The rapid rise of Generative Artificial Intelligence (GenAI) in recent years has forced many industries to adapt and evolve at an astronomical rate. The financial services industry is no different: staying at the top of the game means embracing AI in order to boost productivity, customer engagement and risk management. According to Forbes, the money spent by the financial services industry on AI is expected to rise from $35 billion in 2023 to $97 billion in 2027.
But is this all positive change? Can AI really replace personal, human experience in the finance and banking sectors? Although Gen AI certainly has its benefits, it’s also important to consider the drawbacks. In this article, we’ll explore the pros and cons of GenAI in finance, as well as taking a closer look at the AI tools that the industry is embracing.
Many financial services companies have already begun to incorporate AI into the workings of their business or develop new AI tools to support different aspects of industry services. This is helping to achieve greater efficiency by streamlining processes and easing pressures on staff, as well as performing tasks currently beyond the capabilities of financial sector workers. There are 3 notable areas of developing AI that are beginning to transform the financial sector:
AI has the ability to revolutionise customer experience through personalisation. It can analyse customers’ financial behaviours, history, income, investment preferences and spending patterns to adapt financial advice to the individual. This means improved customer service through proactive recommendations, helping customers to make informed decisions based on their personal circumstances, and tailored financial products.
Chat bots are another AI service that offer the potential to provide hyper-personalised customer experiences. These bots have already had a huge impact on the online customer service landscape; they pop up in the corner of almost every website you visit, offering varying levels of customer service to users. Although they can’t solve every issue that a human could solve, these bots make customer service more accessible, offering 24/7 customer support and advice and performing extra tasks such as processing transactions and gathering data. These actions reduce the need for customer service staff and provide quick and efficient service to customers, enhancing their experience.
All of this works to bring AI assistants closer to being a predictive, rather than a reactive service: ultimately, the aim for banks should be to be able to predict customers’ needs, queries and issues early, reaching out to them with a solution before they even realise they need one. By helping customers to avoid issues, AI could enhance customer experience and improve operational efficiency.
There are many uses for and types of AI copilots. They can take care of the easy, mundane or repetitive tasks previously carried out by people, creating a more streamlined workflow and optimising efficiency in the workforce by automating manual processes. This allows staff to spend time on more complex tasks and reduces the risk of human error. It also allows staff to write reports or handle specific queries more accurately by accessing data quickly. By saving time and resources in this way, FS firms could see a huge improvement in productivity, customer satisfaction and even staff satisfaction.
These copilots can also be used by financial institutions to assess data from different sources to offer comprehensive insights into the financial markets. It is hoped that they will soon be able to accurately predict market trends using real-time insights, meaning these institutions will be able to always stay one step ahead of the game.
Web crawlers are bots that crawl the entire internet looking for and gathering information. The bots will start with the websites most relevant to the information they need, and navigate via hyperlinks in those websites to expand their search. Normally used by search engines, they can gather data from almost any website and store that data in an index, drawing on the relevant information from that index when a user searches for something.
The financial services sector can use these bots to pick up on financial news and log market movements in real time. Having them running permanently in the background to pick up on every shift in the financial landscape means that financial institutions can calculate risk, gain investment insights and make informed decisions based on live information being gathered by these bots.
Using permanent web crawlers could be revolutionary for banks and other financial institutions in terms of staying ahead of the competition and making the most informed decisions for the industry, with all the information they need at their fingertips. Web crawlers can also detect shifts in public opinion on social media, meaning the information provided is not only factually accurate, but can help banks to know their customers better too.
the information provided is not only factually accurate, but can help banks to know their customers better too.
Having explored how GenAI is changing the financial landscape, it’s clear to see that there are many benefits to adopting these AI developments for banks and their customers. Let’s take a closer look at how GenAI can be used to its full advantage in finance.
AI can be used for risk management in several different ways. One example is Fraud Detection: it can analyse transaction data and identify any suspicious or unusual patterns in user behaviour. By continuously monitoring user behaviour, fraud detection AI can fine-tune its understanding of user behaviour patterns and become hyper-alert to anomalies in these behaviours, flagging fraudulent activity.
Another way that AI can be used for risk management is Credit Risk Management. AI could look beyond the traditional data used to calculate credit risk (such as credit scores and financial statements) by accessing unstructured data, making the process faster, more accurate and more personalised to each customer.
AI makes it possible for customers to have round-the-clock access to customer service, without employing staff to cover customer service lines 24 hours a day. When staff do speak to customers, AI can give them easy access to more data and help to provide answers to questions, making the process faster and more accurate and ultimately improving customer satisfaction. AI can also analyse customer interactions to continuously learn from them and improve its service, meaning that customer engagement is becoming ever-smoother.
All of these factors mean that AI offers increased efficiency and lower costs for banks. Tasks that have previously been manual and time-consuming are being automated; this ultimately means that staff can focus on the more complex and important tasks, contributing more meaningfully to the running of the business whilst customers receive a more personalised and streamlined service.
Although here are many benefits to the use of AI, there are also a number of drawbacks to consider.
Arguably the most important of those drawbacks is the environmental impact of AI. It takes a huge amount of energy to power AI servers, data storage drivers and network equipment, and a huge amount of water to cool these servers. This means that we are draining natural resources to power each and every AI search, command and function.
The rapidly increasing use of AI means that this demand is only going to increase, with potentially catastrophic results on the already-fragile environment. It has been estimated that training ChatGPT-3 (a predecessor of ChatGPT) led to more than 550 tonnes of carbon dioxide being released into the atmosphere and over 2.5 million litres of water being used. The more we rely on AI in every sector, including finance, the worse this problem will become.
Although AI can improve customer service in many ways, it is reported that half of customers still prefer to talk to a real person over an AI chatbot. Talking to a real person can make a customer service experience feel more personal and more trustworthy, when information is being filtered through a person rather than drawn straight from a database. Humans can often understand commands and requests with human idiosyncrasies better than a bot, meaning that talking to a person can lead to a less frustrating experience for customers.
Half of customers still prefer to talk to a real person over an AI chatbot.
AI can also be more biased than a human, as it passes judgment based on the data available to it. This data may not always provide a completely objective view of, say, a person applying for a loan, as it fails to take into account other potentially mitigating factors outside of the digital records that AI accesses. Biases in AI training data may also affect its objectivity, meaning it may discriminate against certain demographics.
Data protection is another worry when using AI in finance. As we feed more and more personal information into AI, any breach of an AI database could risk exposing huge amounts of sensitive data. Regulations are constantly evolving to keep up with developments in AI, to make sure that customers feel as secure as possible when giving personal financial details to service providers, whilst simultaneously understanding the risks involved.
The rate at which Generative AI is transforming our world means the financial sector cannot afford to be left behind; the industry is embracing AI as a useful tool for improving customer service, staying on top of industry trends, and streamlining efficiency. However, the financial services industry needs to be wary of the potential negative ramifications of AI, and the impact these could not only have on customers, but the impact they are already having on the environment. Although AI is already widely used in finance, we will undoubtedly continue to witness major developments over the coming months and years as the world turns increasingly towards AI and its uses.
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