I rate this product a nine out of ten because there are only minor details I would change. However, we may have more opinions on the solution as we deploy it to more customers. A good piece of advice is to pay attention to the schema. You will need a definition of the schema, so you must be aware of the planning and gather all the information you need to process on the platform and monitor real events. For example, suppose you have a specific pattern you want to prevent, such as identity theft, and have one field that lets you know it is not identity theft. In that case, you must include it in the schema to have all the information you need to process on the platform. You must correlate every value or information in your core system to the schema Featurespace has.
What is Fraud Detection and Prevention? It wasn’t that long ago that fraud detection and prevention involved reviewing a fair bit of historical data analysis. Data scientists would be poring over tons of credit card records in order to spot fraudulent (or with luck, potentially fraudulent) activity.
Fast forward to today and we see fraud detection systems depend on catching and stopping fraud the second it’s spotted or even before it actually occurs. Automated solutions for fraud...
I rate this product a nine out of ten because there are only minor details I would change. However, we may have more opinions on the solution as we deploy it to more customers. A good piece of advice is to pay attention to the schema. You will need a definition of the schema, so you must be aware of the planning and gather all the information you need to process on the platform and monitor real events. For example, suppose you have a specific pattern you want to prevent, such as identity theft, and have one field that lets you know it is not identity theft. In that case, you must include it in the schema to have all the information you need to process on the platform. You must correlate every value or information in your core system to the schema Featurespace has.