Background

OCBC leveraged Savos to improve the identification of high-potential candidates across large hiring programs, enabling more consistent talent evaluation and helping recruiters make faster, better-informed shortlisting decisions.

The Problem

OCBC receives large volumes of applications across graduate, early-career, and professional hiring programs. While academic qualifications and work experience provide useful signals, they often fail to reveal motivation, adaptability, learning agility, and long-term potential.

The challenge was to create a scalable and consistent approach to evaluating candidates while ensuring recruiters could identify high-potential talent without significantly increasing review effort.

Outcomes of Project & Success Metrics

Observed operational outcomes included:

  • Up to 60% reduction in application review effort
  • Up to 70% faster applicant processing times
  • 30% improvement in identification of high-potential candidates
  • 25% increase in recruiter productivity
  • 2.5x faster shortlisting workflows

KPIs tracked include:

  • application processing time
  • recruiter workload reduction
  • candidate engagement rates
  • shortlist quality metrics
  • hiring manager satisfaction

Lessons Learned

Key lessons learned included:

  • academic credentials alone are insufficient predictors of long-term success
  • AI-assisted investigations uncover valuable contextual information
  • standardized candidate intelligence improves consistency across hiring teams
  • scalable evaluation frameworks improve recruiter effectiveness