Artificial intelligence is rapidly spreading across industries that rely heavily on data — which covers virtually all industries. The financial services sector is no exception.
Some studies show that 52% of financial services industry executives are currently making “substantial” investments in artificial intelligence (AI), and 72% of business decision-makers believe that AI will be a business benefit in the future. Machine learning (ML) has become a perennial competition in fintech, creating almost infinite possibilities as it grows and develops.
With this in mind, read on more about some of the areas where financial institutions are developing machine learning capabilities.
Customer service is an integral part of banking and other financial services for the foreseeable future, which is why machine learning is so important in this area.
For example, to process and call automation, JPMorgan Chase, Bank of America, Citibank, PNC and US. Many large banks, including the bank, are focused on improving customer service in the region while increasing revenue.
Security and fraud detection
It is always important to identify security and fraud in the financial sector for obvious reasons. With ever-evolving technological advances and increasing transactions, the security threat is on the rise, and ML is a prizefighter in this regard. ML algorithms can be used to find out if a particular activity is suspicious or out of character and can be flagged accordingly. Not only that, but it also provides better user authentication by analyzing a variety of factors.
Mobile banking is directly impacted by AI and ML customer service and security and fraud detection innovations — and it is also looking at other benefits.
The AI in mobile banking fundamentally reshapes the customer experience. The main premise of mobile banking is to provide banking services around the clock, as well as to help customer support staff focus on more complex tasks.
For example, chatbot users such as Bank of America’s Erica, an AI-based virtual assistant, can help customers check balances, remember bills, and answer bank-related questions. To pick up the deal further, Erica does not take a lunch break and does not need a day’s leave.
Algorithmic trading is a veteran when it comes to the use of machine learning, but that does not even mean that we see a fair share of its progress. The human brain is very limited in the amount of information it can analyze in a single moment compared to machine learning algorithms.
Analyzing thousands of pieces of data simultaneously allows ML to return a rigorous evaluation based on Q-learning to assess both profit and potential risk and help the human side in making commercial decisions.