Quick Summary
AI for Financial Services: Fraud signals, credit support, regulatory document intelligence, and research assistants - model-agnostic, with RAG preferred when frameworks change weekly.
- ·We treat explainability as a first-class requirement. If a regulator asks why a score moved, you need evidence paths. Most FS work starts with RAG over controlled corpora before co...
- ·Typical use cases: Fraud / anomaly detection assist; Credit underwriting support workflows; Regulatory reporting document assist
- ·Compliance focus: SOC 2 aligned architecture, PCI DSS aware, GDPR, Model risk documentation
- ·Outcome signal: ~$260K - vs fine-tuning path on one risk framework POC
- ·Human-in-the-loop on irreversible decisions; audit trails by default
What gets in the way
We design for the constraints your teams already know - not a lab demo.
- Model risk management and inventory duties
- Vendor lock-in fears when pricing shifts
- Data residency and cross-border constraints
- Hallucinated citations destroy trust overnight
How we approach Financial Services
We treat explainability as a first-class requirement. If a regulator asks why a score moved, you need evidence paths. Most FS work starts with RAG over controlled corpora before considering fine-tuning.
Related case studyAI use cases we ship
- Fraud / anomaly detection assist
- Credit underwriting support workflows
- Regulatory reporting document assist
- Research / memo drafting over approved sources
- AML/KYC document intelligence
Patterns & products we deploy
Compliance & control
We design for the frameworks your buyers and auditors care about. Labels below mean architecture and process alignment - not a claim that Xenqube is certified under every badge on this page.
Questions Financial Services buyers ask
Fine-tune or RAG for proprietary risk frameworks?
Usually RAG first. Fine-tuning is for behavior and format; RAG is for knowledge that changes. We have replaced multi-hundred-thousand fine-tune proposals with validated RAG POCs in weeks.
Can you support our model risk process?
Yes - inventory, intended use, validation sets, monitoring, and change control. We write for how your MRM team already works, not a parallel shadow process.
Do answers cite sources?
Production research systems should. We insist on source attribution for knowledge Q&A so compliance can review provenance.