Rise Of AI In Finance: Meaning, Applications, and Business Benefits Explained

What is AI in finance?

Artificial intelligence (AI) is an area of computer science that enables machines to perform advanced tasks just like humans. With AI, machines can organise, interpret, and analyse data, and make recommendations and predictions based on this assessment. For example, an AI-powered computers can access, understand, and translate speech and/or text. What's more, they can mimic another human ability – learning and improving on their own.

The use of AI in banking and finance allows organisations to use the large datasets at their disposal to offer a wide range of personalised products and services of the highest quality as well as to simplify and speed up payment processing and other core financial services, all while prioritising security and compliance. With finance AI, banks and fintechs gain the ability to understand market conditions, make accurate predictions, offer dedicated service to their customers, and improve decision-making with the help of data analytics.

Finance AI comprises a set of technologies, many of which we might already be familiar with. These include:

Machine learning

Machine learning is a technology that uses data and algorithms to enable machines to learn and improve – much like humans – without being programmed to do so. How does it work? Machine learning algorithms make a prediction based on data inputs. There are three ways in which they do this:

• Supervised machine learning: In this model, algorithms classify data, predict outcomes, or recognise patterns using labelled datasets (tagging email data as spam is an example of data labelling). Supervised machine learning aids predictive analytics (forecasting future outcomes) and is used across industries – from healthcare and marketing to, yes, financial services.
• Unsupervised machine learning: In this model, algorithms assess unlabelled datasets to identify similarities and differences and compile them into clusters. Unsupervised machine learning is useful in segmenting customers into relevant clusters.  
• Semi-supervised machine learning: A combination of the two previous models, semi-supervised machine learning uses a small labelled dataset alongside a larger unlabelled dataset to achieve accurate classification and prediction. Semi-supervised machine learning is useful when a company doesn’t have the resources for labelled datasets, which is expensive and takes time and labour to compile.

Machine learning in finance services a key problem area for banks and other financial institutions – fraud prevention. With the sheer volume of sensitive data they handle, these institutions use machine learning to scan data and pinpoint anomalies and red flags that present a security threat. Other applications of machine learning in finance include risk analysis, data management, and loan underwriting (gauging a customer’s creditworthiness before approving a loan or insurance policy).  

Generative AI

Generative AI – also called gen AI – is artificial intelligence that can create new content such as text, image, video, and audio. Think ChatGPT. Generative AI works on deep learning – a sub-set of machine learning that trains algorithms to impersonate the human brain and recognise patterns in words, images, sounds, and other data types. With generative AI, one can create articles, images, invoices and even workflows. Some common applications of generative AI in finance include virtual assistants, face recognition systems, and fraud detection tools. Generative AI is transforming the financial industry by enabling the creation of new content and automating complex tasks.

Conversational AI

Conversational AI is used to make machines interact and talk with humans. Think of a bank chatbot that answers users' queries about account details, provides personalised financial advice, and offers product recommendations based on spending habits.

Natural language processing

Both generative AI and conversational AI use natural language processing (NLP), which is a form of machine learning. Natural language processing gives machines the ability to comprehend, interpret, and analyse human language. Using information held in data resources such as emails, texts, audio and video files, and social media feed, NLP can recognise speech and translate it. It can also assess the intent and sentiment in the data source and use it to provide an accurate response in real time. Banks and financial service providers use NLP to run chatbots, answer queries, analyse feedback from customers, and process large documents. For example, a fintech might use NLP to identify certain keywords on customers’ emails and use the insights gained to offer personalised financial services.

Computer vision

Computer vision is a type of AI that enables computers to identify and understand people and objects as seen in images and videos. In this way, it replicates the way humans see and comprehend things. Computer vision can be used to verify signatures, authenticate biometrics, and assess and validate documents. It is also useful in fraud prevention as it catches irregularities that might be indicative of fraudulent activity. Many online payment service providers use computer vision to bolster their fraud detection systems.

Key applications of AI in finance

1. Personalised financial services

With AI finance, companies attain the power to conduct accurate and in-depth analysis of customer behaviour and organise customers into groups. This paves the way for personalised interactions and recommendations and targeted marketing, which enhances customer satisfaction and loyalty. A personalised recommendation could be a special loan offer or targeted investment advice aimed at a customer group based on their journey with the company, their financial goals, and their risk tolerance. Or, it could be a finance AI chatbot capable of intelligent, human-like interactions that can answer customers’ queries and guide them through various services.

2. Customer service

Personalised services naturally translate to a happier user experience and greater engagement. The use of artificial intelligence in banking, for instance, allows people to use facial recognition or voice command tools to log in to their accounts anytime and anywhere. Such convenience matters to younger customers, the majority of whom prefer digital banking channels over visiting the branch. The use of AI in finance has created other conveniences such as round-the-clock support, ensuring that customers get the help they need when they need it and reducing wait times. What’s more, today’s intelligent virtual assistants do much more than offer basic information. They boast a wide variety of services, further enhancing the customer journey and helping companies build trust.

3. Fraud detection and prevention

Global losses from online payment fraud are predicted to exceed USD 362 billion between 2023 and 2028, according to new research. As online scams get more creative – using strategies like phishing, account takeover, and email compromise – financial institutions need best-in-class security systems. Integrating AI into fraud detection mechanisms adds layers of security that are missing in traditional methods. By using machine learning in finance, companies can conveniently automate tasks like detecting threats, identifying compromised data, and exposing hidden attackers. With the power of automation, companies can spot threats and suspicious activities almost immediately, allowing them to stop unauthorised transactions and prevent losses before they happen. Furthermore, finance AI tools such as facial recognition and biometric authentication are useful in accurately verifying the identities of customers, which helps secure their personal information and prevents fraud. Another use of AI in banking and finance is that it makes regular monitoring and analysis of data traffic possible. This too helps detect, prevent, and respond to cyber attacks and risks.

4. Risk management

Generative AI and machine learning have greatly improved risk management in the finance sector. For financial service platforms, there are many applications of machine learning in risk management. Give that machine learning makes it possible to analyse large datasets, identify patterns, and predict outcomes, it is useful in forecasting market trends and anticipating potential financial threats. Meanwhile, using generative AI finance in credit risk management has improved credit scoring accuracy by laying the groundwork for customers to be assessed on extensive data points, including non-traditional data sources such as social media feed and mobile phone usage. Accurate credit scores lower chances of default and minimise credit risk, which has another beneficial outcome. It encourages financial institutions to expand their services to a wider market, reaching previously underserved sections such as small businesses. Another use of generative AI in finance is as a virtual expert and enforcer of company policy, regulations, and guidelines. 

5. Automation

In the not-too-distant past, most finance companies had their junior staff take care of data entry, document processing, customer service, and other repetitive work. But with the advent of AI in finance, most of these monotonous tasks have been automated. There are finance AI tools that can generate reports, answer customer inquiries, analyse and research, offer financial advice, and a lot more. Automation reduces task execution time, eliminates human error, improves data accuracy, and frees up staff so they can focus on more complex work that generates greater value for the company. Intelligent automation that runs on AI has the added benefit of reducing costs. By taking over time and labour-intensive tasks, it lowers the cost per transaction for each automated task. It also encourages informed, data-driven decision-making in real time, which is directly linked to revenue growth. While it is standard practice to automate basic tasks, moving forward, companies will increasingly harness the power of AI to automate complex functions (such as risk analysis). For example, the role of artificial intelligence in banking has shifted from performing simple data entry and document verification tasks to automating complex customer onboarding and compliance check (such as KYC) functions.

6. Predictive analytics

With AI finance, the future is no longer an unknown entity. Predictive analytics is a sub-set of artificial intelligence that processes large datasets and makes assumptions and predictions about future events with unprecedented precision. Financial institutions often use predictive analytics to forecast sales, revenue, risks, and stock prices. It helps them anticipate and prepare for shifts in market and economic conditions and changes in consumer behaviour. With the foresight gained, they can then predict customers’ requirements, plan targeted marketing campaigns, and invest in product development and market expansion to take advantage of opportunities. Additionally, predictive analytics helps institutions avoid risky moves, prevent fraud, and make contingency plans for periods of uncertainty. Deploying predictive analytics and AI in accounting and finance is the most effective way of predicting the organisation’s future health. A company’s financial statements and historical data hold a wealth of information and insights that predictive analytics uses to project future earnings and expenses, paving the way for intelligent decision-making.

7. Financial management

In all organisations, finance teams shoulders the burden of budgeting, spending, invoicing, policy development, and more. Without AI, these teams would have to micro-manage every single bill, transaction, and expense. But the growing relevance of AI in accounting and finance has eased their burden to a great extent. How? One, AI has redefined personalised financial management, allowing customers to transact without having to visit the bank. Two, financial managers are using AI-powered credit scoring to identify responsible borrowers, which means they have fewer worries about bad debt and defaulters. That's not all. There is a finance AI tool for just about every financial management activity. Finance AI automates repetitive tasks (data entry), processes and analyses financial data, makes predictions and forecasts based on data insights, predicts stock prices and revenue growth, spots fraudulent transactions and cyber attacks, and much more. AI for finance is augmenting financial management and making life easier for finance teams across industries.

Finance AI tools and their benefits

How AI finance tools can benefit your business

Here are eight rewards of using AI in banking and finance:

• Error reduction: Finance AI tools use automation and advanced algorithms to significantly reduce errors made by humans while processing documents, making data entries, and performing other manual tasks.
• Accuracy and consistency: Finance AI uses standardised methods to process and analyse data, delivering consistent and reliable outcomes as a result. The deployment of AI in banking and finance has improved forecasting and planning efficiency, resulting in highly accurate predictions of market trends, customer behaviour, and potential risks.
• Time and cost savings: Artificial intelligence completes tasks faster than humans, speeding up processes. It is also capable of finding patterns in large datasets that humans might miss. This contributes to quicker decision-making and actions. Furthermore, the automation of key processes such as data collection, entry, and processing lowers labour costs. With the application of machine learning and natural language processing, accounting systems can instantly create, upload, and validate invoices, capture data from documents, and do much more without human intervention, leaving employees free to pursue more pressing business.
• Workflow optimisation: AI in finance optimises workflows by automating repetitive tasks, improving forecasting, speeding up decision-making, identifying inefficiencies and bottlenecks, and streamlining processes from end to end.
• Security boost: Finance AI tools monitor transactions, spot suspicious activity and interactions, detect potential threats, and send alerts in real time, allowing financial institutions to proactively prevent fraud from it happens rather than reactively finding a solution for it later. What’s even more impressive is that these AI tools are continuously learning and adapting to new risks and threat patterns, making them a powerful buffer against hackers and malicious parties.
• Innovation: Finance AI is driving innovation in financial services. Its ability to analyse huge datasets and find hidden patterns and insights has led to a rush in creative products and services, helping companies stay relevant and competitive.
• Availability: Unlike humans who get tired or burned out and need regular time away from work, AI never sleeps. These tools are available any time and anywhere. A finance AI chatbot, for example, helps customers transact, find solutions, and manage their finances without time and place constraints.
• Accounting API integrations: AI finance has energised accounting systems by automating time-consuming tasks, streamlining processes, speeding up data analysis and reporting, and improving auditing. The seamless, cohesive system created by the integration of multiple accounting APIs is further enhanced with the application of AI in finance and accounting. With an AI-powered accounting system, businesses can boost decision-making through data-driven insights and improve cashflow and financial management.

Key considerations

While the benefits of AI for finance are many, some challenges remain:

Bias

There is a possibility of finance AI tools having some inherent bias, reflecting the biases present in the training datasets used. For example, an AI tool for recruitment might have a hidden gender bias. It is up to the companies using the AI to have a monitoring system in place to check for biases and correct them to ensure their processes are fair and equal.

Regulatory compliance

Large data volumes are a given when using AI in finance, which means that maintaining data privacy and security are of paramount importance. Financial institutions must ensure their AI tools comply with relevant laws and regulations, both legal and ethical. This is no small task because AI regulations vary across regions. Formal collaborations with regulators will help keep a lid on violations and ensure accountability.

Heavy investment

The deployment of AI in banking and finance requires heavy investment in technology, infrastructure, and skilled manpower. In the backdrop of a severe shortage of professionals trained in AI skills in Singapore and around the world, financial institutions might need to reskill staff or make new hires, adding to their expenses.

Finance automation with Aspire

Optimise your financial management with Aspire’s finance AI offerings. With Aspire, you can scan and upload your invoices instantly, detect anomalies in employee spend in real time, and make smarter decisions with comprehensive cashflow analytics and spend insights. And yes, all these AI features come with robust security.