Insurance claims processing deals with the extraction and processing of data from a variety of documents and requires employees to go through intricate details. Automating claims processing has been a challenge faced by companies because of the level of complexity involved in processing insurance documents. However, intelligent document processing using claims automation software is revolutionizing the sector and changing that by leveraging OCR and AI models to scan and interpret information from documents.
Best claims automation solutions are tailored to scale up with businesses, meet varying requirements, and improve overall customer experiences. Most of these platforms are capable of processing a wide variety of documents and do not have any limits to the number of claims they can process. Below we give an overview of the top claims automation software solutions for businesses and more.
The types of documents processed for different insurance claims processing:-
Your insurance agent may request for additional details such as personal information, driver’s licenses, assets you own, and income status depending on the type of insurance policy you are applying for. The coverage and limits you obtain will depend on the basis of the documents you furnish.
Insurance claims automation software helps agents automate workflows, evaluate, and manage insurance claims when processing them. Claims automation solutions can help businesses reduce operational costs, eliminate document fraud, and ensure data compliance, thereby enhancing the whole customer experience.
The best claims processing automation software used by the insurance industry are:-
Docsumo is an Ai platform that uses smart machine learning to automate insurance claims processing tasks. Using machine learning and intelligent OCR technology, it extracts data from unstructured documents, giving them meaning, making it easier to analyze and process claims. Policyholders enjoy automated Cloud backup and data recovery when processing their insurance documents with Docsumo. Any number of documents that can be processed in a day without compromising on accuracy and processing time. Docsumo is compatible with multiple document types, and extracted data can be downloaded in JSON, Excel, or CSV.
Abby Flexicapture uses precision data extraction technology to automate data capture from health and medical insurance claims. The software streamlines claims entry processes by using various automated validation rules for processing insurance data. It also gives organizations greater visibility into their documentation and the platform supports all standard claim forms such as claim attachments, HCFA/CMS-1500, and UB-04/CMS-1450.
Amazon textract is a special service that uses machine learning algorithms to extract handwritten data from scanned documents, PDFs, and other file types. It is able to pull elements like tables, forms, columns, and other important areas from documents for insurance claims processing. Unlike traditional OCR solutions that require constant reconfigurations for processing different insurance forms, Amazon textract’s engine is designed to be self-learning which means it adapts to various document layouts automatically and structures information.
Kofax RPA technology uses machine learning automation to eliminate repetitive tasks involved in insurance claims filing. It accelerates payments, helps in managing customer subscriptions, and provides increased visibility across operations via business analytics. Policy holders can track the status of their claims any time, and customize their communication preferences with vendors on the platform.
Hyperscience lets users parse unstructured documents and automate claims processing tasks such as underwriting, claims filing, accounting, and enrolments in organizations. It can automatically classify and validate submitted user data against master databases and submit it to insurance agents for further review. For incomplete packets of data or missing details, it forwards tasks to an exception handling system.
Pick your option for the one that suits your requirements best. By doing so, you’d be eliminating the need for manual data entry in your system and replacing it with intelligent document processing which is 95%+ accurate and 10 times faster than your current setup. Claims automation processing can streamline insurance workflows and make it easier for agents to manage documentation, deal with clients, and give refunds.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.