Business Impacts
Real-Time Fraud Flagging and Benchmarking
Multi-Tenancy for Rule Suites, UI-Based Case Management, Case Analytics, and Dashboarding
Explainable AI for Fraud Detection
Customer Key Facts
- Location : Europe
- Industry : Financial Services
Problem Context
The client is a leading digital payment as a service provider offering businesses a faster, easier, and more reliable way to move money. The client sought to develop a real-time fraud detection system with low false positives for their Point of Sale transactions.
Challenges
- Need of real-time Fraud Detection at Point of Sale
- The model needed to be explainable
Technologies Used
Amazon API Gateway
AWS Lambda
Amazon Aurora
AWS S3
Amazon Sagemaker
Amazon Fraud Detector
Set Up a Fraud Detection Platform for Transactions
Solution
Quantiphi helped them set up a fraud detection platform for transactions at Point of Sale. Salient features of our solutions included:
-Client onboarding/ Risk Scoring: Conduct historic screening of a new client across millions of data items to feed into their risk score
-Real-time Monitoring: Screen millions of clients transactions across billions of data items in real-time to identify sanctions, politically exposed accounts, and adverse media.
-Threat Investigations: Connect the dots via an all-source investigations tool for exploring screening results.
-AI-driven Anti Money Laundering: Verify applicant identity, maintain verification records, and flag in real-time when a person appears on any suspected criminal list.
-Anomaly Detection: Deploy self-learning ML models trained to recognize well-known fraud patterns as well as to adapt to new, unknown fraud techniques.
Result
- The new platform had real-time fraud flagging and benchmarking capabilities, API Endpoints for fetching risk scores, and explainability for flagged transactions. In addition, it has a rule engine and API Endpoints integrated with the current pipeline
