How to achieve exceptional model accuracy in minutes instead of months with automated feature engineering and unsupervised machine learning.
Sophisticated modeling practices are critical for modern fraud management, and the ability to detect, deter, and defeat massive-scale coordinated attacks is made possible by the power of AI and machine learning.
All modeling processes have three main steps:
- Data Collection and Cleansing
- Feature Engineering
- Model Building and Evaluation
In this post, we’ll discuss feature engineering, which is one of the most important and valuable steps for achieving the highest quality results.
Feature Engineering
Let’s begin by establishing a basic definition of feature engineering. A feature is a characteristic that can help solve a problem using machine learning. The process of extracting such features from a raw dataset is called feature engineering. There is an art to this process, and final results depend on how well this step is managed. Domain expertise and data insights help create the right features that produce the best possible results.
The challenges of manual processes
Feature engineering is often still performed manually by data scientists. A data scientist will analyze data and then, based on their domain expertise and experience, decide what features to create. The goal is better model results, but since many features are available for modeling, overfitting—an overabundance of applied parameters that narrow, and negatively impact, a model’s ability to perform—is a common problem. Adequate tools and technical skills are required for successful feature engineering, and even then, the process can still be labor-intensive and time-consuming.
The benefits of automation
Where there is a clear problem to solve, and domain expertise that can be applied, it is possible to standardize certain features that can be used for building models. These features can be automatically derived or extracted from raw data. For example: IP address is essential for fraud detection. For each IP address in the raw data, we should be able to derive additional features such as: ip prefix, ip city, check_ip_from_datacenter, ip_country, and more. In this way, we can begin to develop automated processes that increase both efficiency and accuracy.
Conclusion
Models are only as good as their data and features, and feature engineering is made more efficient and effective when the most important features necessary for fraud detection—as determined by extensive domain expertise—are automatically created. When paired with sophisticated unsupervised machine learning algorithms, automated feature engineering can deliver exceptional model accuracy in minutes instead of months.
