According to experts, the banking and financial sector need to continue embracing revolutionary AI technology. Through it, they will remain relevant to customers and resist the proactive competition posed by fintech players in the market. Artificial intelligence in the banking sector is maturing, creating the potential for more complex solutions that generate positive ROI across different business segments.
The use of AI in banking has become standard in a bank's daily operations. Many financial services companies say they have implemented technology across multiple business domains, such as risk management (56%) and revenue generation through new products and processes (52%), as per the Cambridge Centre for Alternative Finance and the World Economic Forum. As AI gains popularity and improves customer engagement in banking, financial institutions are building on their existing solutions to solve complex challenges they might face. By increasing labor productivity, AI technologies could structurally reduce costs in the banking sector. Rapid implementation of AI technologies is central to fighting persistently weak profitability while remaining competitive.
AI is taking over every industry and the use of AI in banking and the financial services sector is no exception.
A report by McKinsey about global banking estimates that artificial intelligence technology has the potential to deliver nearly $1 trillion in additional value every year. The finance and banking sectors ought to become 'AI-first' to unlock such tremendous values. AI technologies include traditional AI solutions for automatic loan approval, risk assessment, and even customer service chatbots. Banks leverage algorithms on the front-end for seamless customer identification and authentication, mimic live employees through chatbots and voice assistants, deepen customer relationships and provide personalized insights and recommendations. Banks are also implementing AI within middle-office functions to assess risks, detect and prevent payment fraud, improve processes for anti-money laundering (AML) and perform know-your-customer (KYC) regulatory checks.
Some think introducing artificial intelligence in banking replaces human-intensive industries with capital-intensive ones. This is where they are wrong - AI extends human capabilities, but it's not a replacement. The demand for AI technology is directly associated with organizational AI maturity levels. The 2022 Gartner CIO and Technology Executive Survey confirmed that interest in AI is increasing, with 48% of CIOs (Chief Information Officers) planning to deploy AI and ML technologies in their organizations within the next 12 months.
According to Gartner, in 2019, 37% of businesses adopted AI in one form or another worldwide. MarketsandMarkets also forecasted that the global AI market would grow from $58.3 billion in 2021 to $309.6 billion by 2026. Demand for AI technologies and associated market growth to organizational AI maturity levels are closely tied.
AI services and customer engagement in banking
AI-powered decisions enable banks to create an intelligent, highly personalized servicing experience based on customer microsegments, enabling different channels to deliver superior service and a compelling experience with fast, simple and intuitive interactions. Banks can support their relationship managers with timely customer engagement and tailor-made offers for each customer.
Customers are evolving as they instantly access their smartphone bank accounts and pay bills with a tap on their devices or the gadgets they wear. Customers have embraced advanced technologies in their daily lives and expect banks to deliver seamless experiences. Technology giants like Facebook and Amazon are challenging the traditional banking system with the evolution of the fintech mechanism.
Competition in this sector is going to increase. Artificial intelligence is successfully employed to provide a convenient and informed customer experience at any point along the customer journey. AI uses raw data to dive deeper into every customer's behavior and purchase patterns to perform predictive analysis to drive better engagement at the right place and time.
How banks leverage AI in their credit process
Regarding the credit decision-making process, AI can be used in several ways, making the process more agile and efficient. One of the initial aspects of the lending decision is the validation of the creditworthiness of individuals or businesses seeking loans. Banks are looking at creditworthiness as one of their everyday applications of AI.
This process starts by collecting, analyzing and evaluating information required to determine a borrower's creditworthiness. After completion of credit analysis, the credit officer then proposes a loan structure for approval that preserves the borrower's strengths and protects against identified weaknesses. At the last stage, the process ends by determining a risk rating, and subsequently, loan approval /rejection. This lengthy process can lead to human errors when done manually.
Therefore, introducing creditbots into the banking and financial services industry to manage their credit process is critical, as the influence of artificial intelligence in the banking sector is far more precise than humans in credit evaluation and lending decisions. Creditbots use an algorithm to evaluate customers' behavior, credit history and banking transactions to determine if they should approve or reject the customer.
Creditbots use past customer payment records to train machine learning models to estimate the risk of non-payment by a lease or borrower. They help in maximizing revenue with highly accurate and swift decisions. It can help banks and financial companies handle up to 2x of credit requests at a 20% more rapid pace. From legitimizing a new customer who applies for credit to choosing a suitable credit product or optimizing the credit check - the scope of intelligent data analytics in the credit sector is vast.
Not managing the credit process through a proper AI system can lead to bad debts. Introducing AI in banking improves their growth rates, which may have fallen due to the credit risks involved.
Evaluating a customer's creditworthiness is one of the most unaccountable tasks for banks. An AI-based loan and credit system can help by studying customers' behavior patterns with limited credit history to determine their creditworthiness. The system also warns banks about the specific behavior(s) that may increase the chances of defaulting. There are certain risks involved in extending credit to borrowers. Banks must contend with bad debts despite thoroughly assessing the borrowers' creditworthiness. S&P Global Ratings had forecasted credit losses of about $2.1 trillion for banks across the globe in 2021.
Many services and products are AI-based and are transforming the financial services industry. According to joint research by the National Business Research Institute and Narrative Science, about 32% of financial service providers already use predictive analytics, voice recognition and other AI technologies. Artificial intelligence in banking examples includes SellSenseAI, an advanced analytics system that uses machine learning, data of customers and their purchased financial products to predict what products a new customer is likely to purchase. Further, it identifies novel up-selling and cross-selling opportunities. This system integrates via APIs with banking apps and email marketing channels, resulting in 30% more purchases and 10% more revenue for the company.
Another AI-based technology, AccountAI, allows accurate payment behavior forecasting for the banking industry. AccountAI can preempt when the borrower fails to make a payment on time. Any action, sending a notice to the borrower and when the lender company can expect the late payment helps predict payment behavior. It enables the lender company to minimize intervention costs, improves lender-borrower behavior and permits accurate forecasts of profits and losses. AI is a compelling and extraordinary field. Not managing the credit process through a proper AI system can lead to bad debts within the organization. Introducing artificial intelligence in banking will allow banks and financial institutions to improve their growth rates, which may have fallen due to the credit risks involved. Introducing AI-based technologies such as creditbots and robo-advisors will benefit organizations. They will receive weekly reports regarding the credit processes and other banking responsibilities.
Artificial intelligence in the banking sector helps predict future outcomes and trends by analyzing past behavior. AI helps banks identify fraud, detect anti-money laundering patterns and make customer recommendations. As industries look towards digital transformation, artificial intelligence is changing the horizons of the banking sector while becoming an integral part of the process. There are many other industries that AI is being launched into, which is improving our economies as a whole.
Here at ARFASOFTECH, we have a highly skilled and dedicated AI team that can help you improve your banks' customer engagement, efficiency, accuracy and more. It's time to move on to the next stage and implement AI-based technologies into your systems to streamline your operations and substantially reduce your costs.