What it is
Fraud, theft and operational anomalies hide in volume. A distribution grid has hundreds of thousands of meters; a payments platform sees millions of transactions; a claims department handles more files than anyone can read closely. Humans are excellent at judging a case once it is in front of them. The hard part is choosing which cases to look at.
That is the job of a detection system. It scores every meter, transaction or claim, explains the score, and produces a short queue ranked by the likelihood that a human will find something. Investigators spend their time where it counts, and their findings make the next queue better.
How we approach it
We begin with your investigators, not the data. What does a confirmed case look like? What patterns do they already recognise? What makes them dismiss an alert as noise? That conversation shapes the features the model looks at, and it sets the false-positive budget: the number of alerts per week your team can actually work.
Then we build in two stages. Unsupervised models find patterns that do not fit, which works even when confirmed cases are rare. As your team reviews alerts and records the outcome, a supervised model learns from that feedback and takes over the ranking. Every alert carries an explanation, so the reason for a flag is never a black box.
What you get
A scoring system integrated with your case management or a lightweight queue we provide. Explanations on every alert. A feedback loop that turns investigation outcomes into a better model. Dashboards for hit rate, coverage and workload, and an audit trail that satisfies internal control and regulators.