Title: Healthcare Insurance Fraud Detection via Continual Fiedler Vector Graph Model
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: online learning, semi-supervised, fraud detection

Abstract: Healthcare insurance fraud detection presents unique machine learning chal-
lenges: labeled data are scarce due to delayed verification processes, and fraud-
ulent behaviors evolve rapidly, often manifesting in complex, graph-structured
interactions. Existing methods struggle in such settings. Pretraining routines
typically overlook structural anomalies under limited supervision, while online
models often fail to adapt to changing fraud patterns without labeled updates. To
address these issues, we propose the Continual Fiedler Vector Graph model (Con-
FVG), a fraud detection framework designed for label-scarce and non-stationary
environments. The framework comprises two key components. To mitigate la-
bel scarcity, we develop a Fiedler Vector-guided graph autoencoder that lever-
ages spectral graph properties to learn structure-aware node representations. The
Fiedler Vector, derived from the second smallest eigenvalue of the graph Lapla-
cian, captures global topological signals such as community boundaries and con-
nectivity bottlenecks, which are patterns frequently associated with collusive
fraud. This enables the model to identify structurally anomalous nodes without
relying on labels. To handle evolving graph streams, we propose a Subgraph At-
tention Fusion (SAF) module that constructs neighborhood subgraphs and applies
attention-based reweighting to emphasize emerging high-risk structures. This de-
sign allows the model to adapt to new fraud patterns in real time. A Mean Teacher
mechanism further stabilizes online updates and prevents forgetting of previously
acquired knowledge. Experiments on real-world medical fraud datasets demon-
strate that the Continual Fiedler Vector Graph model outperforms state-of-the-art
baselines in both low-label and distribution-shift scenarios, offering a scalable and
structure-sensitive solution for real-time fraud detection.

TL;DR: an online learning method for real world scenario in medical insurance fraud detection