The role of AI and machine learning in fraud detection and financial security

This technical article explores the transformative role of artificial intelligence and machine learning technologies in strengthening financial security frameworks and combating fraudulent activities across digital platforms. As financial transactions increasingly migrate to digital environments, traditional security measures prove inadequate against sophisticated fraud tactics. The article examines how AI-driven solutions leverage behavioral analytics, anomaly detection, and biometric authentication to create adaptive security systems that continuously evolve against emerging threats. By analyzing implementation strategies across major financial networks including credit card processors, mobile banking platforms, and cryptocurrency exchanges, the article provides a comprehensive overview of current applications while highlighting the challenges and opportunities that will shape the future landscape of financial security. Furthermore, the article identifies critical research priorities including quantum-resistant algorithms, federated learning approaches, explainable AI methodologies, and human-AI collaboration frameworks that will define the next generation of financial security systems in an increasingly complex threat environment.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17275555
Primary Topic
Infectious Diseases and Tuberculosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The role of AI and machine learning in fraud detection and financial security

Zenodo (CERN European Organization for Nuclear Research)
Infectious Diseases and Tuberculosis
article

The role of AI and machine learning in fraud detection and financial security

article en

Abstract

This technical article explores the transformative role of artificial intelligence and machine learning technologies in strengthening financial security frameworks and combating fraudulent activities across digital platforms. As financial transactions increasingly migrate to digital environments, traditional security measures prove inadequate against sophisticated fraud tactics. The article examines how AI-driven solutions leverage behavioral analytics, anomaly detection, and biometric authentication to create adaptive security systems that continuously evolve against emerging threats. By analyzing implementation strategies across major financial networks including credit card processors, mobile banking platforms, and cryptocurrency exchanges, the article provides a comprehensive overview of current applications while highlighting the challenges and opportunities that will shape the future landscape of financial security. Furthermore, the article identifies critical research priorities including quantum-resistant algorithms, federated learning approaches, explainable AI methodologies, and human-AI collaboration frameworks that will define the next generation of financial security systems in an increasingly complex threat environment.

Zenodo (CERN European Organization for Nuclear Research)
Shumei University (JP)
Peace, Justice and strong institutions
Openalex Percentile: Top 33%
Infectious Diseases and Tuberculosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.