Industry: Fintech

Knowledge graphs for fintech.

Real time fraud detection, alternative data underwriting, KYC/AML with transitive beneficial ownership, and grounded LLM copilots: built for fintech speed and audit ready under sponsor bank and regulatory scrutiny. Live in weeks, not quarters.

Request a scoping call ← Back to knowledge graph consulting

Why fintechs need graphs, not just more ML models

Every Signal You Care About Is a Relationship

Fraud isn't a feature of one transaction; it's a pattern across many. Creditworthiness isn't a bureau score; it's a graph of cash flow, employer, gig activity, and network reputation. A knowledge graph is the substrate that lets you model, query, and audit these relationships.

Speed That Sponsor Banks and Regulators Accept

Fintechs move fast. Sponsor banks and regulators move carefully. A graph substrate resolves the tension: your team ships new use cases weekly, but every decision comes with a defensible traversal path and audit log the compliance side can point at.

The AI Layer Every Fintech Wants to Ship

Once the graph exists, the LLM copilots (support, compliance, ops) become trivial to ground correctly. That's the AI product every fintech PM has been trying to ship since 2023: finally on a substrate that survives audit.

Six proven fintech use cases

Real time fraud and account takeover graphs

Model accounts, devices, IPs, cards, and behaviors as a graph. Community detection surfaces fraud rings before the first chargeback. Sub 100ms decisioning latency at authorization time.

•Fraud detection lift on rings and ATO patterns rule engines miss
•Explainable declines with the exact suspicious relationship shown
•Continuously refreshed as bad actor tactics evolve

Alternative data underwriting

Traditional bureau data plus cash flow, employer, gig work, utility, and network signals combined as a graph. Better decisions on thin file and near prime applicants without sacrificing explainability.

•Higher approval rates without materially higher default rates
•Adverse action reasons that trace to specific graph paths (ECOA friendly)
•Rapid A/B testing on new data source integrations

KYC / AML / sanctions with beneficial ownership

Transitive screening across customers, corporate structures, sanctions lists, and adverse media. Beneficial owner trees kept current from company registry feeds and news signals.

•Detect PEP and sanctions exposure via indirect ownership chains
•Continuous re screening as ownership and lists change
•Auditable path for every alert and every dismissal

Customer 360 for growth and retention

Resolve customers across products, devices, sessions, and support interactions. Segmentation and lifetime value modelling with real relationship context.

•More accurate churn and LTV predictions
•Cross sell recommendations grounded in real relationship networks
•Foundation for downstream generative AI experiences

Grounded copilots for support, ops, and compliance

GraphRAG assistants over your product docs, policies, ticket history, and customer graph. Every answer cites the exact policy or customer record field it came from.

•Support handle time reductions with grounded, cite checked answers
•Compliance and ops teams that trust AI answers because they can audit them
•Zero fabricated policy citations

Regulatory reporting and lineage

For fintechs partnered with sponsor banks, regulators expect audit ready lineage from transaction to submitted report. A graph makes that lineage a query instead of a data warehouse rebuild.

•Faster response to sponsor bank and regulator RFIs
•Transformation level lineage without a dedicated data catalog project
•Reduced reconciliation effort at month end and quarter end

Frequently asked questions

How is this different from off the shelf fraud or KYC vendors?+

Off the shelf vendors are trained on generic patterns and cannot see your customer graph. A knowledge graph you own gives you fraud detection, KYC decisions, and adverse action reasons based on your customer relationships: with explainability regulators can audit. Most fintechs run both: vendors for baseline coverage, the graph for lift and defensibility.

How fast can we ship the first use case?+

6 to 10 weeks for a fraud or KYC pilot over your existing data plus one third party source. Alternative data underwriting is usually 10 to 14 weeks because it involves policy work with credit and compliance teams. Grounded copilots are 6 to 8 weeks over an existing knowledge base.

We're partnered with a sponsor bank. Does that constrain things?+

It shapes the deployment model. Data residency, encryption, key management, and audit log requirements need to match your sponsor's expectations. We build to those from day one: on prem, VPC, or your sponsor's approved cloud region.

What tech do you use?+

Neo4j or Amazon Neptune for the graph. Real time inference on a mix of graph algorithms (Neo4j GDS, custom Cypher/Gremlin) and downstream ML. LLMs from OpenAI, Anthropic, Google, or open weight (Llama, Mistral) hosted in your VPC when needed.

How do you handle explainability for ECOA / adverse action letters?+

Every declined application gets an explainability trace: the specific graph path or ML feature that drove the decision, mapped to standard adverse action reason codes. This is the same substrate compliance uses for regulator inquiries.