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Description
KYCsphere is an innovative compliance platform utilizing AI to streamline the KYC and AML processes throughout the entire compliance lifecycle, from the initial onboarding of customers to necessary regulatory reporting, all hosted within a cloud-based SaaS framework on Microsoft Azure. This comprehensive solution amalgamates various functions such as customer onboarding, identity verification, sanctions and PEP screening, transaction monitoring, risk evaluation, alert management, investigative case handling, and regulatory reporting, including SAR and CTR filings, into a singular system. By leveraging machine learning, the platform enhances risk scoring, utilizes fuzzy-matching techniques for name screening, and implements real-time anomaly detection to significantly minimize false positives. Compliance teams can effortlessly set up workflows and customize risk parameters using no-code tools, eliminating the need for IT support. The solution adheres to the regulatory standards set by FinCEN, OFAC, FATF, and EU AML Directives, ensuring compliance across all jurisdictions. There is no requirement for hardware investment, allowing for rapid deployment within days, automatic scaling, and comprehensive audit trails throughout every region. By integrating these diverse functionalities, KYCsphere not only simplifies compliance but also improves operational efficiency for organizations.
Description
NetOwl NameMatcher, recognized for its excellence in the MITRE Multicultural Name Matching Challenge, delivers unparalleled accuracy, speed, and scalability in name matching solutions. By employing an innovative machine learning framework, NetOwl effectively tackles the intricate challenges of fuzzy name matching. Conventional methods like Soundex, edit distance, and rule-based systems often face significant issues with precision, leading to false positives, and recall, resulting in false negatives, when confronting the diverse fuzzy name matching scenarios outlined previously. In contrast, NetOwl leverages a data-driven, machine learning-based probabilistic strategy to address these name matching difficulties. It automatically generates sophisticated, probabilistic name matching rules from extensive, real-world multi-ethnic name variant datasets. Furthermore, NetOwl employs distinct matching models tailored to various entity types, such as individuals, organizations, and locations. To add to its capabilities, NetOwl also integrates automatic detection of name ethnicity, enhancing its adaptability to the complexities of multicultural name matching. This comprehensive approach ensures a higher level of accuracy and reliability in diverse applications.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
Tableau
Integrations
ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
Tableau
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
Divas Software
Founded
2000
Country
India
Website
www.divassoftware.com
Vendor Details
Company Name
NetOwl
Founded
1996
Country
United States
Website
www.netowl.com/name-matching-software
Product Features
Product Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management