Titan has introduced a range of AI models tailored specifically for banks, credit unions, and regulated financial technology firms.

These AI models were developed by a multidisciplinary team comprising former banking professionals, regulatory experts, compliance leaders, and specialists in applied artificial intelligence and machine learning. According to Titan, this diverse expertise was crucial in embedding domain-specific reasoning directly into the model from its inception.

Titan has shared internal and external benchmarks to validate its claims about model performance. When evaluated with the Retrieval Augmented Generation Assessment (RAGAS) framework, their models achieved a 76% accuracy rate in answers, compared to 54% for ChatGPT and 47% for Gemini. In terms of correct responses, Titan’s systems scored 82%, against 70% for ChatGPT and 66% for Gemini.

Additionally, the company devised a proprietary Banker Trust Index (BTI), which assesses AI performance across key metrics relevant to regulated environments such as safety, reliability, and compliance alignment. Titan claims that its models outperformed general-purpose language models in all measured categories of this index.

Performance Analysis

While acknowledging that general-purpose AI systems excelled on some RAGAS metrics like Faithfulness and Answer Relevancy, Titan argues that these metrics may not fully capture the nuances required in banking contexts. The company believes that banking queries often necessitate additional regulatory or policy context beyond the content retrieved, leading to more comprehensive answers despite potentially lower faithfulness scores.

Model Architecture and Deployment

Titan’s models are based on a banking ontology that integrates regulatory guidelines, risk management principles, and operational procedures into the model architecture. This design ensures that responses include traceable reasoning, making them suitable for audit purposes.

The company aims to deploy these models near an institution’s own data sources to minimize latency and enhance predictability. A human-in-the-loop mechanism is incorporated to support decision-making processes without fully replacing the expertise of bankers.

This platform is targeted at financial institutions under pressure to integrate AI technologies while adhering to stringent compliance requirements. The increasing scrutiny from regulators over general-purpose AI’s potential for generating inaccurate or inconsistent information in critical decisions highlights the need for domain-specific AI solutions that prioritize explainability, auditability, and regulatory alignment.