🇦🇺Australia

Erroneous Admission Decisions from Manual Reviews

2 verified sources

Definition

Portfolio-driven selections with minimum 12 works and interviews prone to bias, affecting yield and diversity compliance.

Key Findings

  • Financial Impact: 2-5% revenue loss from bad cohort fits or appeals (AUD 50k+ for mid-size school)
  • Frequency: Per intake
  • Root Cause: Lack of visibility in manual interview/portfolio data

Why This Matters

The Pitch: Fine Arts Schools risk AUD 20,000+ in refunds/disputes yearly from flawed decisions. Automated scoring ensures defensible, data-driven admissions.

Affected Stakeholders

Admissions Panel, Academic Directors

Deep Analysis (Premium)

Financial Impact

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Current Workarounds

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Methodology & Sources

Data collected via OSINT from regulatory filings, industry audits, and verified case studies.

Evidence Sources:

Related Business Risks

Lost Enrolments from Slow Audition Delays

AUD 10,000-20,000 per lost student (avg tuition ~AUD 15,000/year)

Idle Studio Capacity Post-Admission Delays

AUD 5,000-10,000/month in idle tutor and facility costs

Manuelle Datenerfassung bei Alumni-Karriereverfolgung

Quantified (Logic): Bei 0,2–0,4 FTE Verwaltung/Alumni-Office für manuelle Datenerhebung und -pflege, mit angenommenen 80.000–100.000 AUD Vollzeitkosten, entstehen ca. 16.000–40.000 AUD jährliche Opportunitäts- und Personalkosten, die durch automatisierte Alumni-Tracking-Tools stark reduziert werden könnten.

Nicht abgerechnete Lizenzgebühren für Eigenproduktionen

Quantified (logic-based): A typical fine arts‑focused school that generates original works and recordings but lacks systematic outbound licensing is likely to forgo around AUD 10,000–30,000 per year in potential licence and royalty income (e.g. 10–30 missed external uses annually at AUD 300–1,000 per use).

Late Program Fee

AUD 50 late fee per delayed submission

Recital Organisation Cost Overruns

20-40 hours per recital in organisational time; potential full event cost loss if flop

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