
Summary
Executing block trades efficiently requires real-time data, deep buyer insights, and coordination across trading desks. However, many firms rely on outdated, manual Indication of Interest (IOI) processes, causing delays in finding the right buyers and increasing market impact. With modern data automation and AI-driven analytics, banks can quickly surface historical buyer behavior, optimize execution strategies, and minimize risk exposure—turning block trading into a competitive advantage rather than a bottleneck.
Challenges
IOI processes are manual and slow, leading to missed execution windows.
Lack of historical buyer insights makes it harder to match blocks to the right counterparties.
Data fragmentation across desks prevents real-time coordination between sales, trading, and execution teams.
Market impact and slippage increase when large trades aren’t optimized based on liquidity trends.
OutcomeCatalyst Solutions
Automated IOI workflows – We build custom solutions that streamline and automate the distribution of trade opportunities, reducing time-to-execution.
Historical buyer behavior analytics – Our data ingestion framework integrates execution history across all desks, helping traders instantly identify counterparties that have bought similar securities.
Real-time liquidity tracking – By structuring internal execution data alongside market data sources (Bloomberg, ICE, etc.), we help traders optimize execution paths and minimize market impact.
AI-driven trade recommendations – Machine learning models analyze historical execution patterns, liquidity depth, and counterparty behavior to recommend optimal trading strategies.