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Generative AI in Traditional Banking Institutes

Ek, Lucas LU and Arnarp, Elliot (2024) INFM10 20241
Department of Informatics
Abstract
This master’s thesis investigates the utilisation of generative AI within traditional banking institutions, emphasising value creation, challenges, and strategic facilitators. Previous research underscores the importance of these areas in the context of generative AI's novel application in traditional banks. Findings reveal that traditional banks primarily employ generative AI for internal operations, enhancing efficiency and productivity by automating repetitive tasks and assisting with activities such as code generation and report feedback. However, the adoption of generative AI for customer-facing applications remains limited due to privacy and ethical concerns. Challenges identified include technological integration, ethical and... (More)
This master’s thesis investigates the utilisation of generative AI within traditional banking institutions, emphasising value creation, challenges, and strategic facilitators. Previous research underscores the importance of these areas in the context of generative AI's novel application in traditional banks. Findings reveal that traditional banks primarily employ generative AI for internal operations, enhancing efficiency and productivity by automating repetitive tasks and assisting with activities such as code generation and report feedback. However, the adoption of generative AI for customer-facing applications remains limited due to privacy and ethical concerns. Challenges identified include technological integration, ethical and privacy issues, organisational culture, and stringent regulatory compliance. To address these, banks have formed strategic alliances with tech giants, invested in AI literacy and training, nurtured an innovative culture with management support, and aligned AI initiatives with business objectives. These facilitators help banks navigate the complexities of integrating generative AI into their operations. In conclusion, while traditional banks are in the early stages of adopting generative AI, they are making significant strides in internal utilisation, although external applications and scaling remain challenging. Future research should explore the long-term impacts and readiness of traditional banks to adopt generative AI on a larger scale. (Less)
Please use this url to cite or link to this publication:
author
Ek, Lucas LU and Arnarp, Elliot
supervisor
organization
course
INFM10 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
Generative AI, Value, Utilisation, Challenge
language
English
id
9165280
date added to LUP
2024-06-18 13:56:32
date last changed
2024-06-18 13:56:32
@misc{9165280,
  abstract     = {{This master’s thesis investigates the utilisation of generative AI within traditional banking institutions, emphasising value creation, challenges, and strategic facilitators. Previous research underscores the importance of these areas in the context of generative AI's novel application in traditional banks. Findings reveal that traditional banks primarily employ generative AI for internal operations, enhancing efficiency and productivity by automating repetitive tasks and assisting with activities such as code generation and report feedback. However, the adoption of generative AI for customer-facing applications remains limited due to privacy and ethical concerns. Challenges identified include technological integration, ethical and privacy issues, organisational culture, and stringent regulatory compliance. To address these, banks have formed strategic alliances with tech giants, invested in AI literacy and training, nurtured an innovative culture with management support, and aligned AI initiatives with business objectives. These facilitators help banks navigate the complexities of integrating generative AI into their operations. In conclusion, while traditional banks are in the early stages of adopting generative AI, they are making significant strides in internal utilisation, although external applications and scaling remain challenging. Future research should explore the long-term impacts and readiness of traditional banks to adopt generative AI on a larger scale.}},
  author       = {{Ek, Lucas and Arnarp, Elliot}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Generative AI in Traditional Banking Institutes}},
  year         = {{2024}},
}