for years. Trained with machine learning models and equipped to interpret and follow the industry, the sector has come of age.
At the helm are more changes in the sector which can accelerate claims of customers who have been diligent in their claims and seeking services that offer maximized advantage. This clear bifurcation clears pathways for the long-term growth of the sector as a whole, proving beneficial for stakeholders involved.