Artificial Intelligence in Banking sector: Evidence from Bahrain

Abdulla, Yomna; Ebrahim, Rabab; Kumaraswamy, Sumathi

doi:10.1109/icdabi51230.2020.9325600

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…See the Table 1 for an overview of the interviewees. [1,7,[15][16][17][18] and systematize them along the meta themes introduced by Syed et al [1].…”

Section: Expert Study On Intelligent Rpa In Current and Future Busine...mentioning

confidence: 99%

“…This includes procedures for exception handling. Likewise, they need to define metrics to evaluate their implementations [18]. Lastly, while the initial use of robots works well on a small scale with limited resources, scaling them is highly dependent on the elasticity of resources [7].…”

Section: Expert Study On Intelligent Rpa In Current and Future Busine...mentioning

confidence: 99%

“…The development of intelligent robots faces several challenges due to a lack of data availability. Many highlight customer privacy concerns or data regulation limitations when sharing their data [7,18]. However, as AI models are data-driven, they need to learn from data [5].…”

Section: Technical Implementation Challenges During Build-timementioning

confidence: 99%

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From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

Herm

Janiesch

Reijers

et al. 2021

Lecture Notes in Computer Science

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Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA.

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“…See the Table 1 for an overview of the interviewees. [1,7,[15][16][17][18] and systematize them along the meta themes introduced by Syed et al [1].…”

Section: Expert Study On Intelligent Rpa In Current and Future Busine...mentioning

confidence: 99%

Section: Expert Study On Intelligent Rpa In Current and Future Busine...mentioning

confidence: 99%