Move Bank
Convergence of data, lakehouse architecture and AI matters for modern enterprises
Over the last couple of years we integrated our solutions within some of Australia’s most respected financial institutions.
Here are some of their stories:
Convergence of data, lakehouse architecture and AI matters for modern enterprises
Supporting the transition from legacy banking systems to a modern core platform
Convergence of data, lakehouse architecture and AI matters for modern enterprises
Move Bank engaged Neo Analytics to help establish a modern Databricks-based data environment that would support the extraction, storage and ongoing use of legacy banking data as the bank progressed its core system transition. The initiative was designed to create a scalable cloud foundation for bringing historical data out of the UniVerse back end and into a more accessible, governed and analytics-ready platform. Early discovery work focused on standing up the Databricks environment, confirming network and security prerequisites, and defining the migration approach, scope and roadmap for how legacy and archive data would be landed and managed over time.
A key part of the program was to preserve access to historic data that would not necessarily move into the new core banking platform. Neo’s approach built on migration patterns already used in the Constantinople program, including extracting raw MultiValue UniVerse files, landing them in cloud storage, and transforming them through bronze, silver and gold layers in Databricks. This provided Move Bank with a practical pathway to retain, query and extend historical data over time, while also creating a stronger foundation for reporting and future data use.
Move Bank’s transition to Databricks involved more than simply standing up a new cloud platform. One of the key challenges was defining the true scope of the legacy data extraction from the UniVerse back end. At the outset, it was not yet clear which archive files, historical datasets and excluded records would need to be brought across, and the team recognised that Move Bank’s requirements were likely to evolve over time as additional historical data needs emerged.
A further challenge was the complexity of the source data itself. UniVerse stores information in a raw MultiValue format, including multi-valued and multi-subvalued fields, which meant the data could not simply be copied into Databricks and queried as-is. Neo needed to extract raw text files, decrypt and curate them, move them into Databricks volumes, and then apply layout-driven transformation logic to convert them into structured bronze and silver tables. In some cases, a single source row could expand into multiple rows in the target structure, adding further complexity to the transformation process.
There was also an important design challenge around how historical archive data should relate to the data already migrated for core banking cutover. While many archive files were expected to share the same structure as active source files, the team still needed to determine how those datasets should be logically integrated, whether overlaps existed, and how to preserve a clear distinction between “as migrated” data and broader historical records. This was critical to maintaining traceability after go-live and avoiding confusion over what had been moved into the new core banking platform versus what had been retained for historical access.
Finally, the program had foundational platform and operating-model challenges to address. Databricks access was dependent on network and VPN configuration, security and identity settings needed review, and the team needed clarity around data ownership, stewardship and access procedures within the business. Move Bank also needed to balance cost against reliability when shaping the Azure and Databricks environment, making the early discovery phase as much about governance and operational readiness as technology delivery.
Move Bank’s Databricks initiative established a practical and scalable foundation for preserving, accessing and extending legacy banking data beyond the core system transition. By drawing on migration patterns already proven in the Constantinople program, Neo was able to define a repeatable workflow for extracting UniVerse data, decrypting and curating raw files, and transforming them into structured bronze and silver datasets that could be used more effectively in a modern cloud environment.
A key result was that much of the migration capability was already reusable for Move Bank’s archive and historical data. Because many archive files were expected to share the same structure as active source files, Neo could leverage existing Python dictionaries, transformation logic and layout-driven processing techniques, reducing delivery risk and accelerating the path to implementation.
The work also created a clearer pathway for preserving both historical data and the “as migrated” state of the data used in core banking cutover. Rather than losing visibility once the migration environment was retired, the approach allowed Move Bank to reproduce and retain migrated datasets in its own Databricks environment, supporting better traceability, post-go-live analysis and ongoing historical access.
Just as importantly, the discovery phase helped define a stronger target-state operating model for Databricks, including clearer consideration of connectivity, security, catalogue structure, data ownership and service-level expectations. This positioned Move Bank not only to retain historic data, but to do so within a more governed, scalable and business-ready cloud platform.
Supporting the transition from legacy banking systems to a modern core platform
Constantinople engaged Neo Analytics to support a complex core banking migration program involving the movement of legacy banking data into a new core banking platform. Neo’s role covered key migration activities across the full delivery lifecycle, including pre- and post-conversion support, mock conversion, go-live preparation, post-conversion clean-up, and the establishment of a lakehouse repository for operational reporting.
First, the legacy source data was not available in simple relational form. The extraction process had to read UniVerse files in their raw format, including multi-valued and multi-subvalued structures, then write them out as text for further processing. That meant Neo had to handle non-standard record layouts, embedded delimiters and variable field structures before the data could be made usable for migration and reporting.
Second, the migration scope extended beyond the active data already earmarked for cutover. Workshop discussions identified a need to consider archive files and older client and account data that would not automatically move into Constantinople, but would still be needed for historic access and future query requirements. This created additional design questions around scope, prioritisation and whether data should be extracted in tranches rather than in one pass.
Third, once raw files were landed, the data required careful transformation. Neo’s team needed to decrypt inbound files, move them into Databricks volumes, and convert them into bronze tables before applying layout-driven transformations into richer silver structures. In many cases, a single source row could expand into multiple logical rows because of subtable and subdelimiter structures, making the transformation logic materially more complex than a standard flat-file ingestion.
Finally, the team identified an important governance risk: once migration completed, there was a possibility the original Databricks migration environment would be decommissioned. Neo recognised the need to preserve an “as migrated” historical record so there would be a defensible reference point for post-go-live questions, issue resolution and auditability.
The transformation of MultiValue UniVerse data into a modern data format involves converting complex, nested and non-relational legacy structures into clean, standardised and analytics-ready datasets that can be used across contemporary cloud platforms, reporting tools and AI workflows.
Neo established a practical and reusable migration pattern for extracting legacy core banking data and converting it into a governed Databricks lakehouse structure. The process supported the movement of raw source data into structured bronze, silver and gold layers, creating a stronger platform for migration assurance, operational reporting and future historical access.
The team also confirmed that much of the migration capability was reusable for archive and historical data. Where archive files matched the structure of active source files, Neo had already developed the Python dictionaries, layout logic and transformation techniques needed to bring those datasets into the same migration framework. That reduced delivery risk and improved the bank’s ability to extend the migration scope as additional historic data needs emerged.
Just as importantly, Neo helped shape a more robust target-state approach by identifying the need to preserve migrated data separately from broader historical datasets. This ensured the program could retain a clear record of what had been migrated into the new core platform while also enabling future access to legacy and archive data outside the immediate cutover scope.
The result was a more controlled, scalable and transparent migration foundation — one that supported cutover readiness, reduced uncertainty around legacy data access, and created the basis for ongoing reporting and historical analysis after the move to the new core banking environment.
Neo Analytics: supported Core Banking Migration for a Stronger Future
When Beyond Bank embarked on one of its transformation journeys—merging with another financial institution—the complexity extended far beyond legal and operational alignment. It required a deep, data-led understanding of customer behaviour, systems integration, regulatory compliance, and brand continuity. This is where Neo Analytics stepped in as a strategic partner, not just a technology vendor.
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.
Neo Analytics was engaged to stabilise, simplify, and accelerate this transformation—particularly where internal capability had been lost. Our approach combined deep industry experience, a proven M&A data integration framework, and agile delivery practices to step into the void and ensure success from planning through to execution.
Rapid Data Recovery & Platform Enablement for Metro Finance
Metro Finance was experiencing an unexpected disruption that impacted core data assets across the business. This challenge affected operational continuity and internal reporting.
Recognising the need for a rapid response, Metro engaged Neo Analytics to deploy a cloud-based data foundation using Databricks to improve access and availability of data. The priority was to lay a scalable foundation for advanced analytics and AI enablement moving forward.
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.
Data access improvements occurred within weeks, significantly faster than projected recovery timelines.
Built a resilient analytics platform that now serves as a foundation for Metro’s ongoing data strategy.
Enabled data-driven decision-making to continue— across finance, operations, risk, and customer service teams.
Established confidence in data controls and recovery capability inline with expectations.
Supporting Greater Bank’s configuration for core modernisation.
Neo Analytics partnered with Greater Bank to support the configuration management of a highly complex data migration program focused on transitioning the bank’s core banking system to a new platform. Neo’s involvement helped bring structure, control and transparency to a critical transformation initiative with significant operational and technical dependencies.
Working closely with stakeholders across the program, Neo contributed to improving migration readiness, reducing delivery risk supporting the successful coordination of key configuration activities.
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.
Migrating from a bespoke core banking system to a general-purpose core banking platform offers several strategic and operational benefits, particularly when viewed through the lens of long-term scalability, regulatory compliance, and modernisation.
Neo built Qudos’ data foundations for automation, insight and AI capability.
Neo Analytics was tasked with providing a comprehensive framework for leveraging data as an asset to drive innovation, optimise operations, and enhance customer experiences. Neo devised a strategy to align with the bank’s overarching business objectives and outlined a systematic approach to effectively manage, analyse, and use data in compliance with industry regulations and cloud best practices.
Success outcomes ranged across strategic, operational, cultural, and customer domains and aligned with the expected banking sector KPIs and regulatory expectations.
