Credit Scoring

Accelerate and enhance credit scoring via interactive scorecards and machine learning techniques.

The goal of this adapt and apply solution is to enable Credit Risk Analysts to understand how Dataiku can be used to create a credit-worthiness model to build scorecards in a user-friendly and customizable way. More details on the specifics of the solution can be found in the knowledge base. This Solution is only available on installed instances.

Business Overview

Credit decision making is the cornerstone of successful lending operations, and continuously evolves with customer behavior and data availability. The complexity and depth of analysis required to offer competitive pricing and accurate prediction of credit events is ever increasing. Higher performance models demonstrably increase revenue (5-15%), reduce credit loss (20-40%), and improve efficiency(20-40%)*

Credit scorecards are a foundational part of a credit teams’ workflow, and enhancing them with more powerful data sources and faster collaborative review is vital to retaining and expanding a customer base. Existing tools can be difficult to adapt to this new environment, and future-focused approaches can often be disconnected from the current technology and needs of the team, siloing the potential benefits and preventing them from being effectively integrated into the working model that directly impacts customers. 

By leveraging Dataiku’s unified space where existing business knowledge, machine-assisted analytics (for example, automatic searching of a large number of features and feature iterations for credit signals), and real-time collaboration on credit scorecards are unified, credit teams can immediately benefit from the value of an ML-assisted approach, establish a foundation on which to build dedicated AI credit scoring models, all while remains connected to their current customer base and systems.


  • Use machine-assisted analysis for your team to move faster and evaluate larger and more varied datasets while retaining complete control and flexibility
  • Leverage a full Responsible AI framework to quickly integrate fairness checks across the entirety of the feature selection and scorecard building process.
  • Access integrated interactive scorecards to allow modeling teams and stakeholders to work collaboratively and in real-time to understand the drivers of any credit scoring model.
  • Establish an AI foundation to allow easy experimentation and evaluation of AI credit scoring approaches, in parallel with existing techniques.