The research behind the platform
Titan publishes its evaluation frameworks, benchmarks, and findings so institutions can evaluate AI on what matters for banking.
Titan's Series on Deploying AI Safely and Effectively in Financial Institutions
Part of a continuing series
This research is Part 1 and Part 2 of Titan's continuing series on deploying AI safely and effectively in financial institutions. Each paper addresses a distinct layer of the problem: how to evaluate AI for banking, and how to give AI the contextual grounding to perform in a regulated environment.
The Banker Trust Index
The Banker Trust Index
Most AI benchmarks were built for consumer chatbots. The Banker Trust Index (BTI) was built for regulated banking: a five-component framework measuring expert preference, domain knowledge under stress, response reliability, consistency, and specificity. BTI maps explicitly to SR 26-2 model validation activities so risk teams can integrate it into existing governance programs.
An independent judge preferred Titan's responses 77% of the time on average across leading frontier models
Titan's margin over frontier reference models on BTI composite score
RAGAS accuracy, vs. 26.7% to 56.9% across the six comparator models
The Knowledge Graph Index
The Knowledge Graph Index
Context is why AI fails to scale in banking. This paper introduces the banking context layer: the structured, traceable knowledge infrastructure that gives any AI model the domain grounding to reason accurately in a regulated environment. It explains how Titan's proprietary Banking Knowledge Graph works, why retrieval-augmented generation alone is insufficient, and how model-agnostic context enrichment compounds in value as frontier models improve.