All eyes on … AI in banking part 1/2
Do You remember artificial intelligence from the movie “I Robot”? When character played by Will Smith asked the question “Can a robot write a symphony?” Instead of the original “Can You?”, today’s Robot will be able to… write a symphony*. The development of artificial intelligence occurs very quickly, and banks as highly technologically advanced and managing huge amounts of data are natural candidates for using artificial intelligence to meet their needs.
So how can banking use the potential of this technology?
Data analysis and profiling
Banks are managing big amounts of data. In addition to the history of clients, currently, a large number of possibilities to contact with the bank (branches, Internet, phone, mobile applications) generate a lot of data connected with customer preferences and behaviors. First of all, banks are obliged to collect and analyse these data but the key to maximizing profits is the ability to use them effectively. Due to the analysis of the right amount and quality of data, AI is able to get to know the client better than a bank advisor. In the next step, these seemingly insignificant information about customers behavior can be transformed into data, which will provide knowledge about the client that can turn into a new source of revenue, signal a change in the client’s situation and immediately offer him a tailored solution, or predict when the client is planning to resign from services.
Security is one of the clients main demands made on banks. But as you can easily guess information related to user account are one of the most common areas of interest for criminals. And in this matter AI is coming for rescue. Routine checking of risk factors allows to faster detection of potential fraudsters and minimizing the losses.
The base for most frauds is an attempt to impersonate another person, the owner of the card/account. What may block this kind of attempt is collecting data from many sources: uses device, Internet connection, software, user standard behavior. All collected information is enriched with context and new experiences and analyzed by artificial intelligence. The model finds links in seemingly unrelated parameters and extracts valuable information about both: individual clients and customers in general. This information allows identifying transactions that bear the mark of fraud. What’s crucial: everything happens in real time – a time of reaction is a key factor.
An example of effective fraud detection can be a situation when extremely high transactions (unusual for clients’ account), occur and the bank’s fraud detection system stop them until they are confirmed by the account owner. Although it sounds like a simple procedure it can actually cause many problems – we can not forget about the User Experience – everything must be done in such a way that doesn’t disturb clients’ comfort.
Another way to use artificial intelligence for protecting data is replacing traditional passwords with voice or face recognition.