AI-augmented decision-making in financial services

Artificial intelligence has fundamentally transformed decision-making across the financial services industry. By integrating machine learning with human expertise, financial institutions now leverage AI systems to conduct real-time risk assessment, detect and prevent fraud, optimize investment portfolios, and provide enhanced decision support to professionals. These AI-augmented approaches enable faster analysis of transaction data, more accurate identification of patterns and anomalies, improved forecasting capabilities, and data-driven investment strategies. While delivering significant advantages in operational efficiency, credit risk management, fraud prevention, and investment performance, the industry still faces challenges related to regulatory compliance, model transparency, and data privacy. Looking forward, explainable AI, federated learning, natural language processing, and quantum computing represent promising directions for further advancing financial decision-making capabilities

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17212280
Primary Topic
Economic, financial, and policy analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-augmented decision-making in financial services

Zenodo (CERN European Organization for Nuclear Research)
Economic, financial, and policy analysis
article

AI-augmented decision-making in financial services

article en

Abstract

Artificial intelligence has fundamentally transformed decision-making across the financial services industry. By integrating machine learning with human expertise, financial institutions now leverage AI systems to conduct real-time risk assessment, detect and prevent fraud, optimize investment portfolios, and provide enhanced decision support to professionals. These AI-augmented approaches enable faster analysis of transaction data, more accurate identification of patterns and anomalies, improved forecasting capabilities, and data-driven investment strategies. While delivering significant advantages in operational efficiency, credit risk management, fraud prevention, and investment performance, the industry still faces challenges related to regulatory compliance, model transparency, and data privacy. Looking forward, explainable AI, federated learning, natural language processing, and quantum computing represent promising directions for further advancing financial decision-making capabilities

Zenodo (CERN European Organization for Nuclear Research)
Institute of Information Technologies (BG)
Peace, Justice and strong institutions
Openalex Percentile: Top 36%
Economic, financial, and policy analysis
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AI-augmented decision-making in financial services · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS