Access integrated interactive scorecards and evaluate in real time the impact of changes to features on the credit score with an entirely visual interface. Allow modeling teams and stakeholders to work collaboratively in real time to understand the drivers of any credit scoring model.
The Dataiku Solution for Credit Scoring takes credit risk teams from model prototyping and eased review by model risk management teams to model exposure in their choice of systems in a matter of a few steps. Thanks to this unified approach, governance is seamless, and the capacity to react to market evolutions is accelerated.
Explore and understand the fairness impact of the features within your model, supported by simple diagnostics and a Responsible AI (RAI) framework. Leverage the framework to quickly integrate checks across the entire process.
Use integrated weight-of-evidence (WOE) and automatic binning techniques to explore the fairness impact of features within your model and perform feature selection and interpretation using ML-supported techniques.
Quickly and easily perform feature selection and interpretation using ML-supported techniques alongside intuitive calculations and visualization. Use machine-assisted analysis to more quickly evaluate large datasets, retaining complete control and flexibility while connected to current customer datasets and tools.
The Dataiku Solution for Credit Scoring helps answer a broad range of questions like:
Establish a foundation to build dedicated AI credit scoring models, all while staying connected to current customer systems. Get immediate, actionable insights while complying with internal policies and ethics and with existing regulations. From machine-assisted analysis and a full RAI framework to interactive scorecards, the Dataiku Solution for Credit Scoring takes your credit risk models to the next level.
A composite organization in the commissioned study conducted by Forrester Consulting on behalf of Dataiku saw the following benefits:
reduction in time spent on data analysis, extraction, and preparation.
reduction in time spent on model lifecycle activities (training, deployment, and monitoring).
return on investment
net present value over three years.