1.0 – Chapter
Know good, catch bad
Uncover transaction monitoring risks with automated modeling of good behaviour
Updates and insights
2.0 – Chapter
3.0 – Chapter
Fighting financial crime isn’t fair
Criminals keep changing the game, while you play by the rules. Lots of rules. Meanwhile your business keeps accelerating. To keep up and comply at the same time means adding layer upon layer of complexity.
Leading to higher costs and ever more cumbersome processes, burdening your clients. Clearly, this way of thinking isnât workingâ¦
So shouldn’t we rethink the problem?
4.1 – Chapter
1. Focus on good vs bad behaviour
Criminals are always looking for loopholes and trying to stay ahead of being detected. Normal people aren’t. So why base your approach on exceptions and not on the rule?
Why not base it on regular customer behaviour? When you know what the common behaviour is of the vast majority of your customers, the anomalies clearly stick out.
How to define good behaviour?
4.2 – Chapter
2. Automated Machine Learning
Automated machine learning (AutoML) built on deep domain expertise reveals patterns of good behaviour in your raw data.
It translates these patterns, while matching them to your regulatory requirements, into actionable detecion models.
Will this work in your system?
3. Enhance your existing platform
Just place these models in your transaction monitoring environment of choice, and press play.
You can complement our detection models with in-house or external expertise and lists.
No complex migrations.
Our results are more
than just numbers
5.0 – Chapter
We achieve substantial performance improvements, but thatâs not all that matters in a regulatory environment.
Our solutions go beyond the quantitative aspects of data science to include your regulatory and internal policies in model generation.
The value is in the
model, not the system
6.0 – Chapter
System-agnostic AutoML for Fraud and AML
Automated machine learning for every transaction monitoring system.
Anomaly detection reveals hidden patterns of good behavior in your data.
Performance improvement with full regulatory explainability.
Continuously validating your detection models to keep you up to date.
7.0 – Performance
Sygno averages 80% improvement on both true and false positives for case handling, across banking domains and payment streams.
True positives become more granular findings, which creates higher certainty and more focused case management.
8.0 – Chapter
Contact us to catch crooks
At Sygno we approach financial crime from a clear-cut premise: most of your customers are not criminals.
By looking at it that way, we make transaction monitoring more manageable and more effective. So you can catch crooks, comply and be in full control.
Why lose any more sleep over it?