Marketing bank services to financially vulnerable customers: evidence from an emerging economy
Bibliographic record
Abstract
Purpose This study aims to explore how banks in Nigeria are marketing financial services to financially vulnerable customers. Design/methodology/approach A multiple case study research strategy was used to analyse three commercial banks and two microfinance banks. Data were collected using semi-structured interviews with the banks' directors as well as from banks' published annual reports and archival images. Findings The study reveals that Nigerian banks develop different product development portfolios, adopt innovative traditional marketing schemes and apply inclusive technologies to reach and extend services to the unbanked and financially vulnerable customers in the society. Research limitations/implications Banks should focus on consumer engagement through the proactive development of technologies and employ innovative marketing methods. Customers' banking experiences can be enhanced if banks communicate with and educate customers about technological modes of engagement. In addition, financial service transaction support and financial literacy education can assist banks in marketing their services to financially vulnerable customers, in mutually beneficial ways. Originality/value This study shows how financial service operators' market and extend their services to financially vulnerable customers in emerging markets. It empirically establishes the importance of financial services to financially excluded customers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".