Oculi: A Conversational Agentic Platform for Automated Credit Risk Analysis
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
StocksTalk is a voice‑enabled conversational agent that turns spoken financial screening requests into validated structured queries over real‑world market data. It integrates streaming speech recognition, retrieval‑augmented constraint extraction, schema‑grounded LLM‑based SQL generation, rule‑based validation, and human‑in‑the‑loop verification, exposing intermediate reasoning artifacts for user inspection. A benchmark of 150 spoken prompts shows that its retrieval grounding, constrained query generation, and interactive verification improve constraint extraction accuracy, SQL executability, logical consistency, and multi‑turn stability over baseline LLM approaches.
arXiv:2607. 04103v3 Announce Type: replace-cross Abstract: Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management.
arXiv:2607. 19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance.
arXiv:2506.11635v2 Announce Type: replace-cross Abstract: Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to...
arXiv:2511. 07322v3 Announce Type: replace-cross Abstract: While LLMs have shown great success in financial tasks like stock prediction and question answering, their application in fully automating Equity Research Report generation remains uncharted territory.
Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language models on financial tasks. However, it narrowly deals with periodic reporting from publicly traded companies (SEC 10-K and 10-Q filings), and its agentic harness relies on naive, unenriched chunk retrieval.