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Ring saves 67% on engineering resources and accelerates time-to-market by 1.5 months, with Sentinel

The challenge
  • The business rules multiplied over time, buried under layers of code from various developers, making change management lengthy and frustrating

  • 70% of Ring’s most valuable resources (data science & engineering) were tied up in operational tasks, limiting innovation.

  • Limited visibility into digital lending operations, making it challenging to debug flaws in credit policies.

Key results

90%

Reduction in turnaround time for policy changes

67%

Savings on engineering resources

1.5 months

were saved in getting LAP offering to market, well ahead of schedule.

How Sentinel addressed these challenges
  • Sentinel visual workflow and advanced dashboards made Ring’s entire decisioning architecture visualisable, synthesisable, and debuggable

    • Credit teams could now access Sentinel (via Role-Based Access Control and Single Sign-On) to perform the following tasks without IT involvement:

      • Create policies/workflows

      • Test policies at the rule-level, workflow-level, or bulk-level using historical data

      • Deploy policies in either of three modes: Full rollout, Champion/Challenger or Canary mode)

      • Monitor what’s going on in real time and identify logical flaws in decisioning

      • Analyse policies at the granular level

    • Credit teams can now design and implement customised borrower experiences and loan offers tailored to individual risk profiles, using a user-friendly drag-and-drop interface.

    • With access to funnel-level analytics, credit teams could optimise workflows to reject customers early in the onboarding process, reducing downstream costs and enhancing borrower experience.

  • Sentinel visual workflow and advanced dashboards made Ring’s entire decisioning architecture visualisable, synthesisable, and debuggable

    • Credit teams could now access Sentinel (via Role-Based Access Control and Single Sign-On) to perform the following tasks without IT involvement:

      • Create policies/workflows

      • Test policies at the rule-level, workflow-level, or bulk-level using historical data

      • Deploy policies in either of three modes: Full rollout, Champion/Challenger or Canary mode)

      • Monitor what’s going on in real time and identify logical flaws in decisioning

      • Analyse policies at the granular level

    • Credit teams can now design and implement customised borrower experiences and loan offers tailored to individual risk profiles, using a user-friendly drag-and-drop interface.

    • With access to funnel-level analytics, credit teams could optimise workflows to reject customers early in the onboarding process, reducing downstream costs and enhancing borrower experience.