Fintech WLTH unlocks data-driven growth without scaling the data team
Customer Story

Fintech innovator WLTH unlocks data-driven growth without scaling the data team

Data done different

WLTH, an Australian digital lending and payments provider, is on a mission to refresh the financial services industry in Australia by leveraging technology and sustainability. Since its establishment in Brisbane in 2019, WLTH has been dedicated to delivering a better experience to small and medium business owners who are often underserved by big banks.

A key aspect of WLTH's approach is their belief in "data done different." They recognize the value of aggregating data from various sources to get to know their customers better and gain complete visibility to accelerate business innovation. Additionally, WLTH utilizes AI analytics to make the right recommendations and to power an intelligent financial product suite, including home loans, business lending, payment accounts, and credit cards made from recycled ocean plastic.

WLTH funds the cleaning of 50 square meters of Australian coastline with each settled home loan.

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When WLTH embarked on their journey to reshape the financial services industry, they turned to Atturra, a VaultSpeed Sapphire-certified service provider specializing in building tech infrastructures. Atturra was there right from the beginning, assisting WLTH in defining their big picture problems, and determining the technology stack needed for success.

One of the significant challenges WLTH faced was managing their data. Initially, their data resided in third-party solutions, and they needed to aggregate it to create a cohesive and valuable customer experience.

To address these challenges, they decided to invest in a data infrastructure which would seamlessly weave together different datasets and provide easy and uniform access to data. Atturra proposed best-in-class vendors, including Snowflake for cloud storage, OKTA for identity and access management (IAM), and VaultSpeed for data warehouse automation.

Why VaultSpeed

When WLTH was searching for a solution to their data management challenges, they recognized the need for a genuinely agile setup that would allow them to add new financial products without the hassle of redoing existing data pipelines. They wanted an efficient, out-of-the-box solution that required low maintenance. That's why they turned to VaultSpeed and the Data Vault methodology.

This choice aligned perfectly with WLTH's business goals. Rather than being technology driven, VaultSpeed focused on defining and modeling the business entities, such as customers and products. This approach provided WLTH with the flexibility to adapt to changing business needs without sacrificing data integrity and consistency.

VaultSpeed's cloud-native setup, which only extracts metadata and not actual data, addressed all security and privacy concerns of the fintech business. This design ensured that sensitive data remained protected.

VaultSpeed no-code automation was the natural choice for efficient and effective design, deployment, and operations. It offered a comprehensive automation solution, that streamlined the data warehouse creation process and guaranteed reduced maintenance costs into the future.

“It’s critical to WLTH to automate data integration to make fast, data-driven decisions, but we weren’t happy with expensive enterprise solutions that required large teams of high-powered data engineers. Atturra suggested VaultSpeed automation and smoothly implemented Snowflake’s Data Cloud with a low-risk deployment and maintenance package which had us up and running quickly. With only one internal Data Engineer on staff, we now have full command of our financial services data as we planned."
Brodie haupt
Brodie Haupt CEO of WLTH


WLTH's decision to implement VaultSpeed proved to be transformative for their data management.

WLTH was able to get the bigger picture in record-breaking time. The data engineer at WLTH underwent comprehensive VaultSpeed training, equipping them with the skills and knowledge to leverage the platform effectively. Atturra also provided VaultSpeed-designed professional services guidance to WLTH, ensuring a smooth implementation and ongoing support.

The benefits of implementing VaultSpeed were evident across the organization. WLTH gained a deeper understanding of their customers, allowing them to propose the right recommendation to the right person at the right time.

Additionally, VaultSpeed empowered WLTH to generate steering reports and regulatory reporting with ease. Compliance requirements were seamlessly integrated into the data infrastructure, enabling WLTH to meet regulatory obligations efficiently.

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VaultSpeed played a vital role in achieving a record-breaking implementation time.

  • The VaultSpeed no-code platform, combining built-in automation templates, metadata repository, and intuitive GUI, relieved the WLTH data team from the gradual process of creating a comprehensive model from scratch, component by component, table by table.
  • It automatically proposed a comprehensive WLTH model based on extracted metadata from transactional systems, customer and market sources, and external data feeds.
  • Nor did the data team have to waste time on preparation, writing code to make automation work for their specific data and technology stack. With its out-of-the-box automation of data integration, modeling, and ETL/DDL code generation, VaultSpeed expedited the deployment process, enabling WLTH to serve its customers quickly and efficiently.
  • Even with WLTH's growth in products, services, and customer base, they maintained a one-person data engineering team.

VaultSpeed's automation capabilities reduced the burden on their data personnel while ensuring faster credit decisions, vastly improved customer experience, lower costs, and a more secure risk profile.

The combination of data-driven insights and automation positions WLTH to stay ahead of the curve and extend its platform's capabilities to assist other banks, fintech, and corporates in the future.


  • 4

    Source systems integrated
  • 86

    Objects in the Data Vault
  • 34 days

    Between first source ingestion and first Data Vault code generation