Fraud Detection Dashboard will provide VC and Angel Investors with a comprehensive and data-driven tool to evaluate the trustworthiness of founders.
This product documentation manual and all included code pieces are subject to contributors' review and/or optimization.
The Product back story resulted from a "satirical joke" that was proven by research and the discussion between me, a Data Scientist friend of mine, and a VC GP.
While the product itself and that repo fall under Boost Software license 1.0.
Invitations for contributions are open for whoever wanna join for the love of a good "Data Meme"!
Proposed PRD for Fraud Detection Dashboard
1. Metrics and Scoring Mechanism
The Fraud Detection Dashboard will use a comprehensive scoring mechanism to evaluate the trustworthiness of founders based on the following dimensions:
Dimension 1: Historical Fraud Cases
Metrics:
- Number of previously listed founders associated with fraudulent companies (Quantitative)
- Types of fraud committed by these founders (Quantitative)
- Duration of fraudulent activities and the companies involved (Quantitative)
- Legal consequences and penalties faced by fraudulent founders (Quantitative)
Scoring Mechanism:
- Assign weights for each metric based on its importance in fraud detection (e.g., 0.2 for the number, 0.3 for types of fraud, 0.2 for the duration, and 0.3 for legal consequences).
- Calculate the scores for each metric based on historical data (e.g., using Excel formula or Python script).
- Calculate the overall score for this dimension as the weighted sum of individual scores.
Dimension 2: Background Checks
Metrics:
- Detailed background checks on founders, including previous business ventures and roles (Qualitative)
- Verify educational qualifications and work experience (Qualitative)
- Investigate any discrepancies or inaccuracies in their provided information (Quantitative)
Scoring Mechanism:
- The background checks will be qualitatively assessed by experts, assigning scores based on the level of thoroughness and completeness (e.g., 0 for incomplete, 50 for partially complete, and 100 for thorough).
- Discrepancies and inaccuracies will be quantitatively scored based on severity (e.g., 0 for no discrepancies, 50 for minor discrepancies, and 100 for major discrepancies).
- Calculate the overall score for this dimension based on the qualitative and quantitative data.
Dimension 3: Regulatory Compliance
Metrics:
- Assess the founders' compliance with industry regulations and legal requirements (Quantitative)
- Check for any regulatory violations or sanctions against the founders or their previous companies (Quantitative)
Scoring Mechanism:
- Assign weights to each metric based on the impact of regulatory compliance on trustworthiness (e.g., 0.5 for compliance and 0.5 for violations).
- Calculate the scores for each metric based on historical data or regulatory records.
- Calculate the overall score for this dimension as the weighted sum of individual scores.
Dimension 4: Industry Reputation
Metrics:
- Analyze the reputation of the industries in which the founders have operated (Quantitative)
- Identify patterns of fraudulent activities specific to certain industries (Quantitative)
Scoring Mechanism:
- Assign weights to each metric based on the industry's impact on trustworthiness (e.g., 0.6 for reputation and 0.4 for fraud patterns).
- Calculate the scores for each metric based on historical data and industry-specific information.
- Calculate the overall score for this dimension as the weighted sum of individual scores.
Dimension 5: Financial Anomalies
Metrics:
- Detect unusual financial patterns or discrepancies in the founders' financial statements (Quantitative)
- Look for evidence of inflating revenues, hiding debts, or misleading investors (Quantitative)
Scoring Mechanism:
- Assign weights to each metric based on the severity of financial anomalies (e.g., 0.4 for patterns and 0.6 for misleading practices).
- Calculate the scores for each metric based on financial data and anomaly detection methods.
- Calculate the overall score for this dimension as the weighted sum of individual scores.
Dimension 6: Corporate Governance
Metrics:
- Evaluate the corporate governance structure of the founders' previous companies (Quantitative)
- Assess the effectiveness of internal controls and oversight mechanisms (Quantitative)
Scoring Mechanism:
- Assign weights to each metric based on their impact on trustworthiness (e.g., 0.6 for governance structure and 0.4 for internal controls).
- Calculate the scores for each metric based on corporate governance assessments.
- Calculate the overall score for this dimension as the weighted sum of individual scores.
Dimension 7: Media and Public Perception
Metrics:
- Monitor media coverage and public perception of the founders and their businesses (Quantitative)
- Look for any discrepancies between public perception and the reality of the company's performance (Quantitative)
Scoring Mechanism:
- Assign weights to each metric based on their impact on trustworthiness (e.g., 0.5 for media coverage and 0.5 for perception discrepancies).
- Calculate the scores for each metric based on media analysis and sentiment analysis.
- Calculate the overall score for this dimension as the weighted sum of individual scores.
Dimension 8: Whistleblower Reports
Metrics:
- Investigate any past or current whistleblower reports related to the founders or their companies (Quantitative)
- Analyze the validity and credibility of these reports (Quantitative)
Scoring Mechanism:
- Assign weights to each metric based on their impact on trustworthiness (e.g., 0.6 for reports and 0.4 for credibility).
- Calculate the scores for each metric based on whistleblower report analysis.
- Calculate the overall score for this dimension as the weighted sum of individual scores.
2. Success Metrics
Business Metrics:
- Increased confidence in investment decisions for VC and Angel Investors.
- Reduced exposure to potential fraud and fraudulent founders.
- Improved portfolio performance through informed investment choices.
Technical Metrics:
- Dashboard uptime and availability (99%+).
- Dashboard response time (less than 2 seconds).
- Data accuracy and completeness (95%+).
- User engagement and adoption rates (weekly active users, number of logins, etc.).
3. Instructions for Data Processing and Display
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Search and Aggregate Data: Use web scraping and APIs to gather data from Forbes' top listings, financial databases, social media, news sources, regulatory bodies, and whistleblower platforms. Aggregate the data in a centralized database.
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Data Cleaning and Preprocessing: Clean and preprocess the collected data to remove duplicates, handle missing values, and ensure consistency. Validate and standardize the data format for each dimension.
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Data Analysis: Calculate scores for each dimension based on the proposed scoring mechanism using Python or similar tools. Apply sentiment analysis and anomaly detection algorithms to relevant quantitative data.
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Data Visualization: Create interactive and visually appealing charts and graphs to display the scores and provide clear insights for VC and Angel Investors. Use libraries like Matplotlib, Seaborn, or Plotly to visualize the data.
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Dashboard Design: Organize the data into user-friendly dashboards with an intuitive user interface. The dashboard should have clear sections for each dimension, displaying scores, red flags, and detailed profiles of founders.
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Real-time Data Update: Implement a mechanism to update the data and scores in real-time to ensure the latest information is available to users.
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Exporting Reports: Provide an option to export due diligence reports for founders of interest in downloadable formats (e.g., PDF, CSV). The report should include all relevant data points, metrics, and scores for the selected founder.
4. Weightings and Score Ranges
The weightings for each dimension are assigned based on their relative importance in the trustworthiness evaluation. They can be adjusted based on user feedback and historical performance of the dashboard.
- Historical Fraud Cases: Weight - 20%
- Background Checks: Weight - 15%
- Regulatory Compliance: Weight - 12%
- Industry Reputation: Weight - 10%
- Financial Anomalies: Weight - 12%
- Corporate Governance: Weight - 10%
- Media and Public Perception: Weight - 10%
- Whistleblower Reports: Weight - 11%
Score ranges for each dimension are from 0 to 100, where 0 indicates high risk or low trustworthiness, and 100 indicates low risk or high trustworthiness.
Highest and Lowest Scores in Each Dimension
- Historical Fraud Cases: Lowest Score - 0, Highest Score - 100
- Background Checks: Lowest Score - 0, Highest Score - 100
- Regulatory Compliance: Lowest Score - 0, Highest Score - 100
- Industry Reputation: Lowest Score - 0, Highest Score - 100
- Financial Anomalies: Lowest Score - 0, Highest Score - 100
- Corporate Governance: Lowest Score - 0, Highest Score - 100
- Media and Public Perception: Lowest Score - 0, Highest Score - 100
- Whistleblower Reports: Lowest Score - 0, Highest Score - 100
Formulas to Measure Scores in Excel and Python
1. Historical Fraud Cases
Metrics:
- Number of previously listed founders associated with fraudulent companies (Quantitative)
- Types of fraud committed by these founders (Quantitative)
- Duration of fraudulent activities and the companies involved (Quantitative)
- Legal consequences and penalties faced by fraudulent founders (Quantitative)
Excel Formula:
- Let N be the number of previously listed founders associated with fraudulent companies.
- Let T be the total number of founders listed in Forbes' top rankings.
- Score for the number of founders associated with fraudulent companies: = (N / T) * 100
Python Script:
def calculate_historical_fraud_score(N, T):
return (N / T) * 1002. Background Checks
Metrics:
- Detailed background checks on founders, including previous business ventures and roles (Qualitative)
- Verify educational qualifications and work experience (Qualitative)
- Investigate any discrepancies or inaccuracies in their provided information (Quantitative)
Excel Formula:
- Score for detailed background checks (qualitative): Assign 0 for incomplete, 50 for partially complete, and 100 for thorough.
- Score for discrepancies or inaccuracies (quantitative): Assign 0 for no discrepancies, 50 for minor discrepancies, and 100 for major discrepancies.
Python Script:
def calculate_background_check_score(qualitative_score, quantitative_score):
return (0.5 * qualitative_score) + (0.5 * quantitative_score)3. Regulatory Compliance
Metrics:
- Assess the founders' compliance with industry regulations and legal requirements (Quantitative)
- Check for any regulatory violations or sanctions against the founders or their previous companies (Quantitative)
Excel Formula:
- Score for compliance with regulations: Assign 0 for non-compliant, 50 for partially compliant, and 100 for fully compliant.
- Score for regulatory violations: Assign 0 for no violations, 50 for minor violations, and 100 for major violations.
Python Script:
def calculate_regulatory_compliance_score(compliance_score, violations_score):
return (0.5 * compliance_score) + (0.5 * violations_score)4. Industry Reputation
Metrics:
- Analyze the reputation of the industries in which the founders have operated (Quantitative)
- Identify patterns of fraudulent activities specific to certain industries (Quantitative)
Excel Formula:
- Score for industry reputation: Assign 0 for poor reputation, 50 for average reputation, and 100 for excellent reputation.
- Score for fraud patterns in industries: Assign 0 for low incidence, 50 for moderate incidence, and 100 for high incidence.
Python Script:
def calculate_industry_reputation_score(reputation_score, fraud_patterns_score):
return (0.6 * reputation_score) + (0.4 * fraud_patterns_score)5. Financial Anomalies
Metrics:
- Detect unusual financial patterns or discrepancies in the founders' financial statements (Quantitative)
- Look for evidence of inflating revenues, hiding debts, or misleading investors (Quantitative)
Excel Formula:
- Score for financial anomalies: Assign 0 for no anomalies, 50 for minor anomalies, and 100 for major anomalies.
- Score for misleading financial practices: Assign 0 for no misleading practices, 50 for minor practices, and 100 for major practices.
Python Script:
def calculate_financial_anomalies_score(anomalies_score, misleading_practices_score):
return (0.4 * anomalies_score) + (0.6 * misleading_practices_score)6. Corporate Governance
Metrics:
- Evaluate the corporate governance structure of the founders' previous companies (Quantitative)
- Assess the effectiveness of internal controls and oversight mechanisms (Quantitative)
Excel Formula:
- Score for corporate governance structure: Assign 0 for weak governance, 50 for moderate governance, and 100 for strong governance.
- Score for internal controls: Assign 0 for weak controls, 50 for moderate controls, and 100 for strong controls.
Python Script:
def calculate_corporate_governance_score(governance_score, controls_score):
return (0.6 * governance_score) + (0.4 * controls_score)7. Media and Public Perception
Metrics:
- Monitor media coverage and public perception of the founders and their businesses (Quantitative)
- Look for any discrepancies between public perception and the reality of the company's performance (Quantitative)
Excel Formula:
- Score for media coverage: Assign 0 for negative perception, 50 for mixed perception, and 100 for positive perception.
- Score for perception discrepancies: Assign 0 for no discrepancies, 50 for minor discrepancies, and 100 for major discrepancies.
Python Script:
def calculate_media_perception_score(media_score, perception_discrepancies_score):
return (0.5 * media_score) + (0.5 * perception_discrepancies_score)8. Whistleblower Reports
Metrics:
- Investigate any past or current whistleblower reports related to the founders or their companies (Quantitative)
- Analyze the validity and credibility of these reports (Quantitative)
Excel Formula:
- Score for whistleblower reports: Assign 0 for no credible reports, 50 for partially credible reports, and 100 for fully credible reports.
- Score for credibility analysis: Assign 0 for no credible reports, 50 for partially credible reports, and 100 for fully credible reports.
Python Script:
def calculate_whistleblower_reports_score(reports_score, credibility_score):
return (0.6 * reports_score) + (0.4 * credibility_score)Overall Trust Probability Score
Excel Formula:
- Calculate the weighted sum of all dimension scores to obtain the overall Trust Probability Score.
Python Script:
def calculate_overall_trust_probability_score(scores_list, weights_list):
return sum([score * weight for score, weight in zip(scores_list, weights_list)])Metrics to Measure Success
Business Metrics:
- Increased confidence in investment decisions for VC and Angel Investors.
- Reduced exposure to potential fraud and fraudulent founders.
- Improved portfolio performance through informed investment choices.
- Number of successful investments after using the Fraud Detection Dashboard.
Technical Metrics:
- Dashboard uptime and availability (99%+).
- Dashboard response time (less than 2 seconds).
- Data accuracy and completeness (95%+).
- User engagement and adoption rates (weekly active users, number of logins, etc.).
- Number of reported fraudulent cases correctly identified by the dashboard.
- Time taken to update data and scores in real-time.