What is transaction cost analysis (TCA), and how does AI improve it?
Transaction cost analysis (TCA) measures the true cost of executing trades — spread, slippage, market impact, and fees — across pre-trade and post-trade. AI improves TCA by processing far more execution and market data in real time, surfacing where costs accumulate, and has helped firms cut transaction costs by 15–20% while improving execution quality.
What TCA measures
TCA goes beyond commissions to capture the hidden costs of execution: the spread paid, slippage against a benchmark, market impact from the order itself, and venue-specific performance. Measured well, it turns execution from a black box into a managed cost.
How AI changes it
Traditional TCA is periodic and backward-looking. AI lets firms analyze execution at the fill level in near real time, across structured and unstructured data, so patterns — a costly venue, a time-of-day effect — surface while they can still be acted on.
Pre-trade and post-trade
Pre-trade, AI helps forecast likely cost and choose an execution strategy. Post-trade, it explains what actually happened and feeds that back into the next decision, closing the loop between measurement and execution.
Frequently asked questions
Who uses transaction cost analysis?
Asset managers, trading desks, and financial institutions use TCA to evaluate execution quality, control costs, and demonstrate best execution to regulators and clients.
How much can AI-driven TCA save?
In ARCHR's work, AI-driven TCA has helped firms reduce transaction costs by 15–20% while improving execution quality and maintaining regulatory alignment.