• There has been a huge increase in digital channels, resulting in more digital and online transactions. • Fraudsters' activities are becoming more complicated every day. Staying ahead of the attackers is key to establishing the 'trust' that users need. • The role of Security & Risk Assessment managers is very critical. → They need powerful tools to focus on their business instead of the tool's capabilities. → They need the tools to analyze the increased data and anomalies that are occurring and to derive results (rules & ML combined). • There is a need to monitor all the channels with a single platform (omnichannel) which allows for observing the interconnected activities across different channels (end-to-end fraud monitoring). • In addition to the transaction channels, internal fraud activities need to be carefully monitored. • Application Fraud is another aspect of fraud which can be addressed under the 'omnichannel+' concept.
What is InACT® Anti-Fraud Hub
InACT® Anti-Fraud Hub is a 360⁰ omnichannel anti-fraud hub, supported by machine learning algorithms and big data technologies, that monitors and prevents multichannel transactional fraud, misconduct attempts, operational faults and regulatory controls.
The Three Pillars of InACT® Anti-Fraud Hub
Enterpise, Multi-Channel Anti-Fraud Management
InACT External Fraud
• Internet & Mobile Banking • Telephone Banking • Branch Transactions • Card & Payment Systems (Cards, Merchants, POS and ATMSs) • Open Banking (PSD2)
• Credit Cards • Loan Applications • On Boarding Process
- Predictive Fraud & Anomaly Detection with Machine Learning Algorithms
- Data Analytics with KNIME
- Visual Relationships Analysis and Advanced Dashboard (enriched with Tableau)
Benefits
A single platform to manage end-to-end processes
Easy management of scenarios, rules, statistics, and profiling:
→ The powerful and flexible platform incorporates a rules engine augmented with machine learning capabilities for predictive fraud detection. → The user-friendly and practical interface enables analysts to dynamically define and deploy their own scenarios without requiring IT involvement.
Advanced dashboard:
→ Business units have the capability to create their own personalized dashboards.
Fast time to market:
→ Easy integration and concise implementation plans.
Local presence, global power:
→ Backed by experience. → Long-term customer relationships. → Trust.
Fast time to market:
→ Easy integration and concise implementation plans.
Local presence, global power:
→ Backed by experience. → Long-term customer relationships. → Trust.
Case management and reporting
Workflow management
Fast time to market:
→ Easy integration and concise implementation plans.
Local presence, global power:
→ Backed by experience. → Long-term customer relationships. → Trust.
End-to-end expertise in the financial sector, ranging from core banking to the latest digital channel applications
Features
Real-Time Scenarios:
Real-time decisions for declining, applying SCA (Strong Customer Authentication), holding transactions, and approving
Near Real-Time Scenarios:
Alert creation and automated workflow actions.
Simple Scenario Management
Blacklist & Whitelist Management
Blacklisted merchants
High risky MCC’s, countries
Alert monitoring
With all necessary detailed data
User and Scenario Performance Monitoring Reports
Dynamic Alert Assignment
Based on transaction score, scenarios or transaction date & time
Scenario Import/Export
Workflow for Automated Actions
Fraud Case Management
Advanced and Interactive Dashboard Enriched with Tableau
Data Analytics with KNIME
Score Card
Common Point Analysis
Fraud Filters - (watch/exclude)
Maker Checker/4 Eyes principles
Customer /Merchant/Device Based
Behavior Profiles
Real Time Scenarios are integrated with profiles
Transactional history controls for previously used IPs, Geo-locations, Beneficiaries, avg/max/total/sum of transactions
Maxmind IP Geolocation
Custom Scenarios, Reports and Profiles with SQL Wizard and SQL Ready functions
Audit Proof operation with full audit logs
Custom fit solutions
Scalable and modular structure
Big data technologies to handle large scale of data
Apache Kafka
Apache Flink
Separate databases for OLTP and OLAP functions
Flexible structure
Convenient for integration with new products and channels
Anomaly Detection with Machine Learning Algorithms
Machine Learning Algorithms for anomaly and fraud pattern recognition: Powered by Solver AI Suite
Elastic search for similarity check
Visual Relationship Analysis supported with Graph database
Neo4j DB to detect complex and hidden relationships. The results from the relationship analysis are being used to power machine learning models for predictive fraud detection
