Performance Insights Across an Entire Company
Automations and alerting keep you up-to-date
Analyze transaction data to find anomalies
Combine data sources to unify financial software
Financial Analytics Solutions
Transform your financial data into actionable insights
Empowering Financial Analytics Teams
Security first with Qrvey’s embedded analytics for financial analytics solutions. Enable teams to safely analyze sensitive data within SaaS applications, from individual records to entire financial practice performance all within your SaaS platform.
Maximum Data Security with Qrvey’s Embedded Analytics Solution
Qrvey is deployed directly to AWS environments for industry-leading security and scalability for financial analysis software.
With Qrvey’s cloud-native solution built on AWS, there are no servers to manage. Resources scale automatically as your needs change. You get powerful analytics capabilities without large upfront investments.
Learn More About Qrvey’s Deployment Model
Connect to Any Data Source, Including Real-Time Financial Transactions
Qrvey connects to any data type – SQL, NoSQL, and unstructured data sources like forms and images – and enables analysis from multiple financial data sources on a single dashboard.
With an API layer built for rapid development, data can be pushed directly from the source for real-time analysis within your financial analyst software solution.
Learn More About Qrvey's Built-In Data Layer
Pixel Perfect Reporting
The pixel perfect report builder enables you to build customizable report templates with precise control over the positioning and style of every element on the layout, down to the individual pixel.
Pixel perfect reports are important when the output must meet exact specifications, such as in audit reports, tax forms, regulatory documents, shipping labels, and more. This provides rigorous controls for formatting PDF and print output.
Learn More About Qrvey's Embedded Pixel Perfect Report Builder
AI + Financial Analysis Tools
Qrvey connects directly to the AWS AI suite to power real-time machine learning augmentation for financial analysis software.
Since Qrvey deploys to your AWS VPC and analyzes financial data in real-time, this eliminates the need to copy data outside your AWS environment or use costly third-party ETL solutions with AI and ML.
Learn More About Qrvey’s Data Management
Learn More About How Qrvey Empowers Financial Analytics Solutions
Download this extended report to learn how the Qrvey’s embedded analytics solution enables executives, CFOs, controllers, and more with a customizable solution to meet the varying financial data analytics needs of any team.
Download ReportImprove ROI and Performance Using Your Data
Qrvey enables a wide range of financial analytics use cases while prioritizing data security. Our software embeds interactive analytics directly into financial data analytics platforms. This integrated approach accelerates time-to-value since teams can immediately access analytics within familiar systems.
Granular Analysis to Uncover Trends Instantly
Ingest and analyze data with Qrvey’s API
Enable teams to drill-in to the lowest level of data
The best in security as your data stays in place
Analyze Large Financial Data Volumes
Spot trends early with comprehensive automations
Optimize real-time detection
Connect to AI services for in-depth analysis
Provide the Best Financial Analysis Software
Collect real-time customer feedback
Analyze results as the data comes in
Alerting to keep processes inline
Key Capabilities of Qrvey for Financial Analytics Software
Real-Time Data Processing
Spot trends in real-time, support rapid decision-making and improve operational outcomes with real-time data analysis.
Enterprise Scale
Efficiently scale to billions of records and increase concurrent user access while lowering infrastructure costs.
Integrated AI
Use AI for predictive analytics, improving sales forecasting, business operations, and inventory planning.
Multi-Tenant Data Lake
Scale analytics offering by combining data sources into a scalable central analytics layer for multi-tenant apps.
Data Governance
Manage data security by ensuring data quality, regulatory compliance, and effective data stewardship.
Integration Ready
Unified data pipeline supporting integrations from enterprise systems, applications, and all data sources.
Self-Service Analytics
Empower users to build their own custom dashboards using Qrvey’s multi-tenant security layer and embedded builders.
Embedded Data Visualization
Advanced data visualization tools and pixel-perfect reporting with real-time dashboards for financial reporting.
Support
Your success is our success. Our award willing support team is with your every step of the way.
Analytics for Those Who Want More
Build Less Software. Deliver More Value.
Frequently Asked Questions
What is financial analytics?
Financial analytics involves using statistical, predictive and optimization models to analyze financial performance and make data-driven business decisions. It entails gathering data from financial statements, budgets, investments, cash flows, transactions, etc. to evaluate risks, identify opportunities and forecast future performance.
Who are the typical users of financial analytics software?
Typical users of financial analytics include:
- Chief financial officers – To monitor finances and make strategic decisions.
- Accountants – To analyze historical financial data and create reports.
- Auditors – To identify control deficiencies, risks, and assess financial health.
- Treasurers – To forecast cash flows, manage portfolios, and evaluate investments.
- Financial analysts – To value assets, assess mergers and acquisitions, and research industries/companies.
- Portfolio managers – To optimize investment mix and returns based on quantitative analysis.
- Risk managers – To measure market and credit risks and minimize exposure.
What are common use cases of financial analysis tools?
Some common use cases are:
- Budgeting and forecasting – Build models to forecast revenues, costs, cash flows.
- Risk management – Assess market, credit, operational risks to minimize losses.
- Investment analysis – Analyze risk-return tradeoffs, construct optimal portfolios.
- Pricing optimization – Set optimal prices for products based on customer demand modeling.
- Fraud detection – Identify anomalies in transactions, claims, reports that indicate fraud.
- Process optimization – Improve efficiency of financial operations like cash flows, collections etc.
- Financial reporting – Generate regular reports for stakeholders on financial health, trends and variances.
- Mergers and acquisitions – Conduct due diligence, valuation, proforma analysis of deals.
What is pixel-perfect reporting?
Pixel perfect reporting is a term that refers to the precise and accurate rendering of reports, ensuring that every element, from text and images to layout and formatting, appears exactly as intended.
Why is pixel perfect reporting important?
- Pixel perfect reporting enhances the credibility and reliability of the information presented in the reports.
- Pixel perfect reporting improves the decision-making process by providing clear and consistent data and insights.
- Pixel perfect reporting saves time and resources by reducing the need for manual adjustments and corrections.
- Pixel perfect reporting exhibits professionalism and quality by delivering visually flawless and consistent reports.
- Pixel perfect reporting supports compliance and audit trail by meeting the regulatory standards and requirements for certain industries.
What are common use cases of pixel perfect reporting?
- Printed forms: Pixel perfect reporting allows for precise placement of text and data within standardized forms, which is essential for compliance reasons in industries such as finance and healthcare.
- Paginated reports: Pixel perfect reporting allows for creating reports that span multiple pages, with features such as page breaks, headers, footers, and numbering, which is useful for printing or exporting to PDF.
- Dashboards and charts: Pixel perfect reporting allows for creating interactive and dynamic dashboards and charts, with features such as drill-down, filters, and parameters, which is useful for data analysis and visualization.