Automatically analyzes 100% of transactional data in real-time to identify anomalies, exceptions, and patterns that indicate potential fraud, errors, or compliance issues. The system runs scheduled analytics and provides immediate alerts when thresholds are breached.
Uses machine learning algorithms to identify unusual patterns and outliers in financial data without requiring predefined rules. The system learns normal transaction patterns and flags deviations that may indicate fraud or errors.
Provides a drag-and-drop interface for building complex analytics scripts without coding. Users can combine data sources, apply transformations, and create sophisticated analyses using visual workflow tools.
Includes hundreds of ready-to-use analytics scripts for common audit, fraud detection, and compliance scenarios across various industries and regulatory requirements.
Connects to virtually any data source including ERP systems (SAP, Oracle, Microsoft Dynamics), databases, spreadsheets, and cloud applications through native connectors and APIs.
Provides workflow tools for managing the entire audit lifecycle, from planning and fieldwork to reporting and follow-up, with role-based access and audit trail capabilities.
Internal audit teams use ACL Analytics to monitor employee transactions for potential fraud indicators such as duplicate payments, ghost vendors, or unusual procurement patterns. By analyzing 100% of payment data and comparing against multiple data sources, organizations can detect sophisticated fraud schemes that bypass traditional controls. This continuous monitoring approach significantly reduces financial losses and acts as a strong deterrent against fraudulent activities.
Public companies utilize ACL Analytics to automate testing of Sarbanes-Oxley controls over financial reporting. The platform continuously monitors key controls and transactions to ensure compliance with regulatory requirements. Automated testing reduces manual effort during quarterly and annual reporting cycles while providing ongoing assurance that controls are operating effectively throughout the year.
Procurement and supply chain auditors leverage ACL Analytics to identify inefficiencies and risks in purchasing processes. The tool analyzes purchase orders, invoices, and contracts to detect maverick spending, contract compliance issues, and supplier relationship risks. This enables organizations to optimize spending, ensure compliance with procurement policies, and identify opportunities for cost savings through better vendor management.
Healthcare organizations use ACL Analytics to detect fraudulent billing practices and ensure compliance with Medicare/Medicaid regulations. The platform analyzes claims data to identify patterns of upcoding, unbundling, or services not rendered. This helps healthcare providers and payers reduce improper payments, maintain regulatory compliance, and protect against both internal and external fraud schemes.
Financial institutions employ ACL Analytics for anti-money laundering (AML) and know-your-customer (KYC) compliance. The system monitors transaction patterns across accounts to identify suspicious activities that may indicate money laundering, terrorist financing, or other financial crimes. Advanced analytics help banks meet regulatory requirements while optimizing investigation resources by prioritizing high-risk alerts.
IT auditors use ACL Analytics to automate testing of IT general controls, including user access reviews, segregation of duties violations, and system change management. By analyzing system logs and configuration data, organizations can continuously monitor IT controls and quickly identify control weaknesses or unauthorized activities that could impact financial reporting or data security.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
