Fund‑flow dashboard: a fintech portfolio idea
A publicly shared fund‑flow tracking dashboard uses graph analytics and ML to surface suspicious banking transactions—a concrete portfolio demo for fintech fraud detection shared. The post was paired with research on explainable AI for DeFi and hierarchical deep learning for financial surveillance, offering ready blueprints for reproducible projects.
Public reproducible pipelines exist for AML-style fund‑flow demos; Feedzai’s research repo supplies code to reproduce supervised and anomaly‑detection experiments on the Elliptic Bitcoin [dataset github.com]. The canonical Elliptic dataset contains 203,769 transaction nodes, 234,355 directed edges, and 166 per‑node features for temporal graph [experiments arxiv.org], and the larger Elliptic2 corpus documents ~49 million node clusters, ~196 million transactions and ~122K labeled suspicious subgraphs for subgraph‑level laundering [detection kaggle.com]. Graph model choices used in comparable demos are concrete: GraphSAGE for inductive node embeddings (Hamilton et al., 2017) [arxiv.org], GIN/GraphSAGE implementations on GitHub for Elliptic [experiments github.com], and hierarchical graph‑attention networks shown effective in fraud detection [benchmarks sciencedirect.com]. Explainability components tied to DeFi and on‑chain flows are emerging in peer‑reviewed work, for example a January 13, 2026 MDPI paper that builds an Optuna‑tuned SuperLearner ensemble with explainability for DeFi [valuation mdpi.com], while cross‑domain XAI surveys were updated through 2025–2026 to formalize interpretable methods for [finance arxiv.org]. Benchmarks from recent preprints show high synthetic performance but realistic constraints: a June 2025 arXiv framework reported 98.2% F1 (97.8% precision, 97.0% recall) on a simulated streaming transaction task using regulatory graphs and [GNNs arxiv.org], whereas practitioner repos repeatedly flag label scarcity and distribution shift as deployment hurdles in real banking [data github.com]. A compact reproducible recipe seen across demos: load the PyTorch‑Geometric Elliptic temporal [loader pytorch-geometric.readthedocs.io], train an inductive GraphSAGE or GIN model using existing example [repos github.com], attach SHAP for local explanations to model [outputs github.com], and visualize fund flows with Sankey/NetworkX charts for notebook or Streamlit [dashboards chartexpo.com].
