Business Situation
- A mid-market investment bank specializing in deal origination and target identification wanted to reduce the time required for target shortlisting and profiling to increase their deal volumes and conversion.
- The client needed a scalable, efficient method to identify potential mergers and acquisitions (M&A) among the investment targets from multiple sources such as databases, filings, and third-party reports. The process transformation necessitated using automation components to materially reduce manual effort and improve accuracy and consistency, prompting the investment bank to seek decision intelligence partners.
SGA Approach
- Data Aggregation: Implemented artificial intelligence (AI)-driven data collection tools to aggregate and process financial and market data on a broad list of companies.
- Company Profile Creation: Identified the top 20 insights required to analyze targets and created AI workflows to extract insights in these specific areas.
- AI-Led Insight Generation: Utilized natural language processing (NLP) and machine learning (ML) models to analyze qualitative and quantitative data for deeper insights.
- LLM Reinforcements: Continuously refined AI models using reinforcement learning to improve the accuracy and relevance of insights.
Key Takeaways
- Efficient Target Selection: 60% less time spent on target identification.
- Faster Revenue Growth: Improved deal conversion to 65% due to richer insights, early-stage intelligence, and enhanced target relevance.
- Automation Impact: Reduced time to create company profiles by 50% while ensuring consistent deal evaluations.
