Victimisation of investors from fraudulent investment schemes and their protection through financial education
Bibliographic record
Abstract
Purpose The purpose of this study is to identify the significant demographic and socio-economic characteristics of individuals who are likely to invest in a fraudulent investment scheme. It also quantifies the extent to which financial literacy helps in reducing the odds of investments in such schemes. Based on these findings, it provides policy recommendations to regulators and governments. Design/methodology/approach This study uses nationally representative data from the “India Assessment of Financial Capability 2018” survey. It further uses logistic regression with a binary outcome variable to assess the individual-level odds of investments in fraudulent investment schemes. Findings This study concludes that males between 40 and 59 years of age, who are well-educated (are at least graduates), score low in financial literacy, belong to the middle-income group, and SEC A3 households are most vulnerable to victimization by financial fraudulent investment schemes. It finds that financial literacy significantly reduces the odds of investment into fraudulent schemes to the extent of 39.118%. Originality/value This study quantifies the extent to which financial literacy helps in reducing the odds of individual investments in a fraudulent investment scheme. As financial literacy has a significant and negative relationship with the likelihood of investment in such schemes, this study provides policy interventions and recommendations to regulators and governments to safeguard the interest of individual investors.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".