Classifying fraudulent transactions
What is fraud prediction?
No insurance company is exempt from investigating fraudulent claims. Knowing the likelihood of fraud in a transaction and the drivers behind it allows a company to lower its fraud rate by preventing transactions or conduct more efficient screening.
3 facts about the model
Fraud prediction as a standard model is a fast track to AI model implementation.
The model uses customer data and transactions to predict the likelihood of fraud.
The model enables you to prevent fraudulent transactions.
2021.AI offers fraud prediction by using a supervised algorithm. This means that a mathematical model is trained on a data set, describing the customers and the services bought, as well as transactions, together with a label (supervised) that classifies the case as a fraud case or not. The model stores which customer profile is associated with fraud, and which of the descriptive variables are most likely to classify the cases.
The business outcome
By using the Grace Standard Model for fraud prediction, the company gets an overview of the likelihood of fraud in a transaction. The company can then direct its screening efforts to those customers which have a high risk of fraud. Furthermore, the company gets insights into what drives fraud and with this information, start to change in order to lower the rate of fraud by screening more effectively.
Interested in taking AI into production?
If you think this model would be relevant for your organization go-ahead and book a demo! We would love to help you get started. Otherwise, you can download our Grace Enterprise AI Platform brochure for more information.