Introduction

This project examined the determinants influencing investment scam losses in Australia.

Seven Determinants Examined:

  1. Number of reports the Scam Watch Centre received (numeric)
  2. Cash rate in the economy (numeric)
  3. Unemployment rate in the economy (numeric)
  4. Victims’ gender (factor = 2)
  5. Age group (factor = 7)
  6. Jurisdiction / State (factor = 9)
  7. Contact methods used by scammers (factor = 4)

Dashboard Analytics

Chart Index

Scamloss(DV) Australia map

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Scamloss(DV) mean monthly

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Scamloss(DV) value by month

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QqplotDeviance Residuals

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ResidualsDeviance vs Fitted

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Scamloss(DV) outliers by month

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GLM results table

GLM gamma coefficients logscale

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GLM gamma coefficients exponentialscale

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Scamloss(DV) NoReports(IV)

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Scamloss(DV) CashRate(IV)

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Scamloss(DV) Unemplytrate(IV)

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AverageScamloss(DV) Gender(IV)

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AverageScamloss(DV) Agegroup(IV)

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AverageScamloss(DV) State(IV)

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AverageScamloss(DV) Contactmethod(IV)

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Posthoc Tukey Contactmethod(IV)

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Posthoc Tukey Agegroup(IV)

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Posthoc Tukey State(IV)

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Estimated Scamloss(DV) Cashrate(IV) State(M)

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Estimated Scamloss(DV) Cashrate(IV) Age(M)

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Estimated Scamloss(DV) Cashrate(IV) Contactmethod(M)

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Estimated Scamloss(DV) Cashrate(IV) Gender(M)

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Conclusion

Statistical Association with Scam Losses:

  • Number of reports: Positive association
  • Cash rate in the economy: Negative association
  • Age group (factor = 7): Positive association
  • Jurisdiction / State (factor = 9): Positive association
  • Unemployment rate: Nil association
  • Victims’ gender (factor = 2): Nil association
  • Contact methods used by scammers (factor = 4): Nil association

Recommendations:

  • Prioritise investment scam strategies for over 65 year old group
  • Have intervention strateigy to reduce investment scam losses of males
  • Enforce regulations on mobile apps to intercept investment scams