Empowering Investors withObjective Financial Analysis
We're on a mission to eliminate bias from investment decision-making by providing AI-powered tools that reveal hidden manipulation in financial reporting.
Meet Our Founder
Om Patel
Om founded BiasBust after experiencing firsthand how biased financial reporting led to poor investment decisions. With a background in AI and machine learning, he recognized the opportunity to use technology to level the playing field for retail investors.
"Every investor deserves access to objective, unbiased analysis. Traditional financial media is riddled with conflicts of interest and promotional language that misleads everyday investors. Our AI cuts through the noise to reveal the truth."
To democratize access to objective financial analysis by providing AI-powered tools that detect bias, manipulation, and misinformation in financial reporting. We believe every investor deserves the truth, not spin.
A world where investment decisions are based on facts, not hype. Where retail investors have the same access to objective analysis as institutional investors. Where bias is transparent and accountability drives better journalism.
Annual Losses
Estimated losses by retail investors due to bias-influenced decisions
Biased Articles
Percentage of financial articles containing promotional language
Struggle to Identify
Investors who can't identify bias in financial reporting
Common Types of Financial Media Bias:
- Promotional language disguised as analysis
- Cherry-picked data and selective timeframes
- Emotional manipulation and fear-based headlines
- Undisclosed conflicts of interest
- Certainty claims without supporting evidence
- Omission of relevant context or risks
BiasBust uses advanced natural language processing and machine learning to analyze financial content across multiple dimensions of bias. Our platform provides:
Language Bias Detection
Identifies promotional language, emotional appeals, and certainty claims without evidence
Sentiment Analysis
Quantifies positive/negative sentiment with confidence intervals using FinBERT models
Source Credibility
Rates publications based on historical accuracy and potential conflicts of interest
Framing Analysis
Detects cherry-picking, selective timeframes, and omission of relevant context
Real-time Monitoring
Track bias trends across your watchlist with customizable alerts and notifications
Actionable Insights
Get specific recommendations on how detected biases might affect your investment decisions
Detection Accuracy
Our AI models achieve industry-leading accuracy in bias detection
Sources Tracked
Comprehensive database of financial publications and analysts
Real-time Analysis
Continuous monitoring and instant bias detection
Our Technology Stack:
- • Advanced Natural Language Processing (NLP)
- • FinBERT sentiment analysis models
- • Machine learning bias classification
- • Real-time data processing pipelines
- • Continuous model training and improvement
- • Secure cloud infrastructure
Our Core Values
We believe in complete transparency about our methods, limitations, and potential conflicts of interest.
We continuously improve our models and validate our results to ensure the highest accuracy possible.
Professional-grade analysis tools should be available to all investors, not just institutions.