Finance technology trends

The finance sector is constantly under pressure as consumers demand more autonomy and faster service. In response, financial institutions are finding new ways to use technology to improve efficiency and meet these needs.

But with so many options available, it can be difficult to know which solutions are right for your business. To help you make the best choices for your organisation, we've compiled a list of the top FinTech trends for 2023.

Hyper personalisation of services and offers for bank customers 

One of the growing FinTech industry trends is becoming more of an imperative than an option. Hyper personalisation means custom-made digital banking experiences for each customer. Backed by Artificial Intelligence (AI) and behavioural science, this trend is characterised by collecting data on an individual’s shopping habits, location, transaction history, and more. This data could later be used by the bank to make future offers based on the customers’ evolving needs. 

Composable applications 

Composable applications are having a profound and immediate impact on the FinTech industry because they enable fast adaptation to changing business conditions. Composable applications allow organisations to develop applications that can be rebuilt and redeployed according to their needs. The ideal finance function technology portfolio is modular, allowing finance team users to acquire, assemble and configure different application capabilities in personalised ways that facilitate their work. According to Gartner, through 2024, 50% of financial application leaders will incorporate a composable financial management system approach. 

AI and ML for risk management and securing financial data 

Artificial intelligence (AI) and machine learning (ML) have made their way to the FinTech industry and will continue to play a huge role in reshaping businesses. AI and ML can be used by organisations to improve their customer experience with the use of bots and virtual assistants. At the same time, they can be used to automate mundane tasks like cash flow management. 

Machine learning works on a lot of data to generate useful insights and can help in risk management, marketing, decision-making, and customer retention. Security is also an area that ML and AI can help improve. For example, both can work to keep user data safe by identifying potential breaches or suspicious activity well in advance. 

Decision intelligence 

Understanding how an organisation makes decisions requires breaking down the decision-making process and aligning each component with a standardised framework. These components are, in turn, aligned with a set of technologies and techniques to support modular automation and continuous improvements. According to Gartner, by 2023, more than 33% of large organisations will have analysts practising decision intelligence, including decision modelling. 

Using data-based predictive analytics is very important for companies to make informed business decisions. It can help them understand their customers better and market their products accordingly.  

If you have been considering a new digital offering for your customers or an internal process contact us today to talk about the first steps to providing a solution for your organisation.