Your core wasn’t built for AI. Your data layer can be.
Community and regional banks are using Fluree to unify siloed data across core, loan, treasury, and ancillary systems — creating an AI-ready foundation that makes bankers smarter without replacing the systems that work. Jack Henry. Fiserv. FIS. Legacy. Whatever your core is, Fluree connects to it. Your data doesn’t move; intelligence does.
Book a Banking Demo Watch the Webinar
What Is Core Banking AI
Modernize without replacing the core.
Your core banking system was installed before the iPhone existed. The board wants AI insights; your data still lives in silos that don’t talk to each other. Your lenders toggle across four screens and still miss the full customer relationship.
Meanwhile, 44% of new checking accounts go to digital banks and fintechs. You can’t afford a $2–5M, 18-month core replacement. You also can’t afford to wait.
Fluree creates a semantic layer over your existing core, LOS, and ancillary systems — one AI-ready graph in 4–8 weeks, not a rip-and-replace.
Connect any source.
CSV, API, Postgres, Snowflake, Salesforce — Fluree ingests it as-is. No schema migration. No pipelines to maintain.
Search 300+ sources…AVAILABLE SOURCESSalesforceApp · OAuthSnowflakeData lakePostgresDatabase · replicacustomers.csvCSV · 1.24M rows1.24M rows stagedstreaming to Fluree · CONNECTED
The graph builds itself.
Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.
CustomerOrderProductContractOwner
Answers, with receipts.
Ask in plain language. Every answer traces back to the exact row it came from — for humans, agents, and apps alike.
NLMCPRESTSPARQLTop accounts at risk this quarter?SPARQL · GENERATEDSELECT ?acct ?arr WHERE { ?acct a fin:Account ; fin:risk "high" ; fin:arr ?arr .} ORDER BY DESC(?arr)ANSWER3 accounts at elevated riskAcme RoboticsNorthwind CoGlobex CorpTOTAL EXPOSURE$1.84M ARR exposedtraced · 4 sources
Why Banks Choose Fluree
Six capabilities no core vendor or warehouse gives you on its own.
Jack Henry, Fiserv, FIS, or legacy — Fluree connects, doesn’t replace
Your core banking system stays exactly where it is. Fluree connects through standard APIs and creates a semantic layer across the 4–8 systems most community banks already operate — no rip-and-replace, no 18-month core migration.
- Jack Henry, Fiserv, FIS, and legacy systems supported
- Core, LOS, treasury, docs, and ancillary systems unified
- No data movement — intelligence moves, data stays
One graph across core, LOS, treasury, and document systems
Customer data lives across 3–5 reporting systems that compete for the same answer. Fluree resolves customers, accounts, loans, properties, and households into a unified graph — one durable identity with lineage back to every contributing source.
- Customers, households, accounts, loans, properties resolved
- Golden records with lineage to every source system
- One truth across the enterprise, not per-screen
Credit, lending, ops, and compliance teams ask questions directly
Credit officers, lenders, and compliance leaders ask questions in plain English — no SQL, no analyst queue, no waiting for dashboards. Queries return cited, source-attributed answers in seconds.
- Natural-language queries across every system
- Dashboards generated on the fly, not waited on
- No specialist training required for business users
95%+ accuracy with full data lineage on every answer
Fluree only answers from actual data relationships inside the unified knowledge graph. Every response is grounded in verified operational records with lineage back to the exact source. No generative guesswork.
- GraphRAG — answers over declared paths, not fuzzy similarity
- Lineage attached to every value returned
- Defensible when the examiner asks why
Immutable lineage + policy-aware access for FDICIA, CRA, BSA/AML
As you cross $500M, $1B, or $10B, reporting expectations rise while the compliance team stays lean. Fluree preserves immutable data lineage, policy controls, and a queryable trail for every answer — scale regulatory reporting without scaling headcount.
- Immutable audit trail across every source
- Role-based access logged for every query
- FDICIA, CRA, and BSA/AML workflows supported
Proof-of-value without an 18-month core or warehouse project
Most community banks run production queries in 4–6 weeks. Full deployment is typically 8–12 weeks — a fraction of a core replacement (18–24 months, $2–5M+) or a ground-up warehouse (6–12 months of ETL pipeline work before any insight lands).
- Production queries in 4–6 weeks
- Full deployment in 8–12 weeks, typical
- Incremental value — every week, more answers unlocked
| Capability | Traditional Core vendor or warehouse |
Fluree Semantic layer |
|---|---|---|
| Data scope | Core only, or siloed warehouse | All banking systems unified via semantic layer |
| Cross-system queries | Not possible, or months of ETL | Native — day one |
| AI accuracy | N/A, or ~70–80% vector RAG | 95%+ via GraphRAG, every answer cited |
| Natural language | SQL or analyst queue required | Plain English for bankers and compliance |
| Entity resolution | Manual or not supported | Automatic — customers, households, properties |
| Audit trail | Core-only or partial lineage | Immutable, source-to-answer |
| Time to value | 12–18+ months | 4–8 weeks |
| Scope of change | Rip-and-replace or warehouse rebuild | Connects to the systems you already have |
| Security model | Application-layer only | Embedded in the data graph |
In Production
Live at a community bank — 330,000+ customers unified.
Fluree helps us with Master Data Management by getting quality into and out of our information. The more quality we have within our data, the more we can help anticipate the needs of our customers.
Brent Wilke · Chief Data Officer, First Bank (Nasdaq: FBNC)
\ \ Webinar replay\ Julia Bardmesser & Eliud Polanco From Defense to Offense — The Data Strategy Shift Redefining Financial Services \ Turning data from a compliance cost center into a revenue engine — playbooks from Citi, Deutsche Bank, and Voya Financial.\ Watch replay](/content/resources/videos/financial-services-knowledge-graph-webinar/index.html) \ Why it matters Why Your Enterprise AI Hallucinates — and the Science Behind Fixing It \ The architecture choices that keep generative AI defensible in regulated banking environments.\ Read the article](/content/blog/why-your-enterprise-ai-hallucinates-and-the-science-behind-fixing-it/index.html) Banking playbook AI-driven customer MDM for banks. \ One-pagerAI-driven customer MDM for banks.\ flur.ee\ Download one-pager \ Practitioner deep-dive Improving Master Data Management With Supervised Machine Learning \ How ML-assisted matching beats brittle string-rule MDM — and what to look for when you evaluate it.\ Read the article](/content/blog/improving-master-data-management-with-supervised-machine-learning/index.html)
FAQ
Do we need to replace our core banking system?
How long does implementation take?
Do we need data scientists or AI specialists?
How does Fluree AI avoid hallucinations?
What about regulatory compliance?
How is Fluree different from our core vendor’s analytics?
Can Fluree help with M&A integration?
How does pricing work?
Recognized by Gartner
See the full Financial Services solution Book a Banking Demo Watch the Webinar