The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers
Bibliographic record
Abstract
Artificial intelligence (AI) is rapidly transforming digital marketing practices. While the extant literature extensively covers AI applications that generally benefit businesses and customers, there is scant research on AI deployments that exacerbate problems for financially vulnerable customers. These customers have limited access to financial systems, services or technologies. To rectify this research deficit, this paper describes the challenges confronting businesses as they attempt to integrate AI into the digital marketing of their financial services. Ultimately, Al-enabled digital marketing is not as simple as collecting big data and using analytical algorithms; the technology may not always help businesses target their customers more effectively. This paper examines the relationships between AI, digital marketing, and financial services in relation to vulnerable customers, highlighting key implications in the collection, processing, and delivery of information, as well as the importance of human connection for optimal customer experience and engagement with financial services providers. Understanding ethical implications, as well as data and modelling challenges, is necessary for the successful deployment of AI. This study provides a theoretical framework to financial services providers, AI developers, marketers, policymakers, and academics, aiding the understanding of the precarious conditions facing vulnerable customers, and the ways in which they can more effectively be reached.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".