The numbers agree. The global AI in Fintech market is estimated to reach a value of US $26.67 billion by 2026 with a CAGR of 23.17%. AI is applying “human intelligence beyond human scale” to revolutionise fintech.
Core market trends
The power of AI will bring about significant growth in quantitative asset management. Some of the key market trends include:
AI will play a greater role in investment management,especially when it comes to evaluating opportunities, optimising portfolios and mitigating risk.
Robo-adviserswill continue to be a feature in the investment advisory service segment.
In financial record keeping,AI will be used in conjunction with blockchain and distributed ledger to create novel ways to record, track and store transactions.
Asset management companies will continue to adopt AI-based solutions to gain real-time actionable information and present clients with more astute portfolio management decisions.
Traditional fund management companies will continue to collaborate with fintechs and adopt AI-as-a-service as long as they enjoy benefits like streamlined processes and optimised investment decision-making.
The top applications of AI in fintech
AI applications are permeating every segment of fintech and experts believe we’re at the beginning of how this radical technology will revolutionise this field. Currently, these are some of thetop applications of AI in fintech:
Robo-advisers:Emerging as one of the most popular applications, robo-advisers are used in everything from personal financial planning to creating a savings goal. They also help keep customers engaged in the long-term by sending reminders of renewals, dividend management and transaction limit approaching.
Transactional search query: Using Natural Language Processing (NLP), AI tools can interpret the meaning of user queries, process information and display results. Users can look up any historical data for a specific time or transaction, avoiding the hassle of bank statements.
Client risk profile:AI tools are going beyond know-your-customer (KYC) and other identification processes to profile, assess risks and mitigate fraud. After customers are onboarded, AI solutions are being used to match and recommend financial products most suitable for them, based on their risk profile.
Customer churn prediction: AI tools look into customer behavior to predict which customers are showing signs of considering canceling policies or leaving. These tools are helping managers prioritise their list of clients and offering more tailored services to them.
Algorithmic trading: Trading stocks involves analysing vast amounts of data and making instantaneous decisions – things machine learning does much better than humans. AI tools can quickly identify patterns that would elude humans and automatically execute trades based on these insights. This allows traders to use real-time data to make more decisive short-term trades.
Finance executives and innovators need to build on their foundational knowledge of fintech to leverage what AI and open banking can offer. According to Dr. Rosowsky, a guest speaker of the Oxford AI in Finance and Open Banking Programme, “Advancements in AI technology are driving transformations in the global [financial] marketplace.”
If you’re ready to learn more about the applications of AI in fintech, join Dr. Rosowsky in our programme.