Automated Trade Reconstruction Workflow with AI Integration

Automated Trade Reconstruction from Voice Records

1. Data Collection

1.1 Voice Recordings

Collect voice recordings of trading conversations from various communication channels, including phone calls and voice messages.

1.2 Metadata Gathering

Capture relevant metadata associated with each voice recording, such as timestamps, participant information, and trade details.

2. Speech Recognition

2.1 AI Speech-to-Text Conversion

Utilize AI-driven speech recognition tools, such as Google Cloud Speech-to-Text or IBM Watson Speech to Text, to convert voice recordings into text format.

2.2 Accuracy Enhancement

Implement advanced natural language processing (NLP) techniques to enhance the accuracy of transcription and reduce errors.

3. Data Processing

3.1 Text Analysis

Employ AI algorithms to analyze the transcribed text for key trading terms, phrases, and contextual information relevant to trade reconstruction.

3.2 Entity Recognition

Use Named Entity Recognition (NER) tools to identify and categorize entities such as stock symbols, trade amounts, and participant names.

4. Trade Reconstruction

4.1 Trade Matching

Cross-reference extracted information with existing trade records in the trading system to match voice data with actual trades.

4.2 Anomaly Detection

Implement AI-driven anomaly detection algorithms to identify any discrepancies or unusual patterns in trade reconstruction.

5. Reporting and Compliance

5.1 Automated Reporting

Generate automated reports summarizing reconstructed trades, highlighting any anomalies for compliance review.

5.2 Regulatory Compliance

Ensure that the reconstructed trades meet regulatory requirements by integrating compliance checks using tools like NICE Actimize or AxiomSL.

6. Continuous Improvement

6.1 Feedback Loop

Establish a feedback mechanism to continuously improve the accuracy of speech recognition and trade reconstruction processes based on user input.

6.2 AI Model Training

Regularly update AI models with new data to enhance performance and adapt to changing market conditions and trading terminologies.