Business Situation
- A US-based asset management firm had shortlisted over 200 companies globally and needed financial models developed promptly to facilitate comprehensive analysis.
- The models were required to incorporate a consensus view and analyst input, with options to adjust for corporate actions and automated pre/post-earnings analysis. The client sought a scalable, intelligent solution that would ensure accuracy and efficiency while maintaining human expertise.
SGA Approach
- Sector-Based Classification: We categorized the shortlisted companies using AI-assisted industry mapping, which was subsequently validated by human analyst in the loop to ensure accuracy.Enhanced Model Customization: Our team leveraged database-linked automated templates, which were then fine-tuned by analysts to capture industry-specific nuances.
- AI-Enabled Pre/Post-Earnings Analysis: We automated earnings revision tracking, corporate actions adjustments (e.g., stock repurchases), and the visualization of historical trends.
- Analyst-Guided Decision Support: The AI provided preliminary insights, which analysts then refined, used to contextualize financial trends, and checked to ensure regulatory alignment.
- Dynamic Updates & Scalability: An AI-assisted monitoring framework was integrated to reduce the manual workload and enhance accuracy across all reporting cycles.
Key Takeaways
- Massive Efficiency Gains: Saved over 80% of the time previously spent on data aggregation, enabling analysts to focus on generating value-added insights.
- Significant Cost Reduction: Achieved an approximate 60% cost reduction when compared to performing the same processes entirely manually.
- Scalable & Adaptive Framework: The Agentic-AI solution facilitated the seamless expansion of models across new sectors without requiring additional analyst work.
- Standardization with Flexibility: The new process ensured consistency in financial modelling while empowering analysts to adjust for deeper strategic insights.
