{
  "annotation_id": "f60afd5c-ccea-458f-aeb6-efb272eb0eff",
  "date_created": "2026-02-17T05:53:07.510000Z",
  "date_modified": "2026-02-17T05:53:07.510000Z",
  "file_hash": "d1891ad8508a27474acfd0e0c882b6630a3212f4115329fd26db3c09f7caf5ae",
  "private": false,
  "record": {
    "abstract": "The rapid proliferation of synthetic media, presentation attacks, and document forgeries has created significant vulnerabilities in Know Your Customer (KYC) workflows across financial services, telecommunications, and digital-identity ecosystems. Traditional monolithic KYC systems lack the scalability and agility required to counter adaptive fraud. This paper proposes an Agentic AI Microservice Framework that integrates modular vision models, liveness assessment, deepfake detection, OCR-based document forensics, multimodal identity linking, and a policy driven risk engine. The system leverages autonomous micro-agents for task decomposition, pipeline orchestration, dynamic retries, and human-in-the-loop escalation. Experimental evaluations demonstrate improved detection accuracy, reduced latency, and enhanced resilience against adversarial inputs. The framework offers a scalable blueprint for regulated industries seeking robust, real-time, and privacy-preserving KYC verification.",
    "arxiv_id": "2601.06241",
    "authors": [
      "Chandra Sekhar Kubam"
    ],
    "categories": [
      "cs.CR",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines",
    "url": "https://arxiv.org/abs/2601.06241",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e82c27ea-c403-45b1-a3fd-8d41be3b8e55",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}