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CUSTOMER STORY

Fostering financial resilience through hyper-personalization

20x

Faster data processing times — from 9 hours to < 10 minutes

5x

Faster data product creation and implementation times

40%

Uplift in the impact of customer engagement activities

CLOUD: Azure

“The Databricks Data Intelligence Platform enables small teams to build higher-quality data products quicker and at a lower cost than anything else we tried or were doing before. It’s simple to use, fast and easy to manage.”

— Stuart Emslie, Head of Actuarial and Data Science, Discovery Bank

Discovery Bank was launched in 2019 to transform banking in South Africa with their shared value banking model. By incentivizing and empowering clients to cultivate positive financial habits, Discovery Bank creates a virtuous cycle wherein clients, the bank and society at large benefit. Using the Databricks Data Intelligence Platform, Discovery Bank combines data and actuarial science with behavioral economics, AI and machine learning (ML) to create data products and hyper-personalized experiences that reward healthy banking habits and lower financial risk. The Databricks Platform transformed the bank’s ability to create fast and reliable data products, resulting in substantial client, business and societal impact.

Struggling to deliver outcomes among complex pipelines

When Discovery Bank set off to bring their shared value banking model to market, it demanded a data-driven approach with sophisticated insights and analytics. This model required a granular understanding of the drivers of client behavior and the causal impacts of these behaviors on risk and profitability, so Discovery Bank focused on building an ecosystem of data products that could power applications throughout the client lifecycle and across business functions. Their legacy on-premises environment struggled to keep up with the volume and velocity of data required to enable this ecosystem.

Stuart Emslie, Head of Actuarial and Data Science at Discovery Bank, explained, “We wanted to create standardized frameworks to get from idea to production as quickly as possible, but we had to spend a lot of time engineering specific processors to achieve the required performance. We didn’t have the flexibility we needed to rapidly iterate on data products due to the complexity of integrating the multiple components in our ecosystem that were often not geared for advanced analytics.”

Discovery Bank required a scalable platform that could surface client data and enrich it with a multitude of other features to unify organizational objectives and execute hyper-personalization use cases.

Fast and efficient analytics with the Databricks Platform

Discovery Bank chose to standardize and consolidate their data, ML and AI on the unified Databricks Data Intelligence Platform to drive use cases with ease, speed and cost-effectiveness. The centralized platform provided Discovery Bank with a comprehensive view of their data and transformative technology.

Emslie said, “The Databricks Platform has transformed our ability to build the centralized ecosystem we need to drive shared value. It integrates everything we need into one consolidated platform, without dependencies. It just works.” The integrated environment simplifies data processing, modeling, serving and dashboarding, creating a seamless end-to-end process for fulfilling business use cases. These efficiencies resulted in an exponential increase in time to insights, time to production and ultimately, data-led innovation, yielding a significant return on investment of more than 500%.

With the Databricks Data Intelligence Platform, Discovery Bank data practitioners can now seamlessly incorporate tailored data and modeling pipelines, enabling a data ecosystem that is secure and reliable. Using the medallion architecture in Delta Lake, Discovery Bank leverages automated workflows to schedule ETL pipelines that quickly consolidate and prepare raw data for the correct layer and use case. With MLflow, Discovery Bank builds model requirements for governance and ensures model quality for accuracy in downstream analytics and production use cases. And to provide a level of data and AI governance that was not previously possible, Discovery Bank has migrated to Databricks Unity Catalog to manage lineage, table history and access controls, further supporting cross-team collaboration for innovation and speed.

Banking innovation through hyper-personalization

Since migrating to the Databricks Data Intelligence Platform, Discovery Bank has had unprecedented insights into what motivates people to form healthy financial habits. The bank now operationalizes those insights within data products and embeds shared-value and data-driven decision-making throughout the business.

Having a centralized data ecosystem on the Databricks Platform has allowed Discovery Bank to integrate data processors, advanced ML models, complex actuarial models and generative AI capabilities across the client lifecycle. With these granular insights and data-driven applications, Discovery Bank can more accurately select and price clients, project and model the lifetime value of clients throughout their lifecycle, boost engagement through personalization and mitigate individual client risk by identifying irregularities and unsafe behaviors. These improvements not only support individual financial and societal health but also increase the lifetime value of the client and improve client satisfaction.

Data teams are empowered with direct pipelines and an integrated ML framework that eliminates SQL conversions and simplifies code management. Data processing times are 20x faster and data product creation and implementation times are 5x faster. Taking advantage of cross-team collaboration, reduced complexity and high-efficiency gains, Discovery Bank has also increased their model-building capacity to more than 300 models per day.

Now, Discovery Bank is executing their hyper-personalized shared value banking model by combining and measuring client actions, milestones, pathways and behaviors to create a behavioral fingerprint for each client. This fingerprint informs segmentation within marketing strategies, business pricing and risk management, client servicing and fraud management, and behavioral change initiatives within the bank’s next-best-action (NBA) framework.

Nic Salmon, Chief Product Officer at Discovery Bank, said, “A centralized NBA system is critical as it allows us to create meaningful and personalized interactions with our clients. By understanding our clients’ needs, preferences and financial health, we can communicate our products and services in a way that is specific to a client and where they are on their journey to financial health.” Discovery Bank’s NBA model has resulted in a 40% uplift in the impact of their engagement initiatives and has been integrated into client servicing, assisting agents with AI-generated communication templates.

Moving forward, Discovery Bank will continue implementing and measuring their shared value objectives on the Databricks Platform, using ML-driven actuarial modeling that accounts for client, business and societal impacts.