Empowering Financial Education by Banks—Social Media as a Modern Channel
Bibliographic record
Abstract
Financial literacy is extremely important, both from the perspective of the financial well-being of individuals and the stability of the financial market and the whole economy. The more financially literate a bank’s customers are, the more frequently and consciously they use financial products and services. Thus, banks are potentially significant stakeholders in the financial education process. Considering that social media have become the leading channel for communication and relationship building, especially regarding young clients, this channel should also be used by banks to increase financial literacy. The aim of this paper is to assess banks’ involvement in financial education activities through social media. We assume that banks use social media as a modern and attractive channel for improving financial education among social media users. The empirical analysis was conducted using several data sources, including non-financial statements and a unique self-collected dataset that describes the specifics of the most popular social media platforms (like Facebook, Twitter, YouTube, Instagram, GoldenLine, and LinkedIn) in the activities of commercial and cooperative banks in Poland between 2010 and 2019. Descriptive statistical methods and cluster analysis were used. The results show that educational activities provided by banks in Poland differ for each social media channel. Additionally, although financial education topics have become more popular among content published by banks, there is a huge disproportion between cooperative and commercial banks. Generally, banks that are more active on social media (mostly commercial banks) also pay more attention to the financial education context.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".