Ertas for Finance

The Challenge

Financial institutions operate in one of the most data-rich and regulation-heavy environments on the planet. From risk modeling and fraud detection to regulatory reporting and customer communications, the potential applications for AI are enormous — but so are the stakes. A hallucinated compliance report or an inaccurate risk score can trigger regulatory action, financial loss, and reputational damage. Generic language models lack the domain knowledge to parse SEC filings, interpret Basel III capital requirements, or distinguish between legitimate transaction patterns and subtle fraud indicators.

At the same time, financial data is among the most sensitive categories of information that exists. Customer account details, trading strategies, proprietary risk models, and internal audit findings are all subject to overlapping regulatory frameworks — SOX for internal controls, PCI-DSS for payment data, SEC and FINRA rules for trading and advisory activities, and GDPR or CCPA for customer personal data. Sending any of this information to a third-party AI provider's API introduces data-residency, auditability, and vendor-risk concerns that most compliance teams will reject outright.

The Solution

Ertas provides a complete, self-contained AI pipeline that lets financial institutions build domain-specific models without exposing regulated data to external providers. Ertas Studio enables quantitative analysts and data science teams to fine-tune foundation models on proprietary financial datasets — earnings call transcripts, internal research notes, transaction records, and regulatory filings — using LoRA adapters for efficient, reproducible training. Models emerge with deep understanding of financial terminology, accounting standards, and market conventions that generic checkpoints lack.

Deployment happens entirely within the institution's own infrastructure. Ertas Cloud provisions private inference endpoints inside your VPC or on-premise data center, with every request logged for regulatory audit. Ertas Vault encrypts all training data and model artifacts at rest and in transit, enforces granular role-based access controls aligned with SOX segregation-of-duties requirements, and maintains a tamper-evident audit trail that satisfies examiner requests. The result is production-grade financial AI that your Chief Compliance Officer, CISO, and regulators can all sign off on.

Key Features

Example Workflow

A regional bank's risk analytics team wants to improve its fraud detection models. The team exports 200,000 anonymized transaction records — labeled with confirmed fraud cases — as a JSONL dataset and uploads them to Ertas Vault, which encrypts the data and verifies that no raw account numbers or customer PII remain. In Ertas Studio, the team selects a Mistral-7B base model from Hub and launches a LoRA fine-tuning run targeting transaction classification and anomaly explanation. After four hours of training on Ertas's managed cloud infrastructure, the adapter is merged and published to the bank's internal model registry. The model is then deployed as a private endpoint inside the bank's on-premise data center via Ertas Cloud, accessible only from the fraud monitoring pipeline. Within two weeks, the model is scoring transactions in real time, flagging suspicious patterns with explanations that analysts can review — reducing false positives by 35% while keeping all customer data within the bank's own network and generating the audit logs required for the next OCC examination.