About Automated Feature Building
Automated Feature Building is a software platform from Pngme designed to change raw financial data into machine learning-ready features automatically. It provides rapid feature generation, industry-specific templates, and automated feature selection so developers can reduce feature engineering time from weeks to hours. This platform facilitates easy access to over 500 predictive features, enabling developers to integrate these features directly into their machine learning models. With its specialized functionalities, users can quickly adapt to various industry requirements, making it a versatile tool in the data science toolkit. Key capabilities: rapid feature generation industry-specific templates automated feature selection 500+ predictive features ML model integration Best for: developers that need efficient feature engineering for machine learning projects.
Automated Feature Building is a critical, AI-driven component of the Pngme data platform, designed to revolutionize credit risk assessment in emerging markets. It excels by automatically generating over 500 predictive, ML-ready features from alternative data sources, enabling financial institutions to accurately score and lend to the previously unbanked or underbanked population. By drastically reducing the time spent on manual feature engineering, it accelerates the deployment of credit models, leading to proven results in lowering default rates and boosting approval rates. While its primary geographic focus is Sub-Saharan Africa and its pricing is custom-based, it is an indispensable tool for banks and fintechs seeking to unlock financial inclusion and achieve market leadership through sophisticated, data-driven lending.
Pros & Cons
- +Built to adhere to multiple data privacy regulations and is SOC 2 Type 1 compliant
- +Provides a lot of features that offer significantly deeper and multi-dimensional risk insights
- +Automates the most time-consuming step in ML
- +Highly effective at generating alternative data features for credit scoring the unbanked or underbanked populations
- −Pricing is not public
- −Institutions need robust processes to understand and validate the features for regulatory and ethical compliance.
- −The quality and volume of the features are dependent on the accessibility and completeness of the data ingested by the Pngme platform.
Features
Key features
- 500+ Predictive Feature Generation
- Automatically generates over 500 complex features from raw transaction, SMS, and other financial data points, moving beyond basic metrics to create highly predictive variables for ML models.
- Rapid Feature Generation
- Accelerates the traditional, manual process of feature engineering, allowing ML models to be built and iterated upon much faster for quick deployment.
- Custom Use Case Adaptability
- Features are customized and tailored for specific industry challenges and use cases, such as assessing credit risk for underbanked segments like gig workers or motorcycle taxi operators.
- ML Model-Ready Output
- The platform cleans, structures, and transforms raw input data directly into a clean, standardized format that is ready to be consumed immediately by downstream ML models for scoring.
- Industry-Specific Templates
- Provides pre-built templates and logic for common industry challenges (e.g., loan stacking detection, segmentation) that expedite model development.
Additional features
- Dynamic Income Score
- A proprietary feature that predicts the stability and reliability of a customer's income based on transaction patterns.
- Lifestyle Affinity Index
- Measures and scores a customer’s lifestyle preferences and spending habits, providing behavioral insights.
- Credit Behavior Pattern
- Analyzes repayment history and credit utilization patterns across various accounts to build a comprehensive credit profile.
- Automated Feature Selection
- The system can help select the most informative and relevant features for a specific credit scoring model or business problem, reducing bias and overfitting.
- Improved Decision Accuracy
- Leveraging a higher volume of predictive features leads to more accurate, data-driven loan decisions and risk assessments.
- Data Ingestion & Aggregation
- Part of the overall platform that connects to and ingests financial data from hundreds of institutions across multiple countries.
- Comprehensive Risk Profiling
- Allows users to segment customers and create holistic financial profiles based on varying account and SMS data.
Pricing
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Available in
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About Automated Feature Building
Automated Feature Building is a software platform from Pngme designed to change raw financial data into machine learning-ready features automatically. It provides rapid feature generation, industry-specific templates, and automated feature selection so developers can reduce feature engineering time from weeks to hours. This platform facilitates easy access to over 500 predictive features, enabling developers to integrate these features directly into their machine learning models. With its specialized functionalities, users can quickly adapt to various industry requirements, making it a versatile tool in the data science toolkit. Key capabilities: rapid feature generation industry-specific templates automated feature selection 500+ predictive features ML model integration Best for: developers that need efficient feature engineering for machine learning projects.
Automated Feature Building Details
Automated Feature Building's In-App Market Place
Does Automated Feature Building have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
N/A
Pricing Options
Accepted Payment Currencies
USD ($)
Pros & Cons
- Built to adhere to multiple data privacy regulations and is SOC 2 Type 1 compliant
- Provides a lot of features that offer significantly deeper and multi-dimensional risk insights
- Automates the most time-consuming step in ML
- Highly effective at generating alternative data features for credit scoring the unbanked or underbanked populations
- Pricing is not public
- Institutions need robust processes to understand and validate the features for regulatory and ethical compliance.
- The quality and volume of the features are dependent on the accessibility and completeness of the data ingested by the Pngme platform.
Automated Feature Building's Support Options
Email Address
hello@pngme.comDocumentation
https://developers.api.pngme.com/