What is AI in finance?
The use of artificial intelligence in finance involves replacing or supplementing human knowledge and skills with computer systems that perform automated tasks, typically for the purpose of speeding up processes, enhancing output and productivity, reducing errors and increasing profitability for companies in the sector. Some common tasks that AI is increasingly taking over or assisting with include:
- Pattern recognition
- Problem-solving
- Decision-making
- Predicting market trends
- Investment management
- Customer service
- Risk management
- Handling fraud
- Data analysis
- Performance management
In the following section, we will explore some of these tasks in greater detail.
Functions AI can be used for in the finance sector
Let’s have a closer look at some of the core functions AI can perform in finance:
1. Algorithmic trading
Algorithmic trading is a union between artificial intelligence and the financial markets. This practice involves using machine-learning algorithms and predictive analytics to analyse market data, identify trends and make real-time trading decisions. Because machines can continuously learn and improve from new data, these models can increase their accuracy over time. Today, many UK investment firms and hedge funds use AI to optimise their trading strategies and gain better portfolio returns.
2. Handling fraud
AI systems can use pattern-recognition and anomaly-detection algorithms to scan transaction data and detect unusual activity that could be the result of fraud. These systems are also able to identify warning signs of emerging threats and prevent them, from simple hacks and scams, to identity theft, spoofing in trading, cyberattacks and even money laundering. The Financial Conduct Authority (FCA) advocates for the use of AI in detecting and fighting financial crimes, and UK banks and payment companies also rely on these functions to monitor large volumes of transaction data for signs of fraud.
3. Risk management
AI can significantly improve the effectiveness of risk management through its ability to analyse vast data sets and spot significant details or patterns almost instantaneously. By studying data like market trends, credit history, economic indicators and other relevant information, machine-learning models can identify potential risks and predict possible portfolio losses with impressive accuracy.
Related: Compliance and risk management: how they differ
4. Credit scores and loan approval
Traditionally, credit scoring or loan approvals relied on a handful of factors – primarily a person’s income and their existing credit history. The capabilities of AI allow for credit scores and lending to take a wider range of data into account. This can be helpful for customers who previously may have struggled to obtain credit or a loan, like school-leavers, graduates and immigrants to the UK.
5. Customer service
One of the most instantly recognisable functions of AI in the financial sector relates to customer service. Chatbots have been in use for many years to streamline communication with customers, not only in finance but in many other industries too. These systems can handle customer inquiries – either from start to finish or partially (before transferring the customer to a human agent) – offer financial advice and even process simple transactions. This can help to make customer service more efficient and cut down on the time human agents spend addressing very straightforward or common queries. These bots can also give customers bespoke recommendations for financial products and services, based on their online behaviour and the data the AI has about them.
Advantages of using AI in finance
The UK government’s national AI strategy actively advocates for an increasing use of AI to help boost economic growth and transform the way we work. Below, we examine some of the advantages of AI in finance.
- Increases efficiency: By automating a raft of typical tasks, AI can help companies streamline their operations, freeing up employees to concentrate on more complex processes and activities that require a human touch, including relationship-building.
- Boosts profits: AI can significantly reduce staffing needs, especially for lower-level tasks and customer-service departments. AI’s fraud-detection capabilities can minimise the risk of your company losing money to bad actors, either directly or indirectly through customers falling victim to scams or phishing. Finally, if your company is in the investment sector, AI can help maximise your returns through its quick and precise decision-making that can predict market movements and execute trades at opportune times.
- Fine-tunes decision-making: Because AI is able to process extraordinary amounts of data very quickly and accurately, and detect patterns a human eye could easily miss, it can make it easier for your company to make informed decisions regarding trading, lending, investment, risk modelling, compliance management and much more.
- Enhances innovation and scalability: As AI systems become more sophisticated and self-improve with human feedback, they can handle increasing amounts of data and transactions. This can enable your company to scale without the additional costs traditionally associated with rapid growth. Its ability to identify trends and track both the markets and your competitors can help you to innovate and gain a competitive advantage.
Potential pitfalls and challenges
While AI offers many advantages in the finance industry, it also comes with its own set of pitfalls and challenges. Here are a few key issues to look out for:
- Data protection and compliance: Different jurisdictions have different rules for handling data. This makes it important to ensure that your AI models are compliant with all UK regulations and UK GDPR. Beyond data protection, maintaining regulatory compliance within your models is crucial to avoid potential legal issues.
- Algorithmic bias: AI models can still be prone to bias at times, if data they are trained on is biased. This can potentially result in discriminatory outcomes, particularly in areas such as credit or loan approvals. Regular audits and close evaluations and monitoring of your models can help mitigate this risk.
- Start-up costs: Implementing advanced AI systems in the finance sector can come at a high cost. The sophisticated technology required to navigate such a complex and sensitive industry typically carries a significant price tag.
- Loss of human touch: AI can provoke mixed reactions from the public, whether consumers, employees or potential employees. Your current workforce may need reassurance that their jobs are secure, or support and training if their roles are going to change significantly. Equally, customers can get frustrated with chatbots and Interactive Voice Response (IVR) systems if they struggle to reach a human representative when needed.
Related: Why is customer satisfaction important?
AI implementation tips
We believe in responsible AI. Here are a few tips to consider when implementing new AI systems at your finance company.
- Clearly define your objectives and goals before implementing new technology
- Invest in high-quality data for training your AI
- Establish guidelines for ethical AI use
- Recruit data scientists and/or AI experts to assist you
- Continually monitor your models, update them when necessary and regularly train them with fresh data
The growing use of AI in finance indicates a shift in the industry’s approach to productivity, decision-making, customer service and more. While it has the potential to reshape the financial industry, it is important to be aware of the potential challenges and risks when you decide to adopt new AI models and to proceed at a measured and informed pace.
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