Business Situation
A US-based asset management firm sought to develop detailed financial models with forecasts and valuations for 200 global firms across industries.
The client wanted models that were vital to incorporating a consensus view, the option to adjust for corporate actions, semi-automated pre/post-earnings analysis, and customizations for analysts’ assumptions.
It required automated data aggregation from multiple sources and semi-automated data processing, enhanced by human intervention to ensure relevance, accuracy, and accountability across investment recommendations.
SGA Approach
- Sector-Based Classification: Categorized the identified 200 companies using an artificial intelligence (AI)-assisted industry mapping tool validated by human analysts.
- Automated Historical Data Population: Created database-linked automated templates for standard financial data and leveraged an in-house data extraction tool to populate industry-specific drivers’ data in the models.
- Semi-Automated Commentary Generation: Developed an algorithm to analyze numbers and ratios across a standard format, generating automated basic commentary for pre/post-earnings analysis.
- Human-in-the-Loop Enhancements: Incorporated intelligent manual intervention from analysts and sector experts to refine assumptions, contextualize trends, and create sector-specific valuations.
- Automated Intelligent Peer Selection: Integrated an AI-assisted tool to recommend best-fit peers from the list provided by third-party subscriptions and identify outliers vs. the industry average.
Key Takeaways
- Efficiency: Saved over 60% of data aggregation time, enabling analysts to focus on generating actionable insights for asset managers.
- Quicker Time-to-Market: Created an investable portfolio in 40% less time than an entirely manual process.
- Scalability: Ensured the financial model was user-friendly and scalable across all sectors under coverage and consideration.
