Financial Services AI.
Compliant AI for regulated financial workflows.
Fine-tune LLMs on your financial data — earnings reports, regulatory filings, risk models, and customer communications — with full data residency control, PII guardrails, and audit-ready logging.
The Problem
Financial firms face strict data residency rules (GDPR, SOX, FINRA) that make it impossible to use public LLM APIs with client data. Meanwhile, generic models lack the financial domain knowledge for accurate analysis.
How Langtrain Helps
Purpose-built for Banking, FinTech & Insurance.
Data Residency
Train and serve the model on infrastructure in the region you choose, so client data stays where your regulations require.
Client Data Isolation
Each client's data is processed in isolated compute environments. No cross-contamination, full audit trails, role-based access control.
Financial Domain Knowledge
Fine-tune on SEC filings, IFRS/GAAP standards, earnings call transcripts, and market research for analyst-grade accuracy.
Risk & Fraud Detection
Train specialized classifiers for fraud pattern detection, AML alerts, and credit risk scoring on your proprietary transaction data.
Structured Data Extraction
Extract key metrics from PDF financial statements, annual reports, and regulatory filings into structured JSON automatically.
Real-time Decisioning
Low-latency inference for underwriting, fraud scoring, and dynamic pricing, deployed on your own infrastructure.
Applications
What teams actually build.
Earnings Analysis
Automatically process earnings call transcripts and 10-K filings. Extract key metrics, compare to guidance, and generate analyst summaries.
AML & Fraud Detection
Fine-tune on your labeled transaction history to detect money laundering patterns, synthetic identities, and fraudulent claims.
Regulatory Document Processing
Classify and extract data from ISDA agreements, KYC documents, loan applications, and insurance policies at scale.
Client Communication Drafting
Generate compliant, personalized investment communications that reference actual portfolio performance and comply with disclosure requirements.