Bank

Design and industrialization of IS analytics

A major banking player was launching a new entity and needed to build its entire Data and Analytics brick from scratch: infrastructure, pipelines, APIs, pricing models and automated credit scoring. We designed and industrialized the system, which is now in production on a global scale.

Problem

The client was creating a new entity and had to build their Analytics IS from scratch, within a tight timeframe. The entity needed to allow its customers to view services and rates, to subscribe online and to obtain an automatic response to their credit requests. Without Data Lake, Datawarehouse, pipeline, and scoring model, nothing could work. The challenge: design, build and industrialize the entire Data chain, from the ingestion of provider data to real-time scoring in a schedule compatible with the launch of the entity.

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Solution

What we built

We deployed a team of 1 Data Architect, 2 Data Engineers and 1 Full-Stack Developer to design and industrialize the entire Analytics IS on AWS.

Step 1 — Data architecture. Define the target architecture and build the Data Lake and Datawarehouse on AWS. Choice of technical components, implementation of the complete environment and associated governance rules.

Step 2 — Ingestion and transformation pipelines. Development and industrialization of ingestion pipelines in streaming and batch mode. Integration of provider data, pre-cleaning and transformation to continuously feed business use cases.

Step 3 — Data Catalog and APIs. Development of a data catalog to document and make data discoverable, and exposure of all data through APIs to serve front-end applications and models.

Step 4 — Pricing models and credit scoring. Production of pricing models allowing customers to visualize their rates in real time, and development of the automated scoring system to grant or refuse credit requests instantly.

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