AI Powered Automated Document Verification and Data Extraction

Automated Document Verification and Data Extraction

1. Document Submission

1.1. Client Interaction

Clients submit required documents through a secure online portal.

1.2. Document Types

Accepted documents include policy applications, claims forms, and supporting documentation (e.g., identification, proof of income).

2. Document Preprocessing

2.1. File Format Conversion

Utilize tools such as Adobe Acrobat or ABBYY FineReader to convert documents into machine-readable formats (e.g., PDF to text).

2.2. Image Enhancement

Implement AI-driven image enhancement tools like Google Cloud Vision to improve document clarity and readability.

3. Data Extraction

3.1. Optical Character Recognition (OCR)

Employ OCR technology using tools like Tesseract or Amazon Textract to extract text from documents.

3.2. Data Validation

Use AI algorithms to verify extracted data against predefined rules and databases. Tools like IBM Watson can assist in this process.

4. Document Verification

4.1. Cross-Referencing

Leverage AI to cross-reference extracted data with existing records in the insurance database to ensure accuracy and consistency.

4.2. Anomaly Detection

Implement machine learning models to identify discrepancies or anomalies in the data, using platforms such as DataRobot.

5. Approval Workflow

5.1. Automated Decision Making

Utilize AI-driven decision-making tools like Salesforce Einstein to automate approval processes based on extracted data.

5.2. Human Oversight

Establish a review process for flagged documents requiring human intervention, ensuring compliance and accuracy.

6. Data Storage and Reporting

6.1. Secure Data Storage

Store verified documents and extracted data in secure cloud solutions like Microsoft Azure or AWS S3, ensuring compliance with data protection regulations.

6.2. Reporting and Analytics

Utilize BI tools such as Tableau or Power BI to generate reports and insights from extracted data, aiding in strategic decision-making.

7. Continuous Improvement

7.1. Feedback Loop

Implement a feedback mechanism to gather insights from users and improve the AI models continuously.

7.2. Model Training

Regularly retrain AI models with new data to enhance accuracy and adapt to changing document formats and regulations.