Embedded Financial Services Analytics.

Financial services analytics is the practice of collecting, modeling, and visualizing financial data from payments, lending, banking, wealth management, or insurance to measure performance and improve decisions. For embedded SaaS products, it means shipping analytics directly inside a fintech or financial SaaS product so end-users (merchants, borrowers, bank partners, investors, or policyholders) can see their own financial data in real time, without exporting to a spreadsheet or opening a separate BI tool.

Embedded finance analytics is customer-facing reporting and dashboards built directly into a fintech product. Instead of directing users to a separate Tableau or Power BI workspace, embedded finance analytics renders native, secure, multi-tenant dashboards inside the product itself, with the platform’s brand, tenant model, and access controls. DataBrain is purpose-built for this pattern: multi-tenant by default, white-label from day one, connected to the financial data your customers already expect to see.

Financial analytics software collects data from financial systems (payment processors, loan management, banking cores, portfolio management, or insurance systems) and turns it into dashboards and reports. For embedded use cases, it is embedded directly inside a customer-facing product so merchants, borrowers, clients, or policyholders see their own data without leaving the platform. DataBrain handles multi-tenant data isolation, white-label theming, real-time updates, and AI-powered queries without a separate data team.

DataBrain is SOC 2 Type II certified with annual audits. For PCI DSS: DataBrain reads financial data from your SQL warehouse via read-only credentials. Payment data lives in your warehouse and never transits through DataBrain’s storage layer. Confirm your specific PCI scope requirements with DataBrain’s security team before go-live. Read the full security posture at /security.

The typical in-house embedded financial analytics build runs $150K-$340K in engineering cost in year one, takes 6-12 months to a production-grade surface, and pulls in a multi-disciplinary data team spanning data engineering, BI, front-end, and the security and compliance work for tenant isolation and PCI scope. On top of engineering cost, you own the SOC 2 controls, audit logging, and ongoing multi-tenant security maintenance. DataBrain’s Growth plan starts at $999/month with multi-tenancy, unlimited seats, unlimited embeds, and one data source. See pricing.

Yes. That is the foundation of DataBrain’s architecture. Row-level RBAC is enforced at the query layer, not in application code. A payments merchant sees their transactions only. A lending borrower sees their loan book only. A bank partner sees their accounts only. A wealth client sees their portfolio only. You configure the tenant model once; DataBrain enforces it on every query automatically, across thousands of tenants from a single DataBrain instance.

DataBrain can track any financial KPI you define from your warehouse data. Use Visual Builder (drag-and-drop), Custom SQL, or AI Chat Mode to create metrics such as authorization rate, chargeback rate, NIM, AUM, delinquency, or loss ratio, then those metrics power the dashboards embedded in your product. Because you define metrics against your own data model, every KPI is specific to your schema and your customers, not a generic template.

Tableau and Power BI are standalone business intelligence tools your internal team logs into separately. Embedded analytics renders dashboards inside your own product, under your brand, with multi-tenant isolation so each customer sees only their own data. Power BI embedding also carries per-tenant licensing and theming limits that get costly at SaaS scale, which is why teams like EpochOS replaced it with DataBrain and went live in two weeks.

Most fintech teams ship their first live, customer-facing financial dashboard in about two to four weeks. DataBrain connects to your existing data warehouse over a read-only SQL connection in minutes, you define your financial metrics using Visual Builder, Custom SQL, or AI Chat Mode and assemble dashboards in one to three days, and embedding takes a single snippet. EpochOS moved from Power BI to a live embedded analytics surface in two weeks, with no new data hires.

Yes. Your end users can build reports, filter dashboards, and define their own metrics inside your product, with every query scoped to their own tenant data by row-level RBAC. You control how much self-service each role gets, from read-only dashboards to a full drag-and-drop builder and natural-language AI queries, all white-labeled with your brand.