Case Study

Optimizing productivity in financial services

What they needed

When a major Australian bank set out to digitize the end-to-end operations of its home-lending division, it faced some tough decisions. Processing a single home loan involves complex, variable activities that shift and change depending on factors like resource availability, competency and service-level agreements (SLAs) and the bank’s operations were managed through multiple digital systems spanning customer engagement, employee management, and application and task decisioning.

Despite these challenges, the bank is required to sustain high workforce productivity, minimize processing time, adhere to SLA’s and, at worst, maintain its home-loan decision quality.

To support its ambition, the bank partnered with KPMG and ELARA to understand:

  • Which changes to its strategic operating model should it prioritize?

  • How should it use its available resources to improve its target outcomes?

What We Did

Through the application of advanced mathematics, ELARA AI defined a high conviction use case to optimize the bank’s third-party mortgage origination space. The process of manual work allocation was selected as a prime opportunity to improve performance. Activities were coordinated between the technology business unit, data and analytics, and the third-party origination business unit to achieve alignment on key objectives, decisions, and delivery of the capability into the business. A minimum-viable-product insights engine was configured, which included:

  • An integration with various business systems, ranging through spreadsheets, data bases and event queues.

  • Use of simulation, scenario, and full potential analysis to calculate prioritization of strategic operating model changes.

  • Use of advanced mathematics to calculate work allocation to employees.

  • An interactive user interface to allow live interaction with the system.

ELARA AI prioritized changes to the bank’s strategic operating model, based on the effect on productivity, time to outcome and SLAs. They included:

  • Local/remote team structure/size/hours and scheduling

  • Third-party grading

  • Alternative work allocation strategies

  • Targeted processing improvements

The Outcome

The solution was able to identify:

  • Total average productivity increase (outcomes/FTE) of more than 30 percent

  • Total average reduction of time to outcome by more than 18 percent