The article discusses how model validation standards are evolving for large language model (LLM) based systems, particularly in the banking sector. It examines what aspects of traditional validation break down, what elements remain applicable, and how to effectively test the quality of LLM outputs. The piece offers practical insights into adapting validation playbooks for generative AI applications.
The article recounts a final‑year project in which the author trained six different models for fraud detection. It highlights the discrepancy between the model that performed best on evaluation metrics and the one that was ultimately chosen for production. The piece reflects on how real‑world constraints can override purely statistical performance.
IndicBankBench is a 799‑case benchmark designed to evaluate the safety and reliability of language model assistants in Indian retail banking. It covers five operational domains, a capability/refusal domain, and twenty primary axes, assessing each case at four stages: safety, action and tool use, response adequacy, and advisory quality. The benchmark uses deterministic safety checks, a narrow resolver for ambiguous confirmation‑before‑write scenarios, and an LLM judge for semantic response adequacy, reporting strict pass rates that reveal a gap between strict reliability (43.7%–58.2%) and at‑least‑once success (60%–74%).
A practical guide to combining pre-churn scoring with uplift modelling for smarter retention. The post How to Improve Customer Retention in FinTech appeared first on Towards Data Science .
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
arXiv:2606. 02755v1 Announce Type: cross Abstract: Large language model (LLM) applications are increasingly expected to satisfy deterministic institutional requirements while relying on probabilistic generative components.
arXiv:2607. 09712v1 Announce Type: new Abstract: Financial control testing increasingly depends on representative enterprise resource planning (ERP) data in quality environments, yet direct production copies expose personal, supplier, banking, and commercially sensitive records.
arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
arXiv:2609.24016v1 Announce Type: new Abstract: Commercial large language models are increasingly deployed across African fintech infrastructure for fraud detection and customer communication, yet no...
arXiv:2604. 24668v3 Announce Type: replace Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems.
A structured methodology for comparing candidate models, testing stability, and selecting a robust final score The post How to Train a Scoring Model in the Age of Artificial Intelligence appeared first on Towards Data Science .
Learn how OpenAI’s Model Spec serves as a public framework for model behavior, balancing safety, user freedom, and accountability as AI systems advance.
arXiv:2511. 07107v3 Announce Type: replace Abstract: Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment.