Blog

Business Challenges in Manual Data Extraction

Organizations in various industries such as healthcare, finance, insurance, and banking are usually inundated with multiple types of forms such as ACORD forms, W2 forms, and even medical records. These documents are manually processed, which increases chances of human error, decreases productivity, and takes a significant amount of time. The different structures and formats of the forms add to the challenges faced.

Key pain points include:

1. Operational Inefficiencies: Entering data manually frequently cannibalizes time and resources and takes focus away from other more critical business activities.
2. Human Mistakes: Poor data entry because of misinterpretations, omissions, and typographical errors can result in increased downstream inefficiencies or compliance issues.
3. Scalability Limits: It becomes exceedingly difficult to operate business with forms when they are manually processed as the business grows and there is a surge in volume.
4. Data Isolation: A lack of integration with CRM, billing, or policy management systems is achieved courtesy of inconsistent, discrete data management.

Vartik Doc Processor

To address these problem areas, we have developed an innovative Vartik Doc Processor that leverages Generative AI technology. Vartik Doc Processor classify documents and extracts data from scanned or machine-readable Insurance Acord forms like (Acord 25, Acord 130, Acord 125, etc.) and finance, healthcare, banking forms by recognizing relevant data fields, and convert them into structured formats such as JSON or CSV. It then can integrate extracted data for real-time synchronization and updates target systems, including CRM, Sales, and other internal systems, improving workflows and ensuring accurate records.

Below is a schematic representation of how the solution works:

Below are the main features and flow for Vartik Doc Processor:

1. Document Classification
   • The Vartik Doc Classifier divides the incoming documents into defined categories such as ACORD form types, W2s, and medical records.
   • This helps to ensure that extraction logic is tailored to the document types.
2. Intelligent Data Extraction
   • Relevant data points are pulled out and encoded into JSON or CSV.
   • Opensource libraries and LLMs make the system smarter and support it in identifying fields within different styles.
3. React Web Application
   • The user side of the application lets users validate, compare, edit, or make any changes to the extracted data.
   • An audit and history function allows users to view the list of ingestion runs by domain (insurance, finance, etc) with specified metadata like status creation date and last updated date.
   • CRUD Operations: The user is enabled to view, edit, and delete records within the app.
4. Efficient Integration
   • The extracted data can be integrated for real-time synchronization and updates in target systems, including CRM, Sales, and other internal systems.

Business Benefits

1. Enhanced Efficiency:The solution greatly lessens the time invested in data extraction and entry by automating the extraction process. This enables teams to concentrate on more strategic matters rather than tedious administrative duties.
2. Improved Accuracy:The automation of extraction using GenAI drastically reduces human errors, ensuring the data provided to subsequent systems is valid and dependable. This minimizes operational flaws and improves the business's overall integrity.
3. Cost Savings:The adoption of automated data processing renders immense savings due to lowered labor costs and enhanced operational efficiency.
4. Scalability:The improvement in business volumes is effortlessly handled by the solution, allowing organizations to increase their workloads without increasing their workforce.
5. Regulatory Compliance:Precise and coherent data handling makes it easier to adhere to various regulations for the different industries, which get rid of the risk of penalties and damage of image.

The Vartik Doc Processor is a game-changer for industries reliant on document processing. By automating the classification, extraction and integration of data from complex forms, this ensures extraction is accurate and reliable.

About Dilbagh Dhindsa

Practice Head – AI and GenAI

Dilbagh is a hands-on leader in Generative AI, AI/ML engineering, Data Science and software development. With over 20 years of International experience. He has developed groundbreaking AI and Generative AI solutions for global customers that helped solve complex business problems and optimize processes.

He had developed GenAI Accelerators for generating Sections of SoW(Statement of Work) using innovative metadata-driven dynamic chunk mapping. A US patent have been filed for the solution. Other GenAI Solutions included Secure Private GPT, an Email processor for license information, Recruitment tool for matching JD with resumes and chat.