Solutions · Financial Services
Governed analytics for banks, lenders, and fintechs, from fraud monitoring to regulatory reporting, on infrastructure you control.
See how Analytify ships financial services analytics dashboards your team and customers will actually use.
Financial institutions sit on more data per dollar of revenue than almost any other industry, yet most still run reporting on legacy data warehouses, spreadsheets, and BI tools that were built before cloud, before streaming, and before AI. The result: fraud is caught after the fact, regulatory reports take weeks, and product teams ship features blind.
Modern financial services analytics changes that economics. Cloud lakehouses (Snowflake, Databricks) handle billions of rows; streaming engines flag fraudulent transactions in milliseconds; semantic layers ensure “active customer” and “AUM” mean the same thing in compliance, marketing, and finance reports; embedded analytics give your fintech end users the same dashboard quality your internal analysts have.
The business case writes itself: a 1-basis-point reduction in fraud loss on $10B annual flow is $1M; a one-day cut in regulatory reporting cycle frees finance team capacity; a personalised offer engine grounded in clean customer data lifts cross-sell by 8-15%.
Financial services analytics dashboards typically track 8-12 KPIs across these categories. Each KPI should have a definition, target, owner, and refresh cadence in your semantic layer.
| KPI | Category | Refresh | Typical Owner |
|---|---|---|---|
| Net Interest Margin (NIM) | Margin | Daily | Treasury |
| Loan Loss Provisions | Risk | Monthly | Risk / CFO |
| Fraud Loss Rate (bps) | Risk | Real-time | Fraud Ops |
| Customer Acquisition Cost | Growth | Monthly | Marketing |
| Customer Lifetime Value | Growth | Quarterly | Marketing |
| Cost-to-Income Ratio | Efficiency | Monthly | CFO |
| Capital Adequacy Ratio (CAR) | Compliance | Quarterly | Risk |
| Regulatory Report SLA | Compliance | Per-cycle | Compliance |
| Digital Channel Adoption | Product | Weekly | Digital |
| Net Promoter Score | CX | Monthly | CX Team |
Stream every authorisation through a feature pipeline that scores it against a fraud model in under 50ms. Block, step-up authenticate, or pass-through based on the score. Modern stacks combine Kafka or Kinesis ingestion, Flink feature engineering, and an ML model served by SageMaker, Vertex AI, or Snowflake Cortex. The analytics layer surfaces fraud-rate trends, false-positive rates, and channel-level anomalies to the fraud-ops team in real time.
Build governed datasets in your warehouse with full lineage from source systems through transformation to final report. Versioned dbt models guarantee reproducibility for audit. Self-service exploration lets compliance analysts answer regulator follow-ups in hours, not weeks. Period-end reports run on a fixed schedule with automated quality tests.
Unify checking, savings, credit card, mortgage, and wealth records into a single customer record using master data management. Score each customer for next-best-product propensity, then push the recommendation back to RM portals and digital channels via reverse ETL. Track lift through experiment dashboards.
Run scenario simulations on portfolios in Snowflake or Databricks. Surface VaR, expected shortfall, and concentration metrics in interactive dashboards. CRO teams drill from portfolio totals down to obligor and counterparty in seconds.
Give your end customers (consumers, SMBs, advisors) personalised analytics inside your app, spending breakdowns, budget vs actual, portfolio performance, tax projections. White-labelled, mobile-responsive, with row-level security ensuring each user sees only their own data.
Combine transaction monitoring rules with ML-based behavioural models to flag suspicious patterns. Investigators see a unified case view with transaction history, network analysis, and supporting evidence. Audit trail is automatic.
A financial services analytics platform should integrate cleanly with the data sources your industry actually uses:
| Category | Examples |
|---|---|
| Core banking | FIS, Fiserv, Temenos, Mambu, via CDC or batch extracts |
| Card processors | Visa DPS, Marqeta, Galileo, Adyen Issuing |
| CRM | Salesforce Financial Services Cloud, MS Dynamics, HubSpot |
| Risk and compliance | Moody's, FICO, Refinitiv, SAS Risk |
| Market data | Bloomberg, Refinitiv, Polygon.io, IEX Cloud |
| Customer comms | Twilio, SendGrid, Iterable |
| Cloud warehouses | Snowflake, Databricks, BigQuery, Redshift |
| Streaming | Kafka, Confluent, AWS Kinesis, Google Pub/Sub |
Financial services analytics has the highest compliance bar of any industry vertical. Your platform must be auditable, governable, and demonstrably secure across multiple frameworks:
Practically this means: row-level security tied to entitlements, full audit logging on every query and export, encrypted at rest and in transit, support for VPC deployment or fully on-prem, fine-grained access controls, immutable lineage, and signed BAAs/DPAs.
A 250-employee challenger bank with $3B in deposits replaced a legacy on-prem BI stack with a modern Snowflake + dbt + embedded analytics deployment. Outcomes after 9 months:
Some financial services analytics teams are tempted to build everything in-house. Here is the honest comparison:
| Dimension | Build In-House | Buy (Analytify) |
|---|---|---|
| Time to first dashboard | 6-12 months | 2-4 weeks |
| Engineering team needed | 4-8 FTEs | 1-2 FTEs |
| Embedded analytics for fintech app | 6+ months | SDK in days |
| Compliance evidence | assemble yourself | SOC 2 + DPA + BAA |
| Total 3-year cost | $3M-$8M | $300K-$900K typical |
| Risk | maintenance burden, audit gaps | vendor dependency |
Analytify is built for the financial services analytics use case from day one:
Ready to ship modern financial services analytics dashboards for your team and your customers?
Compliance evidence (SOC 2, PCI, GDPR), row-level security, full audit logging, lineage, VPC/self-hosting options, and integrations with core banking, card processors, and risk systems. Generic BI tools without these often fail bank security reviews.
Yes for most use cases. Tokenisation preserves referential integrity for joins. Cardholder data analytics typically run on tokens; PII analytics use masking or differential privacy where the use case allows.
Authorisations flow through Kafka or Kinesis; a feature engineering job (Flink, Spark Streaming) computes features (velocity, geo-velocity, device, merchant); a model serves a score in <50ms; downstream analytics dashboards monitor fraud rate, FPR, and channel anomalies.
Most financial services teams need both, often via a lakehouse (Databricks, Iceberg on S3, Snowflake Iceberg tables). Raw and unstructured data lands in the lake; curated, governed analytical models live in the warehouse layer.
Versioned transformations (dbt with git tags), immutable raw data zones, signed report runs with parameter logs, and automated dbt tests. Auditors can re-run a 2024 report against 2024 source state and get the exact same number.
Yes, many regulated banks run open-source BI in production. The key is the operational wrapper: SOC 2, support contracts, vulnerability management, and signed DPA/BAA. Analytify provides those on top of an open-source core.
Analytify ships a JavaScript SDK and an iframe embedding option, with row-level security enforced server-side. Each end user sees only their data, branded as your product.
Typical phases: 2-4 weeks for connector setup and core dashboards; 6-10 weeks for governed semantic layer and 5-8 priority dashboards; 3-6 months for full stack including embedded analytics and regulatory reporting. Faster than traditional BI rebuilds because the building blocks are productised.