Artificial intelligence: the future of banking & finance
18 Apr 2023
AI in banking: all about artificial intelligence in finance
Artificial intelligence, or AI, is transforming many industries, including banking and finance. If you’ve ever sought the solution to your problem through an automated chatbot or received a highly relevant or timely recommendation for a product you really needed, chances are that you have experienced the wonders of artificial intelligence.
But beyond retail banking and customer service, there’s more – a lot of artificial intelligence (AI) in banking and the financial industry actually goes unseen and runs in the background, benefiting the back and middle offices. It’s surprising how many tasks no longer require human management, and instead can be programmed for intelligent machines to take over. AI typically comes hand in hand with machine learning (ML). AI is when the computer is able to independently “think” like a human and perform tasks, while ML is when it “learns” enough from past data or trends to carry out specific tasks and/or predict future activities.
Some examples of artificial intelligence (AI) in banking include fraud detection, risk assessment, portfolio management, algorithmic trading, credit scoring, and financial planning. It is also often used to power marketing, sales and operations decisions and processes.
Applications of artificial intelligence (AI) in banking and finance
Here’s a quick rundown on the real-life applications of artificial intelligence (AI) in banking and finance:
Fraud detection: AI algorithms are used to detect and prevent fraudulent transactions.
Risk assessment: AI is used to analyse large amounts of data to assess credit and loan risks.
Algorithmic trading: AI algorithms are used for high-frequency trading, risk management, and portfolio personalized.
Credit scoring: AI is used to analyse vast amounts of data to assess creditworthiness and make loan decisions.
Portfolio management: AI-powered investment advice and portfolio management services.
Operations: AI helps automate repetitive tasks and improve efficiency in areas such as back-office processing and compliance.
Financial planning: AI-powered financial advisors provide personalized investment advice.
Marketing and sales: AI-driven personalized recommendations help banks cross-sell products.
Customer experience and service: AI-powered chatbots provide 24/7 customer support and handle basic queries.
Fun fact: This list was actually generated using the artificial intelligence (AI) tool ChatGPT!
Benefits of AI for customers and banks
Generally, with artificial intelligence (AI), there can be faster and more accurate decision making, as well as enhanced security – a key pillar when it comes to banking. This is thanks to modern machines’ ability to process large amounts of data, and “learn” from it. They are able to identify trends and make similar decisions as needed, and detect when unusual activity is spotted. We are also able to enjoy enhanced customer service through more personalized financial services, and increased convenience through automated tasks.
How artificial intelligence is disrupting banking & finance industries
According to a 2022 report by Business Insider1, more than half of financial services companies studied have implemented artificial intelligence (AI) in risk management (56%) and revenue generation through new products and processes (52%). The same report references other studies that found similar trends, such as that many large banks with over USD 100 billion in assets are currently implementing AI strategies.
It is no surprise that banks and financial institutions are eager to adopt AI solutions. As seen in the use cases above, AI improves the speed, accuracy, and efficiency of many banking processes while reducing costs and improving the customer experience.
In particular, these are three examples of how AI is most significantly disrupting the industry.
Using AI chatbots to improve customer service
The most visible way artificial intelligence (AI) has disrupted banking is through the use of chatbots in the frontline. Currently, you can chat with an AI to get solutions to simple queries and problems. You can also perform straightforward tasks. For example, with the DBS Digibot, you can retrieve your transaction history and account information anytime, and even get your loans approved immediately (if eligible). DBS is not the only bank to implement this – in fact, chatbots are practically an industry standard today.
Algorithmic trading with AI for asset and wealth managers
Traditionally, we used to rely on experienced asset and wealth managers to manage the portfolios of wealthy businesses and individuals. What artificial intelligence (AI) brings to the table is an added layer of accuracy and security that is challenging to achieve with humans only.
Today, there are AI-based applications that can help with portfolio management – through pattern recognition – by recommending which stocks are suitable for you. Paired with machine learning (ML), it can assess things like your current financial health, your risk tolerance, and the historical performance of stocks. The magic of this is that with time, the recommendations get increasingly accurate as the AI continues “learning”.
There are even fully automated roboadvisors for beginner investors who do not have wealth managers. The DBS digiPortfolio uses a hybrid model that is powered by AI yet leverages our team of portfolio managers.
How artificial intelligence can solve money laundering through fraud detection
But perhaps one of the biggest ways artificial intelligence (AI) is disrupting the industry is through enhancing security. Traditional anti-money laundering (AML) systems can get outdated very quickly.
This is where AI comes in – it can detect suspicious activity through analysing and predicting what is considered usual behaviour. Additionally, specialised AI tools can automate the fraud reporting process, which is typically a very manual, repetitive and inefficient task.
At DBS, we focus on “transaction surveillence” through a rule-based system, whereby we use AI and ML to screen transaction data and flag out those that do not match expected conditions. Additionally, we have trained the system to issue a numerically calculated probability score that indicates the level of suspicion so as to further improve efficiency. Before a report is made, the flagged cases are reviewed by human analysts to act upon.
The impact of such technology is profound – according to estimates by the United Nations Office on Drugs and Crime (UNODC)2, money laundering activity accounts for between 2% to 5% of the global GDP or USD 800 billion to USD 2 trillion.
How DBS is using artificial intelligence (AI) to transform banking
DBS believes in the potential of artificial intelligence (AI) as a cutting edge, emerging technology with the potential to transform banking all over the world. DBS started our digital transformation in 2014 with a vision to be digital to the core. Today, we see ourselves as less like a bank and more like a tech company, and are proud to be recognised as a global leader in digital transformation.
Using over 100 AI and machine learning (ML) algorithms, we have created what we call ‘Intelligent Banking’ to serve over 5 million retail and wealth customers across our markets in the region. This is done through some 45 million monthly hyper-personalised communications, or nudges, that can offer bespoke product recommendations and more.
Of course, harnessing the capabilities of AI comes with a huge responsibility to safeguard our customers’ interests and data. DBS takes this seriously and will continue to leverage the technology to further enhance security as well.
Our AI-powered platform enables safer money management by alerting customers to unusual transactions and payments, and any other suspicious activity.
Future of artificial intelligence (AI) in banking for DBS
We are continuously working to improve our artificial intelligence (AI) capabilities at DBS. Currently, we hope to go beyond studying transactional data to include behavioural and location data in our analyses. In future, we may be able to better serve niche customer segments, such as pet owners, parents, avid shoppers and more.
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