AI fraud detection applications collect public customer data from across the entire internet to identify who is a real customer, and who may not be. Combined with a financial institution's internal customer data, a high level of accuracy is achieved in spotting fraudulent activities in real-time. Additionally, false flags are reduced. For example, in the past, if a credit card holder swiped their card from the other side of the country, the card ran the risk of being locked by the financial institution. With today's AI tools and predictive analytics, a bank may have access to a customer's geolocation, transaction history such as airline tickets, and social media posts regarding future vacations, preventing false flags, and ultimately, damage to the bank/client relationship.

If you take the word Ibiza, on its own a person reading would assume the word was referring to a location. However, used in the context of a vehicle manufacturer then the word has a different meaning. When using NLC, or'classifying data', then words and information are labelled - for example the word'Ibiza' in the above use case can be labelled as a'vehicle model' so when read, the context of the word can be understood. The training process can be described in a similar way to how people learn - no one is born with domain specific knowledge, but once taught, they can understand and respond to different words dependant on their meaning.