AI & analytics
AI on your company’s real data: where to start
How to connect AI to your company’s real data — with permissions, context and privacy — to query in natural language and generate real value.
Read articleKey takeaways
- Fraud detection analyses patterns to identify anomalous operations.
- It combines business rules with machine-learning models.
- The challenge is balancing detection, false positives and customer experience.
- It requires near real-time data and privacy-respecting processing.
- Rules catch known fraud; models catch new fraud.
Fraud evolves constantly: block one pattern and another appears. Static rules alone always lag behind. Data analytics and AI detect suspicious operations with a speed and precision manual control cannot match, adapting to new patterns.
What it is
Data-based fraud detection analyses large volumes of operations to identify anomalous behaviour that deviates from normal patterns, combining business rules with models that learn from data, aiming to catch fraud before the operation completes.
How it works
Business rules
Known fraudThresholds
ML models
New anomaliesLearn normal
Decision
Near real-timeBefore completion
The most effective approach combines two layers. Business rules capture known fraud patterns (thresholds, lists, suspicious combinations). Machine-learning models detect new anomalies by learning what is "normal" and flagging deviations. Together they cover known and emerging fraud.
The difficult balance
- Detection: catch the maximum of real fraud.
- False positives: avoid blocking legitimate operations or annoying customers.
- Speed: decide in near real time, before completion.
Tuning this balance is the real art: too aggressive frustrates legitimate customers; too lax lets fraud through.
Data and privacy
Effective detection needs near real-time data and often personal or behavioural data, requiring careful privacy handling. Minimisation, access control and, where possible, anonymisation let you fight fraud without breaching the GDPR. The data layer feeding it must be as governed as any other.
The art of fraud detection is the balance: catch the maximum without frustrating the legitimate customer.
In summary
Data-based fraud detection analyses patterns to flag anomalous operations, combining rules (known fraud) with ML models (new fraud). The challenge is balancing detection, false positives and customer experience while deciding in near real time — and, handling sensitive data, doing so within the GDPR.
Sources & further reading
Frequently asked questions
Are rules enough to detect fraud?
Rules catch known fraud, but not new fraud. Combining them with anomaly-detection models also covers emerging patterns.
Is detecting more the main challenge?
Not only. The challenge is balancing detection with false positives and customer experience, while deciding in near real time.
How is privacy respected?
By applying data minimisation, access control and techniques like anonymisation where possible, processing data in line with the GDPR.
Why combine rules and models?
Rules capture known fraud patterns; ML models detect new anomalies. Together they cover both known and emerging fraud.
What if the system is too strict?
It generates false positives: blocking legitimate operations and frustrating customers. The detection must be balanced with experience.
Does it need real-time data?
Near real-time, to decide before the operation completes. That adds complexity but is what allows blocking fraud in time.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.