Yapı Kredi Delivers Better Customer Insights 50% Faster
Customer Company Size
Large Corporate
Region
- Asia
- Europe
Country
- Turkey
- Malta
- Netherlands
- Russia
Product
- Datameer
- SAS
- Informatica
- SAP IQ
- Business Objects
Tech Stack
- Hadoop
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Cost Savings
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Data-as-a-Service
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Services
- Data Science Services
About The Customer
Yapı Kredi is the first national private bank of Turkey and has set the standards for the Turkish banking sector by introducing innovative products and services since 1944. It is now the fourth largest private bank in Turkey with over 19,500 employees and 11 million active customers. Adhering to a customer-centric strategy and segment-based service model, Yapı Kredi delivers its service through a network consisting of 1,015 branches and more than 4,217 ATMs in addition to its rich-content Internet and telephone banking applications. Yapı Kredi also has banks in four other countries: Russia, Azerbaijan, Netherlands, and Malta.
The Challenge
Yapı Kredi, the fourth largest private bank in Turkey, wanted to become a more data-driven company to increase business agility, reduce operating expenses, and improve the overall customer experience. However, they faced the challenge of deriving value from their vast amount of data, most of which was structured and stored in a traditional relational data warehouse. Their traditional business intelligence tools were too inflexible and forced a waterfall approach, which was time and resource-consuming. The rigid data schemas required before moving to the analysis step every time made the process laborious and slow. Yapı Kredi needed a more agile toolset for the iterative process of data discovery that’s important for any analysis.
The Solution
Yapı Kredi chose Datameer and Hadoop to expedite the traditional analysis process. Datameer allowed data preparation and analysis to be done in one application with one UI, enabling data scientists to prepare, discover and understand their data much more quickly for use in their SAS application. Business analysts were brought into the credit risk model update process to determine the relevant attributes to consider, as they understand their data in its context better and can more quickly determine the significance of data changes. With Datameer’s familiar Excel-like user interface, analysts learned to use Datameer quickly and built correlation calculations using Datameer’s 240+ functions to determine the relevant attributes to update in the model.
Operational Impact
Quantitative Benefit
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