Every transaction triggers the same four-stage response a biological immune system runs against pathogens. Continuously, at scale, with full explanations for your audit team. This is what happens in the 200 milliseconds between request and decision.
When a transaction arrives, ISOFORT gathers more than 200 signals from your application context: device telemetry, behavioral fingerprint, transaction history, counterparty graph, network reputation. Each signal is scored against a learned baseline for that specific customer, in that specific context.
There are no static rules to write or maintain. The baselines update continuously as your customer base evolves. A behavior that's anomalous for one customer may be perfectly normal for another. We score against the individual, not the average.
Signals alone don't make a decision. The Isolation Forest algorithm separates the truly suspicious from the merely unusual. Unlike supervised models that need labeled fraud examples (which are scarce and lag the threat), Isolation Forest finds outliers by structure, not by past examples.
Mathematically, it partitions the signal space with random splits, then measures how isolated each event becomes. Genuinely anomalous events get isolated in fewer splits than normal ones. The depth at which an event gets isolated is its anomaly score.
The anomaly score informs the action. Below the release threshold, the transaction goes through. Above it, the system either blocks outright or escalates to step-up authentication, depending on the customer's risk profile and the action's reversibility.
Every decision returns with the signal trail that drove it. Your fraud team sees not just "score: 0.94", but the specific signals that contributed: which behavioral deviation, which network signal, which historical pattern. Explainability isn't a nice-to-have for European regulators - it's required.
Every confirmed event becomes part of the model's memory. When your fraud team confirms a block was correct, that signal pattern joins the learned baseline of "this is what real fraud looks like for this customer base." When they overturn a decision, the model learns the inverse.
This is the difference between a static rule engine and an adaptive immune system. Tomorrow's coordinated ATO attempt won't surprise the model the way today's did. The fraud team's expertise compounds into the platform, not into individual rule changes.
Labels are scarce. Attackers adapt fast. Supervised models lag the threat by months. We needed something different.
Fraud labels are scarce, biased, and arrive late. Isolation Forest finds outliers by structure, not by examples.
O(n log n) scoring complexity. Works at production throughput without the cost of deep models.
Path length through the trees is interpretable. Auditors don't need ML expertise to understand a decision.
Watch the response on your real attack vectors. We bring the data, you bring the questions.