Managers' understanding of artificial intelligence in relation to marketing financial services: insights from a cross-country study
Bibliographic record
Abstract
Purpose Given that managers play a crucial role in developing and deploying AI for marketing financial services, this study was aimed at better understanding their awareness regarding AI and the challenges they are facing in providing the attendant technologies, as well as highlighting key stakeholders and their collaborative efforts in providing financial services. Design/methodology/approach Exploratory, inductive research design. The data was gathered through semi-structured interviews with 47 bank managers in both developed and developing countries, including the United Kingdom, Canada, Nigeria and Vietnam. Findings Managers are aware of the prospects of AI and are making efforts to address AI as a business need but find that there often exist certain challenges in accelerating AI adoption. The study also presents a conceptual framework of AI in relation to financial service marketing, which captures and highlights the interactions among the customers, banks and external stakeholders, as well as the regulators. Research limitations/implications Banks must understand their business objectives, the available resources and the needs of their customers. Managers should keep the ethical implications of their working relationships in mind when selecting a team or collaborating with partners. In addition, managers should be trained and assisted in comprehending AI in relation to financial services, while the regulators must be involved in the development of AI for financial service marketing. Finally, it is critical to communicate the prospects for AI to consumers. Originality/value This study provides empirical insight into the opportunities, prospects and challenges pertaining to the use of AI in the area of financial service marketing. It also specifically calls into question certain preconceptions regarding AI and its role in financial services, the chatbots adopted for financial service delivery and the role of marketing managers in developing AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".