AI Wants Your Money Signals
“Put your money where your mouth is!” This well-known phrase is often used to encourage people to back up their words with actions, especially in the realm of financial investments or support. Essentially, it means that talk is cheap, and true commitment or belief requires tangible actions that involve a commitment of resources like money.
We often think that text data, what a person writes and says, is the best way to learn about a person’s values and habits, but in reality, how they spend their money can reveal far more. By analyzing monetary signals – derived from the monetary fields in a dataset – we can gain valuable insights into consumer behavior and preferences.
For businesses, understanding monetary signals is essential. Customers who spend more or invest in higher-profit products are worth more, while those who cost more without contributing to profits are worth less. By using monetary signals, companies can better identify valuable customers and target them with tailored marketing strategies.
Monetary signals also play a crucial role in areas such as insurance underwriting and fraud detection. For example, insurers charge higher premiums or refuse coverage to individuals with a history of expensive claims. Banks and auditors use monetary signals to detect fraudulent transactions.
To improve the accuracy and effectiveness of monetary signal feature engineering, there are a few key tips to keep in mind.
- It is important to cap extreme magnitudes to reduce the bias of extremely bad luck.
- Since money has a time value, monetary values should be adjusted for inflation.
- Using event data with a one-to-many relationship with an entity can help to derive more robust monetary data.
At FeatureByte we put our money where our mouth is by building an open-source feature engineering library that makes it easy to create a dozen signal types! Click here to sign up for a free download, with worked examples in Python: https://23892737.hs-sites.com/featurebyte-early-access