Ethics in financial planning: Analysis of ombudsman decisions using codes of ethics and fiduciary duty standards
Bibliographic record
Abstract
Scandals show that ethics is an important topic in financial planning. Our research analyses 212 financial ombudsman decisions (2013–2018) to understand the nature of financial planning misconduct in complaint decisions. We develop a coding structure to ascertain what professional conduct involves and then use content analysis and cluster analysis to identify the aspects of professional conduct occurring in these misconduct decisions. Diligence, acting in the client’s best interest and having no reasonable basis for advice are interconnected elements in over half of these decisions. Secondary elements are misleading statements, conflicts of interest and disclosure. Analysis of decisions involving fiduciary duty showed that financial planners failed to ascertain a client’s circumstances and did not form advice based on their client’s information. As financial planning professionalises, future research, financial planning education, policy and practice should address these issues. JEL Classification: D14, G20, G50.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".