Share this post
Pavlo Khropatyy, VP and global head of delivery, FS&I, at Intellias, explores the profitable alliance of machine learning and finance, highlighting five remarkable ways this technology is revolutionising the industry.
Machine learning (ML) has become a powerful tool in various aspects of our lives, from transportation to customer service. Today, ML is revolutionising the way financial institutions operate. However, its application in the financial sector, especially in banking and fintech, is gaining popularity due to its potential to drive lucrative results.
The fusion of machine learning and finance has the potential to reshape the industry. The research indicates that the market size of artificial intelligence (AI) in fintech grew up to $7.3 billion in 2022, making AI and ML the most impactful trends in the sector.
Five notable uses of machine learning in finance
There are five remarkable ways machine learning is revolutionising the industry, through:
- Loan approvals and credit scoring;
- Better fraud prevention;
- Easier regulatory compliance;
- Customer service personalisation; and
- Being a powerful tool in the stock market.
Loan approvals and credit scoring
The use of machine learning in finance spans various domains, from banking and credit offerings to asset management and regulatory services.
One major application is credit scoring, which allows lenders to gain deeper insights into a borrower’s ability to pay by analysing extensive and diverse datasets, including social profiles, utility payments, and health records. This data-driven technique opens new possibilities for loan approvals with lower risks, attracting borrowers who were previously overlooked.
Prominent providers in this category include ZestFinance, using machine learning-based credit models for more profitable underwriting, and Deserve, offering credit cards based on machine learning for individuals without a credit score or needing credit rebuilding.
Fraud prevention: A key focus area for machine learning
Fraud in the fintech sector poses a significant challenge for businesses, leading to massive economic losses. Machine learning offers a promising solution for combating financial fraud.
By analysing enormous datasets of simultaneous transactions in real time, machine learning algorithms can identify patterns indicative of fraudulent activities. The advantage of using machine learning lies in its ability to continuously learn from results and update models, minimising the need for human intervention in detecting fraudulent behaviour.
Noteworthy providers in this sector are Feedzai, offering advanced fraud prevention measures powered by machine learning, and Biocatch, combining behavioural biometrics with machine learning to detect and prevent cybersecurity threats.
Regulatory compliance made easier with machine learning
Complying with ever-changing regulations is a time-consuming and costly process for financial institutions. Machine learning applications under the category of regulatory technology (RegTech) can streamline this process by reading and learning from a multitude of regulatory documents, automating the tracking and monitoring the changes.
Machine learning can also help organisations monitor transaction data to ensure compliance with regulatory requirements.
Notable providers in this category include Pendo Systems, streamlining compliance processes using unstructured data, and ComplyAdvantage, employing machine learning for fintech compliance and online fraud prevention.
Customer service personalisation through machine learning
Fintech companies are leveraging machine learning to improve customer experiences and create personalised offers.
Machine learning algorithms analyse vast amounts of customer data, enabling companies to predict user preferences accurately. Intelligent chatbots powered by machine learning provide human-like interactions and reduce the need for extensive customer service departments.
Machine learning as a powerful tool in the stock market
Machine learning algorithms have found a strong foothold in the stock market by predicting market dynamics based on vast historical trading data.
These algorithms can quickly analyse real-time data sources, such as news and trade results, to identify patterns indicating price movements. The speed, accuracy, and lack of bias in machine learning models make them a valuable asset for traders.
Distinguished providers in this sector are Sentient Technologies, developing quantitative trading and investment strategies using AI, and I Know First, providing stock forecasts based on machine learning predictions.
What does the future of machine learning in finance hold?
The future of finance lies in the profitable alliance of technology and data-driven decision-making.
Machine learning’s impact on the financial industry is just beginning to unfold. As more accessible tools and computing power become available, the number of machine learning use cases in finance will undoubtedly increase.
Embracing this trend will enable financial institutions to enhance fraud detection, improve customer experiences, and make data-driven decisions, ensuring they stay competitive and secure in a rapidly evolving landscape.
By harnessing the power of machine learning, the financial sector can unlock new opportunities and drive profit-generating results for businesses and customers alike.
Pavlo Khropatyy, VP and global head of delivery, FS&I, Intellias