In the high-stakes realm of private equity, due diligence is the critical pulse point of any deal—a process historically bounded by the limitations of human analysis. One of the world's four largest multinational private equity firms partnered with Sigmoidal to challenge these boundaries. Their goal was to leverage Artificial Intelligence to elevate the due diligence process, enhancing the accuracy and efficiency of their deal analysis.
What was the business objective?
One of the largest publicly traded private equity firms aimed to achieve the following objectives through AI integration in their due diligence process:
- Compliance and Risk Assessment: To deploy AI that could meticulously evaluate compliance across their portfolio companies, assessing risk with a level of depth and precision beyond human capability.
- Information Synthesis: To synthesize vast and varied datasets into a cohesive and interpretable format, enabling the firm to gain a comprehensive understanding of potential investments.
- Information Analysis and Discovery: To analyze complex data structures systematically, utilizing AI to identify key insights that could significantly influence the due diligence outcomes.
- Operational Efficiency: To streamline the due diligence operation, making it more efficient and effective by automating data-intensive tasks.
- Predictive Analytics and Foresight: To incorporate predictive analytics into the firm's due diligence toolkit, allowing for an advanced forecast of trends and outcomes that could impact investment decisions.
How did we accomplish it?
Sigmoidal's approach to constructing this AI-powered due diligence tool was executed in several detailed phases:
Phase 1: Data Compilation and Model Training
- Over 32,000 past deals were analyzed to train the AI tool, creating a vast foundational database from which the machine learning models could learn.
Phase 2: Algorithm Optimization
- The algorithms were refined to identify patterns, anomalies, and trends with precision, focusing on indicators of fraud, financial discrepancies, and risk factors.
Phase 3: Real-Time Analysis Capability Development
- The tool was engineered to process real-time data flows from live deal interactions, allowing for the analysis of thousands of data points within seconds.
Phase 4: Automation and Efficiency Enhancement
- Tasks that traditionally took hours were automated, significantly streamlining the due diligence process and freeing up the firm’s analysts for higher-value work.
Phase 5: Predictive Analytics Integration
- Predictive analytics were incorporated, giving dealmakers foresight into future trends and potential outcomes, strengthening their negotiating position.
Phase 6: Continuous Learning and Adaptation
- The self-learning nature of the AI ensured that with each interaction, the tool's accuracy and effectiveness were amplified, perpetually increasing its value.
The deployment of the AI-powered due diligence tool was nothing short of transformative:
- Within seven days of its full-fledged version release, the tool achieved a remarkable 97% accuracy rate in deal analysis, setting a new industry benchmark.
- The ability to automate and analyze at rapid speeds resulted in a substantial reduction of manual work, translating into significant cost and time savings.
- Predictive analytics enabled dealmakers to anticipate market movements and deal outcomes, providing a strategic advantage in the M&A due diligence process.
- Self-Improving Analytics: The AI system's self-learning algorithms continuously improved with each data interaction, providing increasingly accurate insights and strengthening the firm's due diligence with each subsequent deal.