Abhimanyu Chakravyuha: An Evidence-Aware AI Framework for Fraud Intelligence Through Semantic Analysis, Mutation Detection, and Graph-Based Reasoning

Abstract: Fraudulent activities increasingly involve evolving narratives, altered textual claims, interconnected entities, and coordinated patterns that cannot always be identified by examining individual records in isolation. This paper presents Abhimanyu Chakravyuha, a proposed AI-powered fraud intelligence framework integrating natural language processing, semantic similarity analysis, mutation detection, graph-based relationship analysis, and evidence-aware verification.

The framework processes textual claims and related records, retrieves semantically relevant evidence, identifies potential modifications to previously observed patterns, and represents relationships using a graph structure. An evidence- verification component evaluates whether available information supports, contradicts, or fails to establish a claim. An abstention mechanism allows the system to return an inconclusive result when evidence is insufficient or conflicting. The design emphasizes evidence traceability, uncertainty management, and human oversight. It separates semantic similarity from factual support, suspicious patterns from confirmed fraud, and automated analysis from human-reviewed decisions. This paper specifies the architecture, methodology, and experimental design; quantitative performance is not claimed without experimental validation.

Keywords: Fraud Intelligence, Natural Language Processing, Semantic Similarity, Claim Verification, Mutation Detection, Knowledge Graphs, Explainable AI, Evidence Retrieval, Abstention, Uncertainty Calibration