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Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications.

Sensors (Basel, Switzerland)·2021
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Robust Financial Fraud Alerting System Based in the Cloud Environment.

Branka Stojanović1, Josip Božić1

  • 1Joanneum Research, DIGITAL-Institute for Information and Communication Technologies, 8010 Graz, Austria.

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|December 11, 2022
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Summary
This summary is machine-generated.

This study introduces a cloud architecture for detecting financial fraud and profiling banking clients. It addresses cybersecurity risks in financial technology (fintech) to enhance system resilience.

Keywords:
anomaly detectioncloud securityfintechformal model checkingfraud detectionmachine learningrisk assessment

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Area of Science:

  • Computer Science
  • Information Security
  • Financial Technology (Fintech)

Background:

  • Digitalization in finance has led to new technological concepts like fintech, improving customer services and business value.
  • Fintech services require 24/7 availability, often utilizing cloud environments for ubiquitous device connectivity and online transactions.
  • Cloud-based fintech systems present significant information security challenges, including increased vulnerability to cyberattacks and potential for undetected financial fraud.

Purpose of the Study:

  • To propose a cloud-based system architecture for fraud detection and client profiling within the banking sector.
  • To systematically assess risks and infer exploitation probabilities for various cyberattack scenarios targeting fintech systems.
  • To analyze the consequences of security breaches and provide recommendations for enhancing fintech system resilience.

Main Methods:

  • Conducted a systematic risk assessment to evaluate security vulnerabilities in cloud-based fintech architectures.
  • Inferred exploitation probabilities for multiple attack scenarios through quantitative analysis.
  • Performed formal verification to determine the impact of successful vulnerability exploits on system security.

Main Results:

  • Identified key security challenges and risks associated with cloud-based fintech systems.
  • Quantified the likelihood of successful cyberattacks and their potential impact on financial transactions.
  • Demonstrated the effectiveness of the proposed architecture in addressing fraud detection and client profiling.

Conclusions:

  • The proposed cloud-based architecture offers a robust solution for fraud detection and client profiling in banking.
  • Addressing cybersecurity threats is crucial for maintaining trust and integrity in the evolving fintech landscape.
  • Continuous risk assessment and formal verification are essential for improving the resilience of fintech systems against evolving cyber threats.