How AI and Machine Learning have impacted the banking industry

Like the Hotel and Airline industries, banks as physical spaces have been almost killed by the impact of Covid. This gets compounded with respect to millennials, nearly 40 percent of whom don’t use brick-and-mortar banks. Artificial intelligence combined with Machine Learning has clearly impacted this landscape, with AI-enabled chatbots and voice assistants now the norm at major financial institutions. We’re also seeing AI impact biometric authorization, and for those who enjoy the occasional throwback visit to a physical bank, AI-enabled robotic help. Artificial intelligence has transformed every aspect of the banking process. AI technologies are making banking processes faster, money transfers safer and back-end operations more efficient.

Banks and other financial institutions are aware of the importance of data. Data is a key differentiator in competing in the market. Machine learning and Artificial Intelligence technologies, provide the opportunity to analyze data and get deep insights into both customer and market behavior. Financial companies leverage data to revise their strategies, improve customer experience, prevent fraud, and mitigate risks.

Banks around the world will be able to reduce costs by 22% by 2030 through using Artificial Intelligence technologies. Savings could reach $1 trillion (Source: Autonomous Next). Financial institutions employ the largest technology staff and are self-sufficient to implement the AI models.

Key areas in Banks where we see the biggest impact of AI and Machine Learning are Data Analysis & Insights, increased productivity, and cost benefits savings

Artificial intelligence and Machine Learning in the banking sector will have a deep impact on banking operations and the way banks interact with the customer. The impact will be experienced both by the bank and the customer, with both eventually gaining from it. The banking sector already extensively uses AI and ML to automate many processes and we will see an exponential jump in its usage as we charge into the future. The major areas where we see it being used are as follows:

All major banks are embracing artificial intelligence and machine learning as a technique to reduce fraud. Banks are particularly targeted because of money transactions. Credit card frauds are most common. Any bank with a huge client base is susceptible to fraud and hence must keep leveraging the latest technologies to help their clients. They are achieving this with artificial intelligence and deep learning techniques.

Customer service is a core part of banking, and a high customer service rating often influences decisions about where you do your banking. Conversational AI and machine learning are now changing customer experience by accommodating chatbots, real-time feedback, and hyper-personalization customer support is getting reimagined. Virtual assistants such as Alexa, Siri, Google, and so on, upheld by AI, utilize deep behavioral learning to up-sell, cross-sell and decide on the best next steps to retain customers and maximize revenue for the bank

Automated transaction monitoring is a key application of Machine Learning in the banking sector. ML-powered Predictive Analytics platforms have already made an impact and help in monitoring AML transactions and help reduce false positives. KYC (Know-Your-Customer) is another area that has benefited from AI and ML with the usage of facial biometric and ML-based scoring to ensure higher compliance.

AI and ML technologies can revolutionize the banking sector. However, like with all new technologies, there is a fear of adoption—mostly associated with the novelty of technologies and the lack of full understanding among users about how they really work. One of the most common fears is disruption of the workforce leading to job cuts. There is also a ‘Trust’ issue because of the lack of human contact. Financial institutions are prone to ethical risks associated with the fact that financial companies use a huge amount of data to their advantage with respect to such technologies.

Finally, Machine Learning systems and AI track patterns of user behavior and correlate them with historical data to take cognitive decisions e.g., Automatic Loan Approvals. From a regulatory perspective there is no ‘Traceability’ of such decisions and the question of ‘Fairness’ becomes subjective.

The number of areas AI & ML are being leveraged worldwide in banking is constantly growing. Use cases in different areas of banking like automated customer support, real-time Fraud Detection, better customer data management, risk modeling, and marketing strategy planning are growing exponentially, and every bank can leverage them to improve its processes.

There is an explosion in the use of AI applications in retail banking, such as seamless integrations with non-banking apps, frictionless payments using facial recognition, investment recommendations, personalized offers, and money-management solutions. Robotic Process Automation using a BOT can help transform the banking industry, by automating the back-office operations, personalizing customized lending solutions, and performing automatic loan and mortgage approvals.

The pandemic resulted in people staying and working from home. This acted as a catalyst for the demand for digital services in the future powered with AI & ML. The demand for cutting-edge technologies in the financial industry is growing exponentially. Customer expectations are evolving very quickly to fast and functional artificial intelligence and machine learning financial services and personalized approaches among banks and other financial institutions. As technologies mature, artificial intelligence and machine learning in banking will be smarter and more adapted to business processes.