Machine learning is emerging to become a key evolutionary force within the fintech landscape, and will form the cornerstone of the industry’s transition into an open finance ecosystem. 

According to McKinsey data, artificial intelligence (AI) can generate up to $1 trillion in additional value for the global banking industry annually, and machine learning (ML) structures will become a core component of this global trend. 

For fintech firms that are more adaptable when adopting an AI-oriented approach, the opportunity to scale up operations and bolster the efficiency provided to consumers could help foster a competitive advantage that forms a more sustainable platform for growth. 

But how exactly will ML influence the future of fintech? Let’s take a deeper look into the industry’s most transformative applications:

1. The Age of Predictive Analytics

Predictive analytics shaped by AI and informed by ML will help fintech firms to significantly reduce costs throughout their operations while providing a boost to efficiency. 

This ML framework can be mutually beneficial for businesses and consumers alike. With machine learning algorithms working in the background to form a comprehensive understanding of user spending habits, big data has the potential to transform the future of payments. 

The technology can actively learn when consumers could be in danger of overspending or can offer more bespoke investment strategies for retail investors based on their income and financial goals. 

For businesses, ML can offer customer pattern research to better understand how to retain customers and coordinate stock based on specific spending trends uncovered by the machine learning interpretation of customer data.

2. Generative Fraud Detection

The generative AI boom has brought fresh innovation throughout countless industries, but it’s perhaps fintech that’s benefited the most from early GenAI use cases.

Crucially, generative AI can play an invaluable role in training machine learning models to become more adaptable in the face of fraudulent activity or suspicious behaviour. This is because GenAI models can produce synthetic data that actively simulates instances of fraud, helping ML algorithms to train their fraud detection capabilities and offer more comprehensive monitoring for suspicious payment patterns.

3. Next-Generation Risk Management

Machine learning is becoming imperative for risk management. In fact, ML could help pave the way for a frictionless lending environment without borrowers having to be confined to rigid credit scores and income data. 

This opens the door to algorithmic interpretation of varied data sources to create a more holistic overview of an applicant’s risk profile based on factors like their monthly rent and utilities outgoings, social media data, and even medical history to determine a more comprehensive rating and risk assessment that’s focused on the likelihood of repayment. 

Equipped with these insights, ML algorithms can cross-check repayment records of the borrower’s profile against others to learn whether they’re more or less likely to default on their obligations. 

These next-generation insights could help to supersede rigid credit checks that can penalise users who may be perfectly capable of borrowing and repaying their loans. The technology can also help to remove human bias from the approval process.

4. Comprehensive Chatbot Support

Fintech firms require plenty of customer support channels for customers. In an industry as complex as financial technology and its rate of growth, it’s imperative that a responsive help system is in place to aid user queries. 

As a result, ML has the potential to completely transform the way fintech firms communicate with customers and how they can provide accurate responses. 

Machine learning excels in improving chatbot support because of its ability to learn from its experiences with users. This means that for every interaction it has, an ML algorithm can learn how to adapt to the different problems that customers face while maintaining around-the-clock support.

5. Paving the Way for Compliance

The fintech landscape is subject to extensive regulatory scrutiny, and instances of non-compliance can be extremely costly for businesses.

Machine learning can help to bring automation to compliance procedures like anti-money laundering (AML) and know-your-customer (KYC) by operating on an always-on basis to learn recurring spending patterns and to highlight anomalies for further investigation. 

Algorithms can also help to adapt to new regulatory rules and changes as fintech firms manage their operations globally or in heavily regulated markets. 

Sustainable Growth in Finance

The scale of the impact that machine learning can have in the fintech landscape can’t be underestimated. From fraud detection to inclusive risk assessment, ML can help reshape trust throughout open finance landscapes and deliver new levels of efficiency, security, and reliability throughout a range of services. 

For fintech firms seeking to secure sustainable growth throughout the industry’s future, accommodating ML should be a primary consideration.