The Future of Fintech: Inside the Machine Learning Revolution in the Financial Technology Sector

It’s been estimated that the impact of machine learning (ML) in fintech could drive the industry’s growth from a $158 billion market into a $528 billion landscape by as soon as 2030, highlighting the technology’s long-term potential that extends far beyond any mistaken perceptions that it’s just a temporary trend. 

The reason for the high potential of ML in the fintech landscape is because of its ability to process massive unstructured datasets in real-time, enabling more financial institutions to deliver a level of hyper-personalization to consumers that’s never before been possible.

At the same time, machine learning applications can tackle systemic risks and deliver significantly lower operational costs at scale for institutions of all shapes and sizes. With this in mind, let’s take a deeper look at the ways fintech is being quietly revolutionized by the arrival of ML: 

Dynamic Credit Risk Assessment

Machine learning is transforming how fintech risk assessments are conducted, changing it from a reactive, rules-based process into an automated and real-time predictive model. 

Because legacy credit scores rely on strict parameters, the more dynamic risk engines that ML can provide have the potential to allow more users to access the credit they need without the danger of falling through the net. 

These ML tools can analyze unstructured big data in a way that can accurately analyze spending behavior on a case-by-case basis to add a much-needed level of dynamism to the industry. 

Recent studies have suggested that traditional credit systems currently exclude around 26 million Americans. Many of these would-be borrowers aren’t uncreditworthy, but they’re simply left out of FICO scoring models because of thin credit, too few accounts, or no credit history to draw on. 

But with the help of deep learning techniques, predictive models can be enhanced to better predict factors that span different credit risks based on data surrounding each individual borrower, as well as wider market risks and possible fraudulent activity, both of which can also influence risk factors. 

Monitoring Global Compliance

Truly global fintech relies on a great deal of regulatory compliance in every jurisdiction served, with the threat of significant cost implications for any service provider that falls into noncompliane territory through a lack of oversight. 

For fintechs with a truly far-reaching international presence, it can be a challenge to keep on top of regulations worldwide, which are prone to publishing updates regularly and in a way that can fly under the radar. 

The ability to continuously monitor the regulatory landscape can also help fintechs to identify new market opportunities. For instance, recent Free Trade Zones in China have recently opened the door to full foreign ownership in selected fintech sub-sectors throughout certain cities, which will require a special focus for firms looking into any meaningful expansion throughout Asia. 

Thanks to a blend of ML and natural language processing (NLP), it’s possible to deliver around-the-clock monitoring of global regulatory databases, interpret text, and automatically alert compliance teams to ensure that products and services remain compliant at all times. 

Next-Generation Wealth Management

We’ve already touched on the power of personalization in delivering bespoke credit scoring systems for a fairer borrowing experience in fintech, but similar ML tools can also help to create a more proactive approach to wealth management that can serve individuals with just about any initial deposit value. 

Rather than enlisting the help of financial planners, machine learning algorithms can move beyond rigid investment portfolios to enrich individuals with fully customised strategies that can involve the creation of bespoke indexes full of hundreds of individual stocks for a single retail client. 

This helps the industry to move beyond general ETFs by providing portfolios that are designed to match the individual preferences of each client and at a lower cost than most traditional services. 

Another advantage that can help to drive the next generation of wealth management is values-based filtering, where ML algorithms can be instructed to only add stocks that comply with the mindset of each client. This could pave the way for more focused ESG strategies and approaches that closely comply with the risk appetite of each investor that’s being served. 

An Inclusive Fintech Ecosystem

Above all else, the arrival of machine learning could be critical in creating a truly inclusive fintech ecosystem where no clients have to suffer against rigid credit scoring systems and ill-fitting investment products. 

Instead, ML can create a brand new environment that thrives on hyper-personalization at scale, where individuals are served in a way that matches their goals and values in equal measure while maintaining full compliance along the way. 

Machine learning is set to create a globally sustainable financial ecosystem for all participants and is likely to be the most revolutionary area of AI to help modernize the industry.