An AI-Based Technical Framework for FinTech Risk Management in the Digital Economy

DOI

10.2991/978-94-6239-777-4_44How to use a DOI?

Keywords

FinTech; Risk Management; Artificial Intelligence; Digital Economy

Abstract

Basel-style capital rules and Value-at-Risk were built for slow-moving balance sheets, not for FinTech, where algorithmic lending, mobile payments, and cross-platform automation propagate shocks within minutes. Existing work either classifies FinTech risks without offering technical remedies, or applies AI to narrow tasks like fraud or credit scoring without a unifying architecture. I address this gap by drawing on complex adaptive systems theory to propose a four-level framework spanning technology infrastructure, data and algorithm, business operation, and systemic contagion. Each level is mapped to a functional module powered by isolation forests, NLP, XGBoost, GNNs, and Monte Carlo simulation. A logistic-regression baseline on a calibrated synthetic dataset (N = 5,000) yields AUC = 0.798, with coefficient signs and rankings consistent with the four-layer design. An illustrative scenario shows Module 2 isolating 340 suspicious accounts from a contaminated cluster, after which Module 3 issues tiered alerts.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Surun Mu
PY  - 2026
DA  - 2026/09/30
TI  - An AI-Based Technical Framework for FinTech Risk Management in the Digital Economy
BT  - Proceedings of the 2026 8th International Conference on Economic Management and Cultural Industry (ICEMCI 2026)
PB  - Atlantis Press
SP  - 411
EP  - 420
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-777-4_44
DO  - 10.2991/978-94-6239-777-4_44
ID  - Mu2026
ER  -