Banking, FinTech & Insurance
Financial Services AI.Keeps financial data in your environment.
Fine-tune open models on your financial data (earnings reports, regulatory filings, risk models and customer communications) and keep that data in your own environment.
- Model weights, exportableYours
- Where client data staysIn-houseWhen you train and serve on your own infrastructure.
- Account audit log exportCSV
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 your own infrastructure, so client data stays wherever that infrastructure runs.
A Model Per Client
Train a separate adapter for each client or desk, so one client's data never shapes another's model.
Financial Domain Knowledge
Fine-tune on filings, accounting standards, earnings call transcripts and your own research, then test it against your own benchmark.
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.
- 01
Earnings Analysis
Automatically process earnings call transcripts and 10-K filings. Extract key metrics, compare to guidance, and generate analyst summaries.
- 02
AML & Fraud Detection
Fine-tune on your labeled transaction history to detect money laundering patterns, synthetic identities, and fraudulent claims.
- 03
Regulatory Document Processing
Classify and extract data from ISDA agreements, KYC documents, loan applications, and insurance policies at scale.
- 04
Client Communication Drafting
Generate compliant, personalized investment communications that reference actual portfolio performance and comply with disclosure requirements.
Check it first
Don't take our word for it. Test it on your own cases.
- 01
Hold back real cases
Keep 50 to 100 real examples out of the training data, with the answers you would accept.
- 02
Ask both models
Put the same questions to the base model and to your fine-tune, and read the answers side by side.
- 03
Have an expert review
Before anything ships, an analyst checks the answers and decides whether it is good enough.
How to run it: the quick start
No certifications yet: we have not completed a SOC 2 or ISO 27001 audit. For regulated data, train in your own environment.
Ready for Banking, FinTech & Insurance?Start with your own data.
Talk to us, or start fine-tuning on the free plan.