Enterprises in the financial sector are increasingly benefitting from the use of artificial intelligence (AI). It enables them to streamline costs, speed up processes, make better predictions and decisions, and improve customer experience. Here are some of the main ways AI is being deployed in finance.
One of the chief benefits for personal finance is the ability of AI to give customers greater control when it comes to managing their own money. The use of AI-enhanced chatbots, which are adept at natural language processing, gives customers the ability to access round the clock financial assistance, even offering personalised insights about financial management and appropriate products. AI can also be used to send personalised messages to customers, alerting them to new products, interest rate and price increases, suspected fraud on their accounts and any other information it considers relevant to their needs.
According to UK Finance, over £600 million was lost to fraud in the UK in the first 6 months of 2022 alone. Globally, online payment fraud is expected to reach £40 billion this year and will cost £300 billion over the next five years. In its fight to prevent fraud and satisfy the customers who are demanding greater protection against it, the finance sector is leading the way in the use of AI. The importance of AI is that it can analyse payment data to understand the patterns that identify fraudulent activity and then apply these in real-time to detect and halt more incidents of fraud. Every time a transaction is processed, its details can be analysed by AI to decide whether it is fraudulent or not. The addition of machine learning strengthens this as the algorithm can learn and adapt over time.
AI can also be used to prevent business fraud and money laundering. AI-enabled fraud detection tools can identify suspicious activity in business transactions, in some cases helping spot the individuals operating illegally.
Risk analysis is crucial in the financial sector. It enables financial institutions to judge loan and creditworthiness, decide individual lending rates and make judgements about the buying and selling of stocks and other investments. As a result, AI-enabled fintech can be used to cut financial risk and strengthen loan underwriting. With credit and loans, this happens through institutions using AI to analyse customer data to identify customers at risk of default. Additionally, AI can be used to predict how customers might be affected during the term of their loan by unforeseen circumstances, such as unemployment or a cost of living crisis.
Cutting costs and improving CX
AI can also be used for a range of other purposes that cut costs and improve customer experiences. For example, its ability to automate tasks reduces the demand on human resources, eradicates repetitive jobs and eliminates the potential for human error. Chatbots and other automated, customer-facing tools, meanwhile, provide customers with round the clock access to financial services, accessible from anywhere. This reduces the need to staff high-street banks and offices while providing better a customer experience for today’s technically savvy, convenience-demanding customers.
In the US and Canada, it is estimated that AI-enabled automation could cut costs by $70 billion over the next three years, while the use of other AI apps could take that figure closer to $450 billion.
Considerations for adopting AI
As younger tech natives begin to become the major consumer groups for financial institutions, it is becoming increasingly necessary to deliver the digital services and customer experiences that they demand. Almost 80% of younger adults expect that they will never need to visit a physical bank. These kinds of expectations have led to a significant increase in IT spending in the sector. In 2022, Banks alone spend £208 billion on IT and this is expected to rise to £256 billion by 2027.
AI has the ability to transform the financial sector, enabling companies to cut costs, minimise risk, reduce fraud and offer a wider range of advanced digital services for customers. However, while it is able to offer these benefits, financial enterprises also need to consider the infrastructure on which they are going to run AI applications and store the staggering amount of data which these apps need to process.
Critical digital services need infrastructure that guarantees 100% uptime and has the on-demand scalability to cope with significant spikes in traffic or the resources to carry out huge workloads. The capital investment and running costs required to achieve this in-house are considerable. It is a project that requires a fully equipped premises, physical hardware, physical and digital security, regular maintenance, insurance, heavy energy consumption and IT expertise.
Migrating to a managed cloud solution to run AI applications can eradicate capital expenditure and reduce running costs while delivering management and maintenance services, 100% uptime and scalability.
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