Artificial intelligence has become a critical disruptor in almost every industry, including banking and finance. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant.
AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Also, intelligent algorithms are able to spot fraudulent information in a matter of seconds.
A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits that AI presents to their sector. Another report suggests that by 2023, banks are projected to save $447 billion by using AI apps. These numbers indicate that the banking and finance sector is swiftly moving towards AI to improve efficiency, service, productivity, and reduce costs.
In this article, we will find out the key applications of AI in banking/finance sector and how this technology is redefining customer experience with its exceptional benefits.
Applications of AI in banking and finance
Artificial intelligence has become an integral part of the world we live in, and banks have already started integrating this technology into their products and services.
Here are some major AI applications in the banking industry through which you can reap the numerous benefits of the technology. So, let’s dive in!.
Cybersecurity and fraud detection
Every day, huge number of digital transactions take place as users pay bills, withdraw money, deposit checks, and do a lot more via apps or online accounts. Thus, there is an increasing need for the banking sector to ramp up its cybersecurity and fraud detection efforts.
This is when artificial intelligence in banking comes to play. AI can help banks improve the security of online finance, track the loopholes in their systems, and minimize risks. AI along with machine learning can easily identify fraudulent activities and alert customers as well as banks.
For instance, Danske Bank, Denmark’s largest bank, implemented a fraud detection algorithm to replace its old rules-based fraud detection system. This deep learning tool increased the bank’s fraud detection capability by 50% and reduced false positives by 60%. The system also automated a lot of crucial decisions while routing some cases to human analysts for further inspection.
AI can also help banks to manage cyber threats. In 2019, the financial sector accounted for 29% of all cyber attacks, making it the most-targeted industry. With the continuous monitoring capabilities of artificial intelligence in financial services, banks can respond to potential cyberattacks before they affect employees, customers, or internal systems.
Undoubtedly, chatbots are one of the best examples of practical applications of artificial intelligence in banking. Once deployed, they can work 24*7, unlike humans who have fixed working hours.
Additionally, they keep on learning about the usage pattern of a particular customer. It helps them understand the requirements of a user in an efficient manner.
By integrating chatbots into banking apps, the banks can ensure that they are available for their customers round the clock. Moreover, by understanding customer behavior, chatbots are able to offer personalized customer support and recommend suitable financial services and products accordingly.
One of the best examples of AI chatbot in banking apps is Erica, a virtual assistant from the Bank of America. This AI chatbot can handle tasks like credit card debt reduction and card security updates. Erica managed over 50 million client requests in 2019.
[Also Read: How much does it cost to develop a chatbot?]
Loan and credit decisions
Banks have started incorporating AI-based systems to make more informed, safer, and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit history, credit scores, and customer references to determine the creditworthiness of an individual or company.
However, one cannot deny that these credit reporting systems are often riddled with errors, missing real-world transaction history, and misclassifying creditors.
An AI-based loan and credit system can look into the behavior and patterns of customers with limited credit history to determine their creditworthiness. Also, the system sends warnings to banks about specific behaviors that may increase the chances of default. In short, such technologies are playing a key role in changing the future of consumer lending.
Tracking market trends
Artificial intelligence in financial services helps banks to process large volumes of data and predict the latest market trends, currencies, and stocks. Advanced machine learning techniques help evaluate market sentiments and suggest investment options.
AI for banking also suggests the best time to invest in stocks and warns when there is a potential risk. Due to its high data processing capacity, this emerging technology also helps speed up decision-making and makes trading convenient for both banks and their clients.
Data collection and analysis
Banking and finance institutions record millions of transactions every single day. Since the volume of information generated is enormous, its collection and registration turn into an overwhelming task for employees. Structuring and recording such a huge amount of data without any error becomes impossible.
In such scenarios, AI-based innovative solutions can help in efficient data collection and analysis. This, in turn, improves the overall user experience. The information can also be used for detecting fraud or making credit decisions.
Customers are constantly looking for a better experience and convenience. For example, ATMs were a success because customers could avail essential services of depositing and withdrawing money even when banks were closed.
This level of convenience has only inspired more innovation. Customers can now open bank accounts from the comfort of their homes using their smartphones.
Integrating artificial intelligence in banking and finance services will further enhance consumer experience and increase the level of convenience for users. AI technology reduces the time taken to record Know Your Customer (KYC) information and eliminates errors. Additionally, new products and financial offers can be released on time.
Eligibility for cases such as applying for a personal loan or credit gets automated using AI, which means clients can eliminate the hassle of going through the entire process manually. In addition, AI-based software can reduce approval times for facilities such as loan disbursement.
AI banking also helps to accurately capture client information to set up accounts without any error, ensuring a smooth experience for the customers.
External global factors such as currency fluctuations, natural disasters, or political unrest have serious impacts on banking and financial industries. During such volatile times, it’s crucial to take business decisions extra cautiously. AI-driven analytics can give a reasonably clear picture of what is to come and help you stay prepared and make timely decisions.
AI also helps find risky applications by evaluating the probability of a client failing to pay back a loan. It predicts this future behavior by analyzing past behavioral patterns and smartphone data.
Banking is one of the highly regulated sectors of the economy worldwide. Governments use their regulatory authority to ensure that banking customers are not using banks to perpetrate financial crimes and that banks have acceptable risk profiles to avoid large-scale defaults.
In most cases, banks maintain an internal compliance team to deal with these problems, but these processes take a lot more time and require huge investment when done manually. The compliance regulations are also subject to frequent change, and banks need to update their processes and workflows following these regulations constantly.
AI uses deep learning and NLP to read new compliance requirements for financial institutions and improve their decision-making process. Even though AI banking can’t replace a compliance analyst, it can make their operations faster and more efficient.
One of the most common use cases of AI includes general-purpose semantic and natural language applications and broadly applied predictive analytics. AI can detect specific patterns and correlations in the data, which traditional technology could not previously detect.
These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.
Robotic process automation (RPA) algorithms increase operational efficiency and accuracy and reduce costs by automating time-consuming repetitive tasks. This also allows users to focus on more complex processes requiring human involvement.
As of today, banking institutions successfully leverage RPA to boost transaction speed and increase efficiency. For example, JPMorgan Chase’s CoiN technology reviews documents and derives data from them much faster than humans can.