Automated ML Pipeline for Fraud Detection Customization
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Solution Overview
Problem
Current fraud prevention systems in online industries face challenges in detecting new account fraud effectively due to high costs, complexity, and the need for significant domain expertise, often relying on external solutions with fragile customization options and unsatisfactory performance.
Innovation Solution
An automated machine learning pipeline generation system that validates, enriches, and transforms raw data into model features, trains optimized machine learning models, and generates executable packages for real-time scoring, enabling scalable and customizable fraud detection without infrastructure setup costs or inflexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If external fraud management solutions are used, then fraud detection capability is provided, but system customization is limited and performance is unsatisfactory
Solution Approach 1:
The system enables businesses to build and customize their own fraud detection models using automated machine learning pipelines. Users can independently configure data sources, select algorithms, and optimize parameters without relying on external vendors, thereby achieving both high customization and satisfactory performance simultaneously
2Adaptability or versatility
If machine learning fraud prevention solution is built in-house, then customization capability is achieved, but investment cost and operational complexity increase
Solution Approach 1:
The system divides the complex fraud detection process into modular automated pipelines with distinct stages: data collection, preprocessing, model training, evaluation, and deployment. Each stage can be independently configured and managed, reducing operational complexity while maintaining full customization capability
Solution Approach 2:
The automated machine learning platform provides a universal framework that handles multiple fraud detection scenarios and algorithms through a single system. This multi-functional approach allows businesses to achieve customization without proportionally increasing operational complexity, as the same infrastructure supports diverse use cases
3Reliability
If traditional fraud management solutions are deployed, then fraud detection is provided, but onboarding process is expensive and time-consuming
Solution Approach 1:
The system performs preliminary automated actions including automatic data collection from multiple sources, automated data preprocessing and feature engineering, and automated model training and selection. These preliminary actions are executed automatically without requiring manual intervention during onboarding, significantly reducing both time and cost while maintaining detection capability
4Reliability
If manual machine learning model building is performed, then model performance is optimized, but domain expertise requirement and development time increase
Solution Approach 1:
The automated machine learning system performs model training, hyperparameter optimization, and performance evaluation automatically without requiring users to have deep machine learning expertise. The system serves itself by autonomously selecting algorithms, tuning parameters, and generating optimized models that achieve performance comparable to manually built models
Solution Approach 2:
The system automatically adjusts and optimizes multiple parameters including algorithm selection, hyperparameters, and data preprocessing configurations through automated experimentation. This parameter optimization is performed automatically without requiring users to understand the underlying machine learning concepts, yet achieves high model performance
Data Source
AI summary
Various embodiments of apparatuses and methods for an automated machine learning pipeline service and an automated machine learning pipeline generator are described. In some embodiments, the service receives a request from a user to generate a machine learning solution, as well as a dataset that comprises values with different user variable types, and mapping of the user variable types to pre-defined types. The generator can validate the dataset, enrich the values of the dataset using external data sources, transform values of the dataset based on the pre-defined types, train a machine learning model using the enriched and transformed values, and compose an executable package, comprising enrichment recipes, transformation recipes, and the trained machine learning model, that generates scores for other data when executed. The service can further test the executable package using testing data, and provide results of the test to the user.