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Description
NEMESIS is an advanced AI-driven technology for anomaly detection that specializes in identifying fraud and inefficiencies. This next-generation platform not only highlights opportunities for improved efficiency within your business management systems, but it also serves as a customizable enterprise solution that allows business analysts to quickly convert data into meaningful insights. By harnessing the capabilities of artificial intelligence, NEMESIS addresses challenges including overstaffing, medical inaccuracies, care quality, and fraudulent claims. With its continuous process monitoring, NEMESIS reveals various risks, from anticipating quality concerns to uncovering waste and misuse. Utilizing machine learning and AI, it effectively identifies fraudulent activities and schemes before they can impact your financial resources. Furthermore, NEMESIS enhances your ability to manage expenses and track budget discrepancies, ensuring ongoing transparency regarding waste and abuse. This comprehensive approach not only improves operational efficiency but also fosters a more accountable financial environment within your organization.
Description
All types of frauds can be addressed with one solution. Subex Fraud Management is a 25-year-old domain expertise that provides 360 degree fraud protection across digital service by leveraging advanced machine intelligence and signaling intelligence. This solution combines a traditional rule engine with advanced AI/machine learning capabilities to increase coverage across all services and minimize fraud run time in the network. It also includes real-time blocking capabilities. The Subex Fraud Management solution's core is a hybrid rule engine. It covers detection techniques such as expressions, thresholds, and trends. Rule engine comprises of a combination of threshold rules, geographic rules, pattern (sequential) rules, combinatorial rules, ratio/proportion-based rules, negative rules, hotlist based rules, etc. These rules allow you to monitor advanced threats in your network.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
IBM Cloud
Microsoft Azure
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Aviana
Founded
1994
Country
United States
Website
nemesisbyaviana.com
Vendor Details
Company Name
Subex
Founded
1992
Country
India
Website
www.subex.com/fraud-management/
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Financial Risk Management
Compliance Management
Credit Risk Management
For Hedge Funds
Liquidity Analysis
Loan Portfolio Management
Market Risk Management
Operational Risk Management
Portfolio Management
Portfolio Modeling
Risk Analytics Benchmarks
Stress Tests
Value At Risk Calculation
Fraud Detection
Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval
Product Features
Fraud Detection
Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval
Telecom Expense Management
Billing for Data
Billing for Voice
Call Monitoring
Chargeback Tracking
Contract Negotiation
Fixed Line Compatibility
Internal Cost Allocation
Mobile Line Compatibility
Usage Reporting