The top 5 benefits of AI in banking and finance
The strategic deployment of AI in banking and finance can bring substantial benefits. Learn about how AI tools are transforming financial services and the risks to be mindful of.
The rationale for this is understandable. From improving staff and customer experiences to boosting back-office processes, banks may benefit from the strategic application of AI’s many technologies, such as machine learning, natural language processing, and computer vision.
The cost savings associated with the usage of AI can be substantial. Using AI-based solutions, according to Accenture, “banks can achieve a 2-5X increase in the volume of interactions or transactions with the same staff.”
According to Martha Bennett, a principal analyst at Forrester Research who specializes in emerging technologies, financial institutions have a head start in leveraging AI. “One of the things AI requires is a large amount of data, which banks have in abundance.”
The application of artificial intelligence (AI) and machine learning in financial services is not new. For a long time, payment companies have used machine learning to detect and prevent fraudulent transactions, according to Bennett. As processing power and storage have improved, real-time detection has become more common.
What are the top benefits of AI in banking?
What follows is a list of the top benefits of AI in banking and finance today and a discussion of some of the risks and challenges financial services companies face when using AI.
1. Reduction in operational costs and risk
Although the banking business is primarily digital, it is nevertheless littered with human-based operations that are occasionally paper-intensive. Due to the possibility of human error, banks confront major operational costs and risk challenges in these procedures.
In banking, robotic process automation (RPA) is being used to reduce much of the time-consuming and error-prone work involved in inputting client data from contracts, forms, and other sources. RPA is software that mimics rules-based digital processes performed by humans.
RPA bots become sophisticated process automation tools that can perform an expanding wide range of banking activities traditionally handled by people when combined with enhanced handwriting recognition, natural language processing, and other AI technologies. The advantages of merging AI with RPA are detailed in this definition of hyper-automation.
2. Improved customer experience
There’s a reason why banking hours were mocked. Banks never appeared to be open when you needed them the most, such as late at night or on weekends and holidays. Long wait times were common in call centers in the past, and when operators were finally engaged, they often couldn’t handle the customer’s issue.
Artificial intelligence (AI) is changing that.
Chatbots on-call: The usage of conversational assistants or chatbots is one of the major advantages of AI in banking. Unlike an employee, a chatbot is available 24 hours a day, seven days a week, and clients are more comfortable using this software program to answer inquiries and complete many typical banking procedures that traditionally required face-to-face interaction.
Upselling: Banks are getting better at deploying chatbots to make their consumers aware of extra services and offerings, in addition to handling customer care inquiries and talks about individual transactions.
3. Improved fraud detection and regulatory compliance
Fraud detection: Machines are “really superior to people” when it comes to fraud detection.
Regulatory compliance: Banking is one of the most heavily regulated industries in the United States and around the world. Governments utilize regulatory authorities to ensure that banks have appropriate risk profiles in order to avoid large-scale defaults, as well as to ensure that banking customers do not use banks to commit financial crimes. As a result, banks must adhere to a slew of regulations that require them to know their customers, protect their privacy, monitor wire transactions, prevent money laundering and other forms of fraud, and so on.
If banking regulatory compliance is not followed, it comes at a considerable expense and even carries a higher risk. As a result, banks are turning to artificial intelligence (AI) virtual assistants to monitor transactions, track consumer behavior, and audit and log data into various compliance and regulatory systems.
As previously mentioned, big-data-enhanced fraud prevention has had a substantial impact on credit card procedures, as well as sectors like loan underwriting, as explained below. AI-based technologies allow banks to practice proactive regulatory compliance while decreasing total risk by looking at client behavior and patterns rather than specific rules.
4. Improved loan and credit decisioning
Banks, meanwhile, are employing AI-based technologies to assist them in making better educated, safer, and profitable lending and credit choices. To evaluate whether or not an individual or firm is creditworthy, many banks still rely on credit ratings, credit histories, customer references, and financial transactions.
However, as many people will attest, credit reporting systems are far from ideal, and they are frequently filled with inaccuracies, omitting real-world transaction histories, and incorrectly classifying creditors. AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to see if a customer with low credit history would be a good credit customer or uncover customers whose patterns might raise the chance of default.
The biggest concern with employing AI-based systems for loan and credit decisions is that they can suffer from bias-related issues comparable to those experienced by humans, which is detailed further down under “AI risks and challenges.” This is related to the way AI models for lending decision-making are trained. To circumvent these issues, banks that want to apply machine learning in real-world, in-production systems must aim to eliminate prejudice and incorporate ethics training into their AI training procedures.
5. Automation of the investment process
Finally, some banks are digging deeper into AI by incorporating smart algorithms into their investment decision-making and investment banking research. AI systems are scouring the markets for untapped investment opportunities and informing algorithmic trading systems at companies like UBS in Switzerland and ING in the Netherlands. While humans are still involved in all of these investment decisions, AI systems are revealing new possibilities through improved modeling and discovery.
Furthermore, a growing number of financial services firms are providing Robo-advisers to assist their consumers with portfolio management. These Robo-advisers can deliver high-quality financial advice and be available whenever the customer wants them because of personalization, chatbots, and customer-specific algorithms.