Business Situation
The client, a global authority in commercial property analytics, aimed to address systemic inaccuracies and missing data in property address records across its extensive real estate transaction database. These inconsistencies posed risks to investment accuracy, geospatial analysis, and asset-level insights for clients in capital markets.
To strengthen the integrity of their analytics solutions, the client partnered with SG Analytics (SGA) to implement a scalable address validation, tagging, and enrichment model bringing together artificial intelligence (AI)-powered tools and geospatial expertise.
Engagement Overview
The project Property Address Tagging & Geocoding Enhancement was designed to elevate address data quality across all commercial property types in the client’s platform. The engagement focused on:
- Verifying and enriching property addresses
- Enhancing geospatial coordinates and visual referencing
- Improving precision for investment strategy and portfolio analytics
SGA Approach
Technology
- Automated Reconciliation: Implemented automated reconciliation processes to ensure accurate and timely settlement of transactions, reducing manual errors by 40%.
- Chatbots and Virtual Assistants: Deployed AI-driven chatbots to handle multiple customer queries and provide instant support, reducing the average response time to customer queries from 5 minutes to 30 seconds.
AI
- AI-Powered Fraud Detection: Implemented machine learning algorithms to detect and successfully prevent fraudulent transactions in real-time, reducing false positives rate by 30%.
- Customer Interaction: Enhanced customer interaction through AI-driven chatbots, which improved customer satisfaction from 70% to 90%.
Data
- Predictive Maintenance: Used data analytics to predict and prevent system failures by analyzing transaction data to identify patterns and predict potential system downtimes.
- Transaction Analysis: Analyzed transaction data to identify and address technical glitches, reducing the transaction failure rate from 15% to 2%.
Key Takeaways
Precision Through Geo-Intelligence
Address accuracy was elevated through a combination of AI mapping tools, open-data cross-verification, and visual triangulation
Human-Centric Analysis
Expert analysts added contextual judgment in edge cases such as repurposed or demolished properties, where automation alone falls short
Commercial Real Estate Confidence
Enabled the client to provide investors with location-verified analytics, increasing trust and usability of their decision support tools
Data That Moves with the Market
The enriched, structured address data will support future capabilities in urban development modeling, environmental, social, and governance (ESG) risk mapping, and regional investment forecasting
