Client type
Commercial Real Estate Lender
Key stakeholders
Multiple business lines
Location
Global
Project outline
- The origination team was overwhelmed by deal volume and spent hours manually reviewing opportunities. Consequently, this led to missed deals and slower response times.
- Skilled originators spent time on data gathering and manipulation instead of complex analysis and client work that leveraged their expertise. Meanwhile, valuable deal information was trapped in emails, pitchbooks, and PDFs, making it impossible to identify trends or benchmark against comparable deals.
The Challenge
StructureIt created an intelligent email monitoring system that transformed how the lender evaluates incoming opportunities. First, the AI deal flow automation solution automatically monitors originators’ inboxes for new and updated deal submissions. Then, it uses advanced language models to extract critical data from emails, attachments, and broker pitchbooks. As a result, this not only eliminates manual data entry but enables originators to immediately focus on the deals that best align with the firm’s investment criteria.
The system captures property financials, sponsor information, loan terms, and market data. Additionally, it structures previously inaccessible information into a centralized repository. Each opportunity is automatically scored against the firm’s investment criteria. Furthermore, deals are ranked and prioritized based on strategic fit and profit potential. High-priority opportunities are immediately flagged, ensuring the team can respond quickly to the most promising deals.
However, StructureIt’s approach established a foundation for the future. The structured data repository creates opportunities to benchmark new deals against historical comparables, identify emerging market patterns, and refine investment criteria based on actual performance. Therefore, the firm is now positioned to use and grow these capabilities as the AI deal flow automation system evolves.
The system delivers exceptional performance across three key dimensions:
- Scalable email inbox monitoring: Successfully monitoring 12 inboxes and processing up to 500 emails per day at a 99.88% success rate
- Accurate opportunity data extraction: Extracting critical data points with 95% accuracy using artificial intelligence
- Reliable email processing: Processing emails successfully without timeout or token limitations at a 99.88% success rate
Now, senior originators spend their time on what matters: sophisticated financial modeling, deal structuring, and client relationship management. The hours previously lost to data gathering and initial screening are redirected to the high-value analysis that drives profitable lending decisions.
How StructureIt helped
StructureIt was asked to build a solution that could rapidly deliver a clear, quick return on investment. StructureIt delivered a cloud-based data ingestion platform that:
- Centralizes data collection from multiple disparate upstream data sources
- Automatically ingests, validates, and extracts data into a cleansed layer, consolidated into a single common normalised format with additional fields derived for analytic purposes
- Provides a generic configuration-driven solution that allows complete control and versioning of historical schemas within a database, enabling the extraction process to adapt to changing data structures over time
Through the solution’s configurable design, users can now validate new schemas and data independently, streamlining adaptation to new and evolving data sets.
The Results
The platform is now a monetized asset for our customers. By providing a centralized repository for cleansed, consolidated, and normalized RMBS data, our customer has been able to deliver analytical insights via Snowflake, Databricks, and other data distribution layers, offering new data products to market and strategically increasing revenue numbers.
Alongside this, the analytics teams and domain experts are enabled to independently onboard and analyze new data sets within days, rather than weeks. This has also significantly reduced the business dependency on IT resources, and the platform’s ability to validate and adapt to new data significantly shortens analytics production cycles.
With this foundation in place, our customer is now expanding the platform to cater to additional data sources, which is crucial for future growth and adaptability in the evolving data landscape.
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