Anomaly & fraud detection
Most of what happens in a business is normal. The rare exceptions, such as a fraudulent payment, a machine starting to fail or a link losing quality, are the ones that cost money.
We build systems that learn what normal looks like in your data and point people to the cases that deserve attention.
What anomaly detection does
Fixed rules catch the problems someone already thought of. They miss new patterns, and they often fire too much when conditions change in harmless ways.
An anomaly detection system learns the usual behavior of transactions, sensors, users or equipment, and scores how far each new event is from it. Reviewers then work through a short ranked list instead of scanning everything.
What we build
Transaction monitoring
Scoring payments, orders or claims for unusual patterns. Reviewers start with the riskiest cases at the top.
Early warning for equipment
Watching sensor readings for slow changes that tend to come before a failure, while there is still time to plan maintenance.
Network and system health
Spotting services, links or servers that behave differently from their usual pattern, and grouping related alerts into one case.
Quality deviations in production
Flagging batches or process runs that drift from normal, using measurements the line already records.
Account and access behavior
Noticing logins, transfers or usage that do not fit how an account normally behaves.
Data quality checks
Catching missing, duplicated or out-of-range values in incoming data before they reach reports or other models.
When it's a good fit
Where it works well
You have plenty of historical data, problems are rare or keep changing shape, and someone is ready to review flagged cases.
Rules may be enough
If the problem is well understood and stable, a few clear rules are cheaper to run and easier to explain. If that is the case, we will tell you.
Not yet
If nobody can act on an alert, or the data is not collected consistently, it is better to fix those first.
How a project runs
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Define what counts as a problem
We sit with the people who handle cases today and decide which events matter, what a missed case costs and how many alerts a team can realistically review.
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Learn normal behavior
We study historical data to understand regular patterns, including seasons, shifts and the differences between sites or customer groups.
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Replay the past
We run the system over past periods with known incidents to see what it would have caught, how early and with how many false alarms.
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Run alongside the team
Alerts go to reviewers in parallel with current checks. We use their feedback on each alert to tune thresholds and improve the model.
Few examples, many false alarms
Because confirmed problems are rare, most of the work goes into balancing missed cases against false alarms, and the choice of model matters less than that balance. Reviewers soon stop trusting an alert that is wrong too often. So we judge the system from their side: by how many flagged cases were worth looking at and how many confirmed problems it caught.
Every alert comes with the reasons behind it, such as which values were unusual and compared with what. Reviewers can decide faster, and the team has something concrete to question when an alert looks wrong.
What you receive
- A working detection system connected to your data sources
- A report on how the system performed on past data
- A process for collecting reviewer feedback and retuning the system
- Code and documentation, handed over to your team
Frequently asked questions
Where does our data stay during an anomaly detection project?
In your environment. The system can be trained and run on your own servers or private cloud, so transaction and sensor data does not have to leave.
Can anomaly detection work with only a few examples of fraud or failures?
Yes. The system mainly learns from normal behavior, which you usually have plenty of. The confirmed cases you do have are used to check and tune it.
How soon can we see what the system would catch?
The first results come from replaying past data, which shows what the system would have caught. How soon that happens depends mostly on how quickly we get access to the data and on its quality.
Do alerts show up in the tools our team already uses?
In most setups, yes. Alerts can go to your existing dashboards and tools, and nobody has to watch a separate screen.
What happens when normal behavior changes?
What counts as normal shifts over time, so the system needs regular checks. We set those up with your team and can keep supporting the system or hand it over completely.
Work with us
Tell us in a few lines what you're working on. We'll get back to you.
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