Improving Fraud Detection by Evangelizing Data Science
Company Size
1,000+
Region
- Europe
Country
- Luxembourg
Product
- Dataiku Data Science Studio (DSS)
Tech Stack
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
BGL BNP Paribas is one of the largest banks in Luxembourg and part of the BNP Paribas Group. It offers an especially wide range of financial products and bancassurance solutions to individuals, professionals, private banking clients, and businesses. In 2017, the international magazine Euromoney named BGL BNP Paribas “Best Bank in Luxembourg” for the second year in a row. The bank was already using a machine learning model for advanced fraud detection, but it was largely static due to limited visibility and data science resources.
The Challenge
BGL BNP Paribas, one of the largest banks in Luxembourg, had a machine learning model in place for advanced fraud detection. However, the model remained largely static due to limited visibility and limited data science resources. The business team was keen on updating the model but faced challenges due to lack of access to data projects and the data team. The challenge was to harness a data-driven approach across all parts of the organization. The bank needed a solution that would democratize access to and use of data throughout the company, without compromising data governance standards.
The Solution
BGL BNP Paribas chose Dataiku Data Science Studio (DSS) to democratize access to and use of data throughout the company. In just eight weeks, BGL BNP Paribas was able to use Dataiku to create a new fraud detection prototype. The project involved data analytics and business users from the fraud department as well as data scientists from BGL BNP Paribas’ data lab and from Dataiku. The collaborative nature of Dataiku and involvement of teams throughout the company allowed for the optimal combination of knowledge to produce an accurate model delivering clear business value. Dataiku’s production features allowed for a smooth transition in BGL BNP Paribas’ production environment, enabling the new fraud prediction project to show results very soon after the start of the project.
Operational Impact
Quantitative Benefit
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