This blog will walk you through the aspects of automation of insurance claims in the insurance sector and the benefits of intelligent document processing vis-à-vis manual claim processing. It will further guide you on the use of Docsumo in data extraction.
The claim process begins with the reporting of loss and ends with the resolution of the claim. The steps involved are as follows:
Any loss you suffer is reported to the broker acts as an intermediary. All the relevant details, including the photos or videos of the damage, are shared. Accordingly, an adjuster is appointed to take follow-up.
The adjuster shall investigate the reported claim and determine the amount of claim covered by the insurance policy.
After the investigation is completed, the adjuster will review the policy, take expert advice and evaluate the extent of damage that is covered.
The adjuster will arrange payment for the repairs of the loss and update the policyholder about the time to be taken to get it done.
A structured and error-less claims processing face challenges in the name of heavy dependency on manual inputs, inconsistent and non-integrated stages of the process in the application, outdated applications, the cost involved in retrieving the data lost in the task, and changes in the regulatory aspects of the different states.
The right claim processing is an intricate task, as it involves many administrative, managerial, and manual efforts to extract the data required. It requires a complete system around manual labor, forecasting of business, management of databases and a well-organized IT structure to support the processing.
Insurance is a data-driven sector that relies on extensive documentation. With the complexity of documents involved, automation has been a challenge. Some levels of automation used by insurers come with setbacks adding to errors and extra cost.
The policy holder’s claim documents have to go through a series of steps to get validated. It ultimately leads to the settlement of insurance claims. These steps can be as given below:
The holder shall connect to the broker and submit a list of documents. These documents include photos or videos that explain the circumstances of the items that are damaged or lost. It can also contain other relevant documents describing the incident.
The adjuster will carefully examine the documents, take expert advice and form opinions. He will decide upon the amount of damage covered by the policy, identify the liability of the responsible parties and
The paper claim forms are generally unstructured and invalidated. However, the online claims are structured and validated with a proper scan via OCR or otherwise and form part of the claims data store.
The claims data are corrected using the business rules management. It ensures that data is synchronized as per the requirement of the business. However, 85-90% of the documents require manual effort to extract, validate and structure the data. Here only 10-15% of the extraction is automated.
Upon evaluation of damage, the adjuster will further assist regarding settlement of claim and payment. Every claim is different, and the procedure may slightly vary, but the ultimate objective is to get the claim settled for the policyholders.
Intelligent document processing (IDP) is the right solution for extracting data from unstructured documents. It uses artificial intelligence to read unstructured data such as photos of the damage, repair estimates, adjuster’s reports and cull out the relevant data.
The data is validated, stored, and corrected using business rules management. It leads to a reduction of material workload and automation of 85% of documents. It integrates the systems, formats, and data sources relevant to the claim and supports automated claim verification based on configured rules. Also, the reduction in time of complex claim cycle, and 4X increase in process capacity.
Let’s take a look at both one by one:-
The existing claim processing takes claims forms as input. Necessary information is extracted from semi-structured forms manually. The extracted data is sent to claim transaction management for further processing.
A business infrastructure has been built around to make the manual process run smoothly. But it often cites the following drawbacks:
The automation of unstructured data is a complex task and requires human-intensive efforts. If done manually, these unstructured data can be very tricky to handle. However, you can always rely on Intelligent Document Processing claim processing to handle multiple data extraction in one go. There are several reasons why you must choose IDP over others. These are as follows: -
IDP eliminates the manual efforts associated with gathering and validating data. It uses machine learning and multiple Al technologies to handle complex documents.
IDP enables end-to-end automation across insurance operations while overcoming the obstacles of manual claim processing. It integrates all applications and systems without creating any disruption.
IDP yields a high degree of accuracy and is entirely scalable according to the requirement. In the case of IDP software, 99+% of accuracy is achievable.
Where manual claim processing cannot track complex errors and resolution, automated processing introduces easy adaptability over the various segment of the business as it is customizable as per the need of the users.
Automation eradicates repetitive and iterative tasks and increases efficiency by improving efforts, reducing cost and time.
Any manual entry requires a processing time of up to 15+ minutes for a single document. Even a traditional OCR takes about 5 minutes to extract the data. However, IDP solves these issues and makes sure that a specific document gets processed in less than 2 minutes.
Docsumo streamlines the claims processing, which induces the processing of documents in bulk. It means the process gets simplified and contributes to over 99%+ field-level accuracy. Some other benefits of using Docsumo include:
Docsumo comes with pre-trained data capture APIs for RS tax forms, Acord forms, bank statements, utility bills, driving licenses, and other claim documents.
It guides you to send countless data through APIs & webhooks. You can analyze the captured data with the help of our dashboard tools. After necessary changes, you can validate your API and Database accordingly.
Docsumo ensures that no wrong data passes through its verification. It applies to excel like a formula to validate co-dependent extracted data within a document.
Docsumo offers 95%+ STP for most documents which means that you would not get stressed out to even look at 95% of your task. With end-to-end processing, Docsumo covers the rightful approach to completing your data extraction job from PDF and image.
During manual entry, operational accuracy and speed are achievable to target the elimination of human error as sighted. Docsumo provides over 99%+ field-level accuracy. It helps to reduce the time required to process the claim.
The user can enter a large volume of data into the Docsumo software. In the return process, the documents are in bulk.
Batch uploads of the file help users bid goodbye to single file uploading. These multiple document uploads help users choose whichever file they need to upload. Smart filters can guide you through an easy data extraction process.
Docsumo robust API provides seamless integration with most enterprise software and platforms. It guides the automation of manual tasks across the enterprise.
Multiple types of document formats require a new business that cannot be handled by traditional OCR. Most initiatives to remove manual bottlenecks through various automation initiatives turned out to be a failure. Here, Docsumo combines AI, machine learning models, and computer vision to process complex structures of almost any document type.
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.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.