There is a newly maturing market for services that relate to AI in banking, which allows for a larger number of much higher-complexity solutions which can help to improve the return on investment for different services.trong
A majority of financial services companies are utilising AI for investment banking, as well as for fraud detection in banking. They have implemented the technology in a number of different areas of their business, such as the management of risk and generation of revenue through new processes and products.
According to the Cambridge Centre for Alternative Finance and the World Economic Forum, almost every bank is highly aware of the potential benefits presented by AI and machine learning. There is already a number of opportunites that are presenting themselves for banks and other institutions to implement these benefits by engaging with the technology
The vast majority of banks with over $100 billion in assets have said, according to studies, that they are in the process of, or have already, utilised a number of AI in banking strategies, while a large number of banks with less than $100 billion in assets are finding solutions for AI in banking and machine learning in banking.
There are a number of reasons why banks are utilising AI, not least of which is the front-facing ability to make their customer service a 24/7 experience through AI algorithms and machine learning that reduce the need for manual support during later hours.
A report from Business Insider has said that costs can be slashed by using AI in banking, as front and middle office positions, such as customer relations and the detection and prevention of payments fraud. AI banking can help to engage better in the stopping of money laundering through your bank and can also help with risk assessment protocols.
There is a need for a comprehensive AI internet banking strategy that utilises all of the data of the bank to monitor business lines, usable data, partnerships with external partners, and a number of other metrics necessary.
Fraud detection in banking is also an issue of major concern, and AIDock is more than capable of engaging the operational processes available to assist when it comes to finding fraudulent behaviors from customers. Full-service banks have always struggled with transaction monitoring, and they have historically found a number of false positives, where genuine transactions are flagged as suspicious. AI banking can play a critical role in addressing this historical issue by helping banks move from rule-based analysis to more risk-based assessments through the uses of machine learning algorithms and various AI in banking.
As a rising number of banking transactions are taking place remotely, new forms of fraud have emerged as possibilities for criminals. AI in banking and machine learning in banking can help to prevent by means of identifying fraudulent behaviour. This can be done by analysing customer behaviour in real-time and determining what activity doesn’t track with the standard behaviour of a customer. The accuracy based on machine learning in banking means that these cases are much easier to spot and the integrity of banking practices can be maintained.
AI functionality in front-facing roles such as customer service and mobile apps is constantly growing to be more advanced and proactive. Banks are now reportedly generating 66% more revenue from mobile banking users than when customers visit branches, with certain functionalities such as implementation of services like Siri.
Chatbots are a great AI-enabled software function that allows for the use of bots to converse with customers and provide top-of-the-range services. Bots, a very popular result of machine learning in banking, communicate with thousands of customers on the bank’s behalf all while cutting the costs of customer service by gigantic numbers. It has been reported that chatbots account for timesaving of four minutes per customer, which means they are more likely to utilise the app effectively and recommend the bank to others for natural growth.
It has been estimated in reports that AI in banking can, if utilised correctly, cut costs by as much as 30%. It depends on how and when strategies are used, but there are opportunities to improve the onboarding experience for customers and make life easier for employees, once the correct strategy is used.