The future of finance will be derived from Artificial Intelligence (AI), so much so, that AI in finance is already taking the industry by storm. Be it faster speeds, personalized response, reduced errors and identifying opportunities, AI technology is already at the epicentre of the biggest financial revolution to happen since the last fifty or more decades. Be it consumer electronics, transportation, retail, healthcare or marketing, Artificial Intelligence companies are ready to ride the machine learning wave of the future. Thanks to the strong technological push towards an Internet of Things (IoT) based world, even the banks are putting their money (and resources) where their mouth is. One of the biggest advantages of machine learning is that it can be designed and trained to deliver a smarter way of assisting customers.
Machine learning and Artificial Intelligence are two buzz terms that could have a profound effect on the overall customer experience related to financial institutions. These technologies are picking up steam as they make their way toward the mainstream in financial services, ultimately, making everything faster and more intuitive. Machine learning is a subset of artificial intelligence that enables computers to learn without being explicitly programmed. With machine learning, computers can analyze new information and compare it with existing data to look for patterns, similarities, and differences. David Gilvin, partner, banking & financial markets leader, IBM Digital Consulting, IBM, discussed this burgeoning theme during a session titled, "Machine Learning & Artificial Intelligence Powering Next Gen CX in Financial Services," at the recent Money20/20 Conference in Las Vegas.
Fraudsters are getting smarter and have more access to information than ever before. Old methods of authentication -- such as passwords, PINs or even bank account numbers -- can easily be obtained by fraudsters on the dark web. To outsmart bad actors and keep customers' information safe, financial organizations should consider how tools like AI can minimize opportunities for fraud and add an extra layer of protection into their security systems. Fighting fraud has always been a key challenge in the finance industry -- especially as fraudsters get more advanced in their approaches. A 2019 survey revealed that more than 60% of banks and other financial institutions saw the volume of fraudulent activity increase from the year before.
Whether financial institutions are looking for improved customer service, risk management, fraud prevention, investment prediction or cybersecurity, the scopes of machine learning and artificial intelligence are limitless. In the modern era of the digital economy, technological advancements are no longer a luxury for the organizations, but a necessity to outsmart their competitors and business growth. With the technological advancements in the recent times, the impact of Machine Learning (ML) and Artificial Intelligence (AI) are very critical than ever before. Previously, we discussed the scopes of big data and data science in banking and financial services. In this article will explain in detail about ML and AI, and their scopes in banking and financial services. Apparently, in order to be successful and making an impact, the banks and financial institutions need to make machine learning and artificial intelligence an expansion of their big data and data analytics approach. In this post, we will look into the Scopes of Machine Learning and Artificial Intelligence in Banking and Financial Services.
Brick-and-mortar banks are struggling to stay open as fintech firms and banking apps evolve. User-friendly, on-demand features are now the norm as customers look for financial partners that treat them as special instead of simply a number. As a result, competition is heating up among banks, and between banks and nonbank alternatives. Recent data suggests that 5.6 million Americans plan to switch banks in the next 12 months, with more than half making the move sooner rather than later. For financial institutions, changing market conditions speak to the need for improved customer experience: Satisfied clients are less likely to seek out banking alternatives, especially if banks can proactively react to emerging consumer demands.