Many tax-loss harvesting systems start as rules engines: find a loss, avoid an obvious wash sale, and trade into a substitute. That works for simple accounts. It breaks down when the platform has thousands of accounts, many tax lots, account-level restrictions, cash flows, and risk budgets.
At that point, latency changes the product. If each account takes too long to evaluate, the platform stops testing alternatives. It narrows the opportunity set before the optimizer has a chance to find the best tax/risk tradeoff.
Figure 1: What a harvesting decision actually balances
Slow optimization makes the product conservative
When solving is slow, product teams compensate with shortcuts: fewer candidate trades, coarse tax thresholds, manual exception queues, and overnight-only processing. Those shortcuts reduce operational risk, but they also leave tax alpha untested.
PRISM's GPU-native path is designed for this pressure. Faster solves let a platform evaluate more candidate harvests, more replacement portfolios, and more account batches while staying inside explicit constraints.
Figure 2: Latency changes how many alternatives can be tested
Harvesting needs auditability, not just speed
Tax-aware rebalancing touches client outcomes, regulatory review, and advisor trust. A fast optimizer still has to explain why a trade was selected, what constraints were active, and what alternatives were rejected. PRISM's role is to make the solve fast enough to be useful and explicit enough to be reviewed.
Practical next step: compare your current harvesting flow against a fixture with lots, restrictions, wash-sale windows, and tracking-error limits. If the fixture needs manual intervention, the optimizer is not yet the system of record.