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Achieving Agility and Transparency through Integrated Feature Management

Published: May 22, 2025 by Jason Pardus

For financial institutions to achieve success, they need to develop high-performing models with easy access to top-tier data sources. It’s also important to focus on data governance, compliance, and risk management throughout the lending lifecycle.

Industry leaders implement advanced analytics and AI solutions to improve their lending decisions, and they also incorporate integrated, efficient feature engineering into their business operations.

What’s feature engineering?

Feature engineering helps organizations turn raw data into comprehensive model development, following this process:

  1. Data collection
  1. Data cleaning and transformation
  1. Feature engineering
  1. Model training and evaluation
  1. Decision-making

Effectively transforming data into valuable insights depends heavily on creating new custom features to enhance model performance, as well as the quality of the data being used. When data is fragmented or managed poorly, it can lead to increased operational costs, missed revenue opportunities, and compliance risks.

Our feature engineering solution: Experian Feature Builder

Financial institutions require optimized workflows that can accelerate development while supporting governance and ensuring transparency. Experian’s feature engineering tool, Experian Feature Builder, streamlines custom feature development and deployment across the modeling lifecycle.

Providing access to 20+ years of proprietary data, Experian Feature Builder enables organizations to:

  • Break data silos by creating unified access across multiple data types
  • Ensure trust and compliance by embedding audit and lineage tracking at each stage
  • Enable strategic agility with faster and more consistent feature experimentation, testing, and deployment

Download our latest e-book to find out more about how Experian’s Feature Builder provides centralized feature development to accelerate time-to-market, enhance compliance, and minimize risk.

Related Posts

By integrating feature engineering, organizations can convert raw data into more accurate features and build higher-performing models.

Published: April 28, 2025 by Jason Pardus