AI-Driven
Fraud Detection.
Main areas of benefit.
Budget Savings π°
We use open-source frameworks which enables us to onboard the latest AI trends quickly without a budget hit. Integration costs are also minimised.
Continuous improvement π
The system learns from your feedback and adapts its decisions accordingly => this enables you to detect new types of fraud quickly.
Customer satisfaction π
Our system can not only help with fraud detection, but also with better customer satisfaction.
Employee satisfaction π₯°
Our system helps you with routine tesks so your employees can focus on creative work. 24/7 service 100% covered by machine.
Our principles π©
Open Source
Zero cost for 3rd party software.
Cloud Ready
No matter if you run on AWS, Google Cloud, Azure or on-premise, we are ready.
Scalable
Prototyping can be done on local machine or small sandbox. Production can run on a huge cluster.
Modularisation
Enable only data sources you need. Detect only the types of fraud you want - a plug-in approach.
Performance π
Accuracy
More than 85%-95% precision depends on a case
0.004% - 0.006% false positives ratio
Latency & Performance
Less than 3 minutes to classify 25 million calls
Approximately 2 minutes to prepare data set
Financial Benefits (T-Mobile CZ)
1 FTE spared
24/7 monitoring not needed now
150k - 200k EUR yearly saved (OPEX)
Use cases π―
Examples
PBX fraund - Hacked PBX, stolen mobile (SIM) identity, etc.
SIM boxing - Interconnection fraud
Spoofing - Detects fake call-centers, masquerading as a trusted entity
Wangiri - "one ring and cut" using premium numbers
Other features - payments fraud, flash-calls, gambling, etc.
Delivered
Fraud detection in Telco network
Fraud detection on Google payments data
Data analytics platform on huge amount of e-com data (scraped htmls mainly + behavior data)
Customer segmentation for marketing and campaign optimization
Setting up environment & processes for data analytics (hadoop & spark clusters, backend for reporting systems etc.)
Tech. stack π§
How we work π¨βπ»
Analysis/Feasibility
We analyse fraud cases specifics, data sources available and decide on feasibility.
Proof of Concept
We prepare deployment of a real, representative scenario to evaluate performance and feasibility, this is cheap and fast.
Detailed design
We define how to integrate with the current systems/data sources and prepare infrastructure.
Integration
System is deployed, AI models trained and fraud detection is up and running.
Activation
System is activated and after a certain monitoring period we ensure the system is running OK and considered in full duty.