How far has AI technology come in terms of usage in the financial sector?
As a result of some key shifts in the market, over the past 20 years, AI adoption in financial services has increased dramatically. The technology itself has come a long way, not only in its ability to process large quantities of data faster and more accurately but also in how inexpensive it has become to implement.
A key driver behind this has been the amount of data that businesses today have access to – not only do they need AI solutions that are able to derive meaningful insights from this data, but they also need solutions that are able to process it effectively.
What do these technologies mean for the growing fintech sector?
Until recently, fintech was viewed by many in financial services as disruptive to current operations and processes. While this perception is still held by some, most businesses have come to realise how crucial this kind of application is to the growth and wider success of their company.
Do they pose any significant challenges?
As more businesses experience the benefits of fintech, the technology will become less of an added benefit and more of necessity. Additionally, with data handled in a more responsible and efficient manner, businesses can make better-informed decisions due to the improved analysis at their disposal.
As a result, those businesses that have embraced fintech will have a competitive edge due to their enhanced decision-making capabilities. But fintech has by no means reached its limits. Advancements in technology will mean improvements to business performance will continue, and due to the wealth of data already stored in most financial institutions, there is great potential for fintech to build on the success of previous solutions.
Machine learning, for example, can now help to predict future trends in market scenarios and detect data anomalies. This ability to forecast trends and patterns may be coming up and will allow companies to plan for the future far more effectively than before and is especially crucial post-Covid.
What AI trends will we see emerging in the next few years in financial services - and why?
The pandemic has clearly exposed the pitfalls of traditional methodologies in sectors such as asset management and hedge funds, namely quantitative models, to effectively analyse risk and predict market changes during times of record volatility. As a result, there has been a surge in demand from fund managers to integrate AI and machine learning systems into their investment and risk management strategies.
Advanced forms of AI and machine learning, particularly deep learning, will become crucial to funding managers in the coming years. Deep learning allows fund managers to capture more complex, non-linear patterns in asset behaviour, and allows algorithms to adapt to changing market conditions continuously. Fund managers can implement these types of AI in larger numbers to build resilience against any future market shocks, like that of COVID-19, and generate greater returns.
What does the future look like in terms of AI and finserve?
The sheer amount of data that some parts of financial services hold means there is a wealth of potential that is waiting to be tapped into by the predictive capabilities of AI. In a world where it’s possible to capitalise on the abundance of market and alternative data, the challenge is to develop a new predictive system for a set of assets that improves on algorithms currently used by the portfolio manager and makes it easy to move from prediction to portfolio construction.
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