AI Powered Workflow for Fraud Detection in Recorded Statements

Fraud Detection through Recorded Statement Analysis

1. Data Collection

1.1 Recorded Statement Acquisition

Collect recorded statements from policyholders during claims processing. This can be achieved through:

• Phone interviews
• Video calls
• In-person meetings

1.2 Transcription of Recordings

Utilize AI transcription tools to convert audio recordings into text format for easier analysis. Recommended tools include:

• Otter.ai: Provides real-time transcription with speaker identification.
• Rev.ai: Offers accurate transcription services with machine learning capabilities.

2. Data Preparation

2.1 Text Cleaning

Process the transcribed text to remove any irrelevant information, filler words, and background noise references.

2.2 Data Structuring

Organize the cleaned text into a structured format suitable for analysis, including key fields such as:

• Claim number
• Policyholder details
• Statement content

3. AI-Driven Analysis

3.1 Sentiment Analysis

Implement AI algorithms to assess the sentiment of the recorded statements, identifying potential red flags. Tools such as:

• IBM Watson Natural Language Understanding: Analyzes sentiment and emotion in text.
• Google Cloud Natural Language: Provides insights into sentiment and entity recognition.

3.2 Anomaly Detection

Utilize machine learning models to detect anomalies in the statements that may indicate fraudulent behavior. Example tools include:

• Amazon SageMaker: Facilitates building, training, and deploying machine learning models.
• DataRobot: Automates machine learning processes to identify patterns and anomalies.

4. Review and Validation

4.1 Human Oversight

Incorporate a review process where trained analysts evaluate flagged statements for further investigation.

4.2 Fraud Investigation

Conduct in-depth investigations on identified cases, gathering additional evidence and documentation as needed.

5. Reporting and Feedback

5.1 Generate Reports

Create comprehensive reports summarizing findings, including potential fraud cases and recommendations for action.

5.2 Continuous Improvement

Utilize insights gained from the analysis to refine AI models and improve the overall fraud detection process.

6. Implementation of Findings

6.1 Policy Adjustments

Adjust insurance policies and procedures based on trends identified through recorded statement analysis.

6.2 Training and Development

Provide ongoing training for staff on the use of AI tools and the importance of fraud detection.