Business Situation
- A sell-side mergers and acquisitions (M&A) focused investment bank wanted to automate and streamline the spread of high volumes of trading and transaction comparables across various sectors.
- The client wanted to avoid manual data extraction and validation to reduce inefficiencies, inconsistencies, and potential errors. The investment bank needed an artificial intelligence (AI)-driven workflow solution that automated data collection, standardized outputs into a predefined template, and integrated seamlessly with existing financial models and dashboards.
SGA Approach
- Data Standardization: Implemented a standardized template to ensure consistency and accuracy of calculations.
- Data Integration: Integrated application programming interface (API)-based automation for seamless connectivity with financial databases such as FactSet, Capital IQ, and internal modeling tools.
- Data Gathering: Developed an AI-powered workflow tool to automate data extraction and validation of trading comparables from multiple data sources.
- Data Processing and Insight Generation: Utilized natural language processing (NLP) and machine learning (ML) models to identify trends highlighting the reasons behind premiums and discounts vs. averages in trading and transaction comparables across sectors and sub-sectors.
- Insight Enhancement: Identified and filled gaps. Corrected mistakes in the analysis provided by the AI tool and reinforced the tool with new findings to make it more robust.
Key Takeaways
- Efficiency: 70% reduction in time spent on manual trading comparables and spreading.
- Bandwidth Optimization: Unlocked 50% of the bandwidth of the onshore team, allowing them to reinvest time into high-impact, client-facing activities.
- Scalability: Developed a long-term, scalable digital asset at an organizational level that can be consistently used across sectors and geographies.
