Inspiring
Case Studies

One of the most immediate challenges was the departure of several senior staff who previously led or had in-depth experience with core banking integrations. This created a capability gap in both the strategic planning and technical execution of the merger. Without this institutional knowledge, Beyond Bank faced an increased risk of missteps in systems alignment, data mapping, and change management.

APRA and ASIC maintained heightened oversight during the transition, particularly under CPS 230 and related regulatory frameworks. The challenge was not just meeting compliance—but doing so with transparency, automation, and agility.

• Address data access issues and ensure continuity of operations and compliance.
• Deploy a secure, scalable platform to support data access and advanced analytics.
• Provide an architecture that enables ongoing AI enablement, governance, and resilience.

Neo Analytics responded with a structured, multi-phased approach that combined deep technical capability with strategic oversight. The first priority was configuring a secure and Databricks tenant tailored to Metro Finance’s governance and performance needs. In conjunction with the customer, Neo orchestrated a series of controlled workloads designed to systematically refactor data storage and the key datasets providing easy access to Metro staff.

Throughout the process, data accuracy and integrity were rigorously validated against available benchmarks and reference points.

Migrating from a bespoke core banking system to a general-purpose core banking platform is no small task, especially in a regulated environment like banking.

Data structures, relationships, and naming conventions in bespoke platforms rarely align cleanly with those in off-the-shelf solutions.

Moving to a new core demands rebuilding or reconfiguring integrations across dozens of touchpoints.

Extracting workflows and compliance procedures embedded in the bespoke system. Moving from “we build and control everything” to “we configure and adapt” required a cultural and mindset shift within IT and the business.

1. Understanding the Starting Point -The starting point for any data strategy must incorporate a clear understanding of the current data landscape and how existing data goals and objectives align with business strategy requirements.
2. Establishing data trust across the bank’s operations –  addressing redundancies and inefficiencies in data infrastructure and data management data governance frameworks.
3. Establishing integrated data management – introducing effective data governance principles, improving data literacy, optimising data collection and storage and evolving the bank into a data-driven decision-making organisation.

1. Dynamic regulatory requirements – New regulatory standards imposed new impairment monitoring responsibilities focused on predicting expected credit losses.
2. Shifting to a pro-active, predictive approach – The complexity of the new standard necessitated a new approach that recognised impairment losses before observable evidence existed, Queensland Country needed to be proactive and aware before the fact, rather than reactive after the fact.
3. Difficulty to execute – Lack of credible and quality data, talent, and understanding on how to implement the change.