Companies in the financial sector may leverage artificial intelligence to analyze and manage data
from multiple sources to provide valuable insights. These innovative results help banks overcome
the difficulties they face on an everyday basis while providing day-to-day services such as loan
management or payment processing. Now, let’s examine some use cases of FinTech innovation
driven by AI, and the main benefits FinTech companies can gain from this technology.
Increased Security
AI in the realm of finance powers many solutions aimed at enhancing security precautions. For
instance, banks offer apps that can be accessed only with facial or fingerprint recognition. This is
made possible due in large part to Artificial Intelligence.
Improved Customer Service
There are many use cases when AI can improve customer experience and customer service. A couple
of examples include:
Chatbots in FinTech
AI-powered chatbots can minimize the workload placed on call centers as they tackle the most typical and frequent user problems. As simple as they may seem, each chatbot uses a complex sentiment analysis, which is possible thanks to artificial intelligence. This sentiment analysis focuses on understanding the customer’s
experience with your service/application, identifying ways it falls short, and training the chatbot itself to address those shortfalls. AI-powered chatbots make communication between a customer and a bank easier and more accessible. They use automated scripts to resolve simple complaints.
AI-Powered Personalized Banking Apps
Many banking apps offer personalized financial advice to help users achieve their financial goals,
track their income and expenses, and more.
User Behavior Analysis
Artificial Intelligence in FinTech can predict a user’s behavior with the help of AI APIs, which can also
be leveraged to banks’ and FinTech companies’ benefit. Let’s say, for example, that the user
requests data about their expenses in the last month – a single request. On the server side, with the
help of AI, you predict their follow-up request (e.g., income in the previous month) and provide this
information in the same response. As a result, you minimize the number of requests and the load on
your system accordingly. The user also benefits, as the system works faster if the predictive analysis
is correct.
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