AI's growth in recent years has been unprecedented. While it might have been popularised by the emergence of ChatGPT, a large language model, a few years ago, its reach across the business landscape is remarkable. One major sector this innovation has disrupted is financial technology, or fintech.
With the finance industry in dire need of automation beyond APIs, mobile apps, and web-based services, AI has helped to expedite this digital transformation. From enabling the institutions to make data-driven decisions to enabling the lenders to calculate risk and forecast future conditions, AI-enhanced fintech has been a net positive. Even better, government initiatives such as the EU's Digital Finance Strategy have significantly promoted AI innovation in fintech. Here's more on how AI has transformed fintech.
Algorithmic forecast on market trends
AI's ability to analyse unstructured data through machine learning and extract meaningful insight is well-documented. However, in recent years, we've seen the enterprises scale up AI's use in pattern recognition to analyse the stock price fluctuations or the implications of economic policies.
For instance, advanced neural networks associated with generative AI can provide actionable insight on market movements that traditional analytic methods normally miss.
Today, the use of AI trading apps is already commonplace due to the technology's ability to identify complex non-linear patterns and execute trades without emotional bias. However, the future of AI on forecasting the market trades will go beyond identifying patterns - from trading in multiple markets to analysing social media sentiment and its impact on market trends.
AI-driven predictive analytics can also be utilised by financial institutions to identify possible revenue opportunities and potential risks. For instance, sales teams can use AI forecasts to set quotas. This allows them to create reachable but challenging goals for the team.
Besides, AI has simplified the setting pricing strategies based on price elasticity insights, among other marketing strategies. With an accurate market forecast, institutions can allocate resources efficiently and reduce costs, enhancing business growth.
Optimised personal finance services
Integrating AI and machine learning tools into personal finance has been a game-changer in financial management. When it comes to adding a personal touch, AI has already had a massive impact on other industries, such as online casinos. In countries like the UK, have a list of different casino bonuses, from which you can choose according to your preferences and behaviour.
AI is now more than just a number-crunching tool. Generative AI has allowed the creation of AI assistants who can offer optimised financial strategies.
Today, banks are incorporating AI apps that analyse consumer spending habits and investment preferences before suggesting saving and budgeting strategies. For instance, AI chatbots such as Cleo and Monarch Money track users' daily incomes and expenses before tailoring a comprehensive budget plan that aligns with their long-term financial plans. Others, such as Tally, help to automate a bill payment, help users to avoid double payments, and identify duplicate invoices.
With these tools, customers no longer make the financial decisions in the dark. They are empowered with real-time personalised recommendations on money management.
Prevention of fraud
Recent 2024 reports have shown the extent to which cybercriminals have been leveraging AI to defraud. From deepfakes of Elon Musk enticing people to buy fraudulent crypto to criminals using a voice cloning scheme to defraud Arup over $25 million, AI-powered fraud has been on the rise. What's more, AI-driven fraud losses in the US are expected to hit over $40 billion by the end of 2027.
Fortunately for financial organisations, developers are trying to fight fire with fire by leveraging advanced AI tools to detect and counter these AI scams. One major development, especially in banking, involves behavioural profiling, where AI creates detailed customer profiles based on their interactions. This could be typing speed, device usage or even session patterns.
Behavioural profiling can be used to supplement static identifiers such as passwords. Better yet, it can help flag the inconsistencies in deepfakes and synthetic identities.
For instance, UK-based neobank Revolut recently built an AI protection feature that determines if a customer's card payment is legit or part of a scam. Other recent notable upgrades on AI include Mastercard's Decision Intelligence Pro and Visa's Account Attack Intelligence (VAAI) Score. These transaction risk tools identify the likelihood of a transaction being fraudulent.
The future is AI
The use of AI to forecast goes across the board – from the fashion industry to healthcare and finance. Its advancement in fintech is revolutionising customer interaction and fraud detection, which are critical aspects in this sector. More so, generative AI in banking has been a game changer. Its adoption rate among consumers has been overwhelming — over 36% of consumers use Gen AI to manage their finances, while over 48.2% of financial institutions are already investing in AI research.
It's still early, but AI promises to trigger a massive shift within the fintech landscape.
