Solvexia Features

Data Source Access (3)

Profiling and Classification: Permits profiling of data sets for increased organization, both by users and machine learning
Data Modeling: Tools to (re)structure data in a manner that enables quick and accurate insight extraction
Data Joining: Allows self-service joining of tables
Data Blending: Provides the ability to combine data sources into one data set
Data Quality and Cleansing: Allows users and administrators to easily clean data to maintain quality and integrity
Data Sharing: Offers collaborative functionality for sharing queries and data sets
Data Governance: Ensures user access management, data lineage, and data encryption

Breadth of Integrations: Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools
Ease of Integrations: Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools
Data Workflows: Operationalizes data workflows to easily scale repeatable preparation needs

Automation: Automates the process of comparing financial transactions between different accounts, statements, or systems
Bank Statements: Helps match transactions recorded in the company's books with those in the bank statement
Exceptions: Alerts users when exceptions and discrepencies can't automatically be resolved
Algorithms: Uses algorithms, such as rule-based matching, fuzzy logic, or machine learning techniques to help with comparing large sets of data

Accuracy: Ensures that financial records are accurate and precise, reducing the chances of errors that could lead to audit issues
Compliance: Meets industry-specific compliance requirements and supports regulatory standards
Reporting: Offeres customizable reporting features to provide a clear overview of the financial reconciliation process and any issues that may need attention
Audit Trail: Provides a detailed history of reconciliations and supports the accuracy of financial records

Accounting: Integrates with accounting platforms to allow the import and reconciliation of financial data
Inventory: Integrates with inventory management systems to help reconcile financial data related to product movements, purchases, and sales
ERP: Integrates with ERP systems to reconcile financial data across various modules and departments
Banking: Integrates with banking systems to import and match bank transactions

Access: Offers role-based access control (RBAC) allowing administrators to define roles with specific sets of permissions
Authentication: Uses features such as multi-factor authentication (MFA) to help ensure that only authorized users can access the software
Encryption: Has encryption techniques to prevent unauthorized access or interception of data
Regulations: Complys with regulations and standards, such as Sarbanes-Oxley (SOX), GDPR, or HIPAA

Data Analysis Tools: Analyze survey results via statistical testing or crosstabs directly within the software
AI/Machine Learning: Software program that continuously adjusts its behavior based on observed data
Data Extraction: Automatically retrieve and pull information from documents, websites, images, data sets, and other sources
Reporting/Analytics: View and track pertinent metrics to find patterns and gain insights from data
AI Copilot: A virtual assistant that uses AI to pursue goals and complete tasks on behalf of users
Data Mapping: Track the management and flow of data throughout the organization
ETL: Process of extracting, transforming, and loading data
Big Data Analytics: Examine large amounts of data to uncover hidden patterns, correlations, and other insights
Data Quality Control: Establish guidelines about how much and what kind of data can be stored in the repository