Personalizing the consumer lending experience
Oakbrook Finance, a UK consumer lending fintech, moved to the Databricks Data + AI Platform on Google Cloud to unify data science and analytics for its loan decisioning platform, O6K, replacing a legacy SQL Server Integration Services environment that could not support advanced risk-prediction analytics. The new platform enabled faster development of data science models for personalized lending decisions, projected 14% cost savings over three years, supported two new financial products launched in 12 months, and is forecast to drive a 5% revenue increase from new top-up loans.
Overview
Oakbrook Finance, a UK consumer lending fintech, moved to the Databricks Data + AI Platform on Google Cloud to unify data science and analytics for its loan decisioning platform, O6K, replacing a legacy SQL Server Integration Services environment that could not support advanced risk-prediction analytics. The new platform enabled faster development of data science models for personalized lending decisions, projected 14% cost savings over three years, supported two new financial products launched in 12 months, and is forecast to drive a 5% revenue increase from new top-up loans.
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Inspect the highlighted sourceThe challenge
Oakbrook's legacy SQL Server Integration Services (SSIS) environment, optimized only for simple SQL queries and reporting, could not support more advanced analytics or data science such as predicting an individual's loan risk. Data teams writing SQL, R and Python could not collaborate, processing often happened outside the main data environment, and the platform suffered limited storage, inability to scale, complex and unreliable data flows, and siloed third-party data.
The solution
Oakbrook adopted the Databricks Data + AI Platform on Google Cloud, letting data scientists working in R or Python operate in a unified environment with their own clusters instead of blocking each other with overnight SQL queries. Oakbrook introduced Fivetran, a Databricks technology partner, to ingest data into the lakehouse in a low-code way instead of building bespoke ETL processes, extracting previously siloed call center and marketing data for analysis. The platform also enabled building summaries of customer information to provide more personalized lending decisions, including a top-up loan and collections decision engine.
Reported business value
Oakbrook projects 14% cost savings over the first three years through productivity gains and optimized compute, launched two new financial products in 12 months, cut new-data-source onboarding time from days to half an hour, and forecasts a 5% revenue increase over the next 12 months from new top-up loans offered to 10,000 customers.
Sources
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