Machine Intelligence and Managerial Decision-Making

Schlenker, Lee; Minhaj, Mohamed

doi:10.1201/9781003019855-3

Cited by 3 publications

(4 citation statements)

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“…The adoption of business intelligence technologies by decision support systems made them more data-driven and provided advanced visualizations for analytical purposes (Eom, 2020). This adoption OLAP "cubes" (Schlenker & Minhaj, 2020). During this period, business intelligence increasingly adopted machine and deep learning technologies and gained wide acceptance in detecting fraudulent credit card transactions (Adewumi & Akinyelu, 2017;Dhankhad et al, 2018;Fadaei Noghani & Moattar, 2017), which is also confirmed by the evolutionary connection in Figure 9.…”

Section: A Data-driven 2010-2016 Period and The Rise Of Big Datamentioning

confidence: 84%

“…It can capture nonlinear relationships, based on neural networks with multiple layers; the more layers, the more intelligent they are (Borges et al, 2021). In machine and deep learning, a data-driven conversation between input and output data is created through a learning algorithm, such as supervised, unsupervised, and reinforcement learning (Schlenker & Minhaj, 2020). The learning algorithm then generates the computer software after optimizing parameters until the input data predicts the output data well (Benaich & Hogarth, 2020).…”

Section: Framework Implementation In Practicementioning

confidence: 99%

“…This adoption moved decision support systems into centrality in the 2010–2016 period, as shown in Figure 11. Business intelligence typically includes data warehousing and online analytical processing, referred to as OLAP “cubes” (Schlenker & Minhaj, 2020). During this period, business intelligence increasingly adopted machine and deep learning technologies and gained wide acceptance in detecting fraudulent credit card transactions (Adewumi & Akinyelu, 2017; Dhankhad et al, 2018; Fadaei Noghani & Moattar, 2017), which is also confirmed by the evolutionary connection in Figure 9.…”

Section: Four Defining Periods For Research and Practice In Bfsi Arti...mentioning

confidence: 99%

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Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Herrmann

Masawi

2022

Strategic Change

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The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, “algorithmic” AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for “responsible AI” (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance.

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Section: A Data-driven 2010-2016 Period and The Rise Of Big Datamentioning

confidence: 84%

Section: Framework Implementation In Practicementioning

confidence: 99%

Section: Four Defining Periods For Research and Practice In Bfsi Arti...mentioning

confidence: 99%

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Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Herrmann

Masawi

2022

Strategic Change

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“…As mentioned earlier, conceptualizations in business and management are often transdisciplinary and inconsistent. Evolutionary mapping is therefore particularly useful for capturing the semantic drift of concepts across various periods (Schlenker & Minhaj, 2020).…”

Section: Science Mapping Reviewsmentioning

confidence: 99%

Introducing the systematic science mapping framework: an innovative and mixed approach for macro scale reviews

Herrmann

2023

Handbook of Mixed Methods Research in Business and Management

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Bidding for B2B or B2G tenders: toward the adoption of pricing models in practice

Herrmann,

Cobo Martín

2024

Manag Rev Q

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This study investigates the lack of adoption of pricing models for tenders in business-to-business (B2B) and business-to-government (B2G) markets. We aim to identify the gaps between research and practice and propose a future research agenda to bridge these gaps. Our study contributes in three ways: First, we outline how our research agenda can influence the adoption of pricing models across specific practitioner roles in tendering. Second, we introduce systematic science mapping (SSM) as a novel methodology for literature reviews. SSM combines a systematic review and science mapping in a multi-stage, mixed-methods research design. We chart the evolution of 1042 research publications from 1956 to 2022 into three thematic areas. Our review of 163 gray literature publications reveals seven schools of thought on tender price modeling and the causes of theory-to-practice gaps. Finally, we introduce a new metric, the mapping factor (MAPF), as a robustness indicator for systematic literature reviews.

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Machine Intelligence and Managerial Decision-Making

Cited by 3 publications

References 0 publications

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Introducing the systematic science mapping framework: an innovative and mixed approach for macro scale reviews

Bidding for B2B or B2G tenders: toward the adoption of pricing models in practice

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