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September 5, 2025International Journal of Social Science Research and ReviewOpen Access

AI in Banking Risk Management and Fraud Detection in Preventing Financial Crimes and Optimizing Credit Decisions

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Authors

TSTanveer Ahmed Siddquee

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Overview

Quantitative analysis improved credit decisions and fraud detection in banking, highlighting key AI advantages.

Key Points

  • AI-based systems improved loan approval rates to 78% and reduced default rates to 6%, enhancing overall effectiveness.
  • Comparative analysis showed significant improvements in processing time—down from 45 minutes to just 12 minutes.
  • Banking professionals expressed concerns about AI explainability and human oversight as critical for implementation success.
  • Legacy system incompatibility and real-time data access issues were major barriers identified in the AI implementation process.

Cite This Study

Tanveer Ahmed Siddquee (2025) studied this question.

synapsesocial.com/papers/68c238d2b210217d6477958ehttps://doi.org/10.47814/ijssrr.v8i8.2889
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial Intelligence in Modern Banking: Revolutionizing Financial Services, Risk Management and Customer Experience2025
  2. 2Ethical and Regulatory Challenges of Deploying AI Powered Credit Management Systems in Banks2025
  3. 3Artificial Intelligence and Fraud Detection in US Commercial Banks: Opportunities and Challenges2025 · 2 citations
  4. 4Artificial intelligence-based risk management for the banking sector: impact and challenges2025
  5. 5Artificial Intelligence in Banking Fraud Detection: Enhancing Security Through Intelligent Systems2024 · 2 citations