Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes

Madiha Madiha; Kira Sam; Sanjai Kumar; Raja Vavekanand

doi:10.62762/TCS.2025.744196

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ICCK Transactions on Computer Science, Volume 2, Issue 2, 2025: 35-50

Free to Read | Review Article | 25 May 2025

Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes

Madiha Madiha 1

Kira Sam 2

Sanjai Kumar 3

Raja Vavekanand 4 *

1 Metropolia University of Applied Sciences, Myllypurontie 1, Helsinki, Finland

2 University Of Salento, Piazza Tancredi, 73100 Lecce LE, Italy

3 University of Sindh, Jamshoro 76080, Sindh, Pakistan

4 Datalink Research and Technology Lab, Islamkot 69240, Sindh, Pakistan

* Corresponding Author: Raja Vavekanand, [email protected]

DOI: 10.62762/TCS.2025.744196

ARK: ark:/57805/tcs.2025.744196

Received: 06 February 2025, Accepted: 13 April 2025, Published: 25 May 2025

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Abstract

Artificial intelligence (AI) is transforming the financial sector by revolutionizing risk management and enhancing personalized banking experiences. This study examines the role of AI in finance by analyzing how companies such as AlphaSense and Kasisto utilize AI-driven solutions to optimize financial decision-making and customer interactions. AI’s advanced capabilities in data analysis, credit underwriting, and automated customer service have significantly enhanced efficiency, accuracy, and accessibility within the financial services sector. The paper explores key AI technologies, including machine learning, natural language processing (NLP), and generative AI, which drive this transformation. Despite its numerous advantages, the integration of AI in finance presents challenges such as data privacy concerns, ethical considerations, and regulatory compliance. The study highlights the importance of addressing these challenges to ensure the responsible and sustainable adoption of AI in the financial sector. By showcasing the contributions of leading AI-driven financial solutions, this research provides valuable insights into the evolving financial landscape and the potential of AI to redefine financial services, making them more inclusive, efficient, and secure.

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Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes

Keywords

AI in finance

digitalization

risk management

financial data analysis

risk analytics

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

Raja Vavekanand is an employee of Datalink Research and Technology Lab, Islamkot 69240, Sindh, Pakistan.

Ethical Approval and Consent to Participate

Not applicable.

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Madiha, M., Sam, K., Kumar, S., & Vavekanand, R. (2025). Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes. ICCK Transactions on Computer Science, 2(2), 35–50. https://doi.org/10.62762/TCS.2025.744196

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TY  - JOUR
AU  - Madiha, Madiha
AU  - Sam, Kira
AU  - Kumar, Sanjai
AU  - Vavekanand, Raja
PY  - 2025
DA  - 2025/05/25
TI  - Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes
JO  - ICCK Transactions on Computer Science
T2  - ICCK Transactions on Computer Science
JF  - ICCK Transactions on Computer Science
VL  - 2
IS  - 2
SP  - 35
EP  - 50
DO  - 10.62762/TCS.2025.744196
UR  - https://www.icck.org/article/abs/TCS.2025.744196
KW  - AI in finance
KW  - digitalization
KW  - risk management
KW  - financial data analysis
KW  - risk analytics
AB  - Artificial intelligence (AI) is transforming the financial sector by revolutionizing risk management and enhancing personalized banking experiences. This study examines the role of AI in finance by analyzing how companies such as AlphaSense and Kasisto utilize AI-driven solutions to optimize financial decision-making and customer interactions. AI’s advanced capabilities in data analysis, credit underwriting, and automated customer service have significantly enhanced efficiency, accuracy, and accessibility within the financial services sector. The paper explores key AI technologies, including machine learning, natural language processing (NLP), and generative AI, which drive this transformation. Despite its numerous advantages, the integration of AI in finance presents challenges such as data privacy concerns, ethical considerations, and regulatory compliance. The study highlights the importance of addressing these challenges to ensure the responsible and sustainable adoption of AI in the financial sector. By showcasing the contributions of leading AI-driven financial solutions, this research provides valuable insights into the evolving financial landscape and the potential of AI to redefine financial services, making them more inclusive, efficient, and secure.
SN  - request pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Madiha2025Case,
  author = {Madiha Madiha and Kira Sam and Sanjai Kumar and Raja Vavekanand},
  title = {Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes},
  journal = {ICCK Transactions on Computer Science},
  year = {2025},
  volume = {2},
  number = {2},
  pages = {35-50},
  doi = {10.62762/TCS.2025.744196},
  url = {https://www.icck.org/article/abs/TCS.2025.744196},
  abstract = {Artificial intelligence (AI) is transforming the financial sector by revolutionizing risk management and enhancing personalized banking experiences. This study examines the role of AI in finance by analyzing how companies such as AlphaSense and Kasisto utilize AI-driven solutions to optimize financial decision-making and customer interactions. AI’s advanced capabilities in data analysis, credit underwriting, and automated customer service have significantly enhanced efficiency, accuracy, and accessibility within the financial services sector. The paper explores key AI technologies, including machine learning, natural language processing (NLP), and generative AI, which drive this transformation. Despite its numerous advantages, the integration of AI in finance presents challenges such as data privacy concerns, ethical considerations, and regulatory compliance. The study highlights the importance of addressing these challenges to ensure the responsible and sustainable adoption of AI in the financial sector. By showcasing the contributions of leading AI-driven financial solutions, this research provides valuable insights into the evolving financial landscape and the potential of AI to redefine financial services, making them more inclusive, efficient, and secure.},
  keywords = {AI in finance, digitalization, risk management, financial data analysis, risk analytics},
  issn = {request pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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