How is machine learning being used by FinTechs?

The use of Artificial Intelligence (AI) is now a central feature in the technology debate for FinTechs and machine learning – a subset of AI – is gaining traction across the sector as it provides wide-ranging benefits to companies.

What is ML?

ML is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using exact instructions, relying on patterns and inference instead.

Algorithms build a mathematical model based on sample data, making analysis of market patterns, taking onboard previous history and building a successful analytical model with little to no assistance from humans, to make predictions or decisions.

What are the benefits?

Customer experience is vital in the sector and it is one of the main uses that ML can offer FinTechs, as it helps to improve the customer experience on Chatbots, making the interaction more personable.

Assistants are integral to the FinTech service and Chabots have existed for some time, but ML takes them a step further, as it enables virtual assistants to learn, rather than just follow a pre-determined set of instructions.

ML-based chatbots have the ability to adapt their approach to the behavior of each customer, acting and feeling more human and so enhancing the customer experience.

Another benefit, is ML can help with marketing as it can bring predictive analytics to the table, based on past behavior, helping companies to improve their marketing strategies as they can develop more successful and focused campaigns.

The technology enables analysis of web activity, mobile app usage and response to previous ad campaigns, predicting the effectiveness of a marketing strategy – and helping them to plan their strategies more effectively.

One benefit that is arguably the biggest of all for FinTechs, is that ML can assist with risk, fraud evaluation and management.

The software can help FinTechs identify and prevent fraudulent transactions as it has the ability to analyse high-volume data. ML can spot patterns that exist using predictive analytics, and algorithms can block fraudulent transactions with a degree of accuracy that is not even possible with stand-alone AI.

When it comes to risk management, ML can identify current market trends and relevant news items can affect a client’s ability to pay for a transaction, and so improve decision making. ML also helps prevent financial crime and crisis prediction.

Security is something that is high priority for all FinTechs, and ML can analyse and recognise suspicious activity across company networks and provide alerts of potential hackers.

The power of ML and its capability to process vast amounts of data quickly plays a role in cybersecurity, giving companies a vital new line of defence against hackers who target them as they handle large volumes of customer data both personal and financial.

Predicting churn is another benefit of ML for FinTechs as it can help companies make better decisions by taking preventive actions, as the technology can analyse ‘churn behaviour’ data.

The technology can provide a list of customers who indicate that they are considering cancelling, based on behavior data to help reduce the churn and provide a better service.

There are other ways FinTechs are using ML to help improve their business and these include more custom solutions such as trading, regulatory technology and for research purposes along with other tailored solutions.

We are still in the early stages of the use of ML in FinTech and it is growing at pace every day and the list of its uses will also continue to expand.