From investment decisions to enhanced efficiency, ML in FinTech is gaining more and more popularity. And since many financial institutions have invested heavily in deploying machine learning technology, is it a pairing made in financial heaven?
The financial industry continues to look for ways to incorporate machine learning in its daily operations in order to be more profitable and deliver better customer service. Machine learning is highly suited for the financial industry due to the sector’s reliable historical trends, high volume of structured and unstructured data, and quantitative nature.
According to a recent survey, about 70% of all financial services firms are using ML to detect fraud, predict cash flow events, and fine-tune credit scores.
Read on to explore the top 7 FinTech use cases of machine learning and decide whether you need to apply ML in your FinTech organization.
Machine learning in FinTech: how do the two combine?
There’s an endless flow of data in FinTech companies, in response to market fluctuations and the activity of millions of customers, not to mention multiple attempts at illegal activity. In such an environment, it is impossible to manually track all processes and report adequately on these massive operating systems.
The best machine learning applications in FinTech involve pattern identification. They detect correlations within a huge number of sequences and events, extracting valuable information camouflaged among vast data sets that the human eye often misses. The ability of ML to learn and predict enables FinTech providers to recognize new business opportunities and work out coherent strategies.
No wonder, then, that machine learning and FinTech have so quickly joined hands. The global market for artificial intelligence in FinTech is predicted to grow at a CAGR of 16.5% from 2022 to 2030, reaching $41,16 billion by 2030.
Top 7 FinTech use cases of machine learning
FinTech companies can use AI and ML to provide customer-centric services in line with changing market trends. In particular, the top use cases for machine learning in FinTech include:
- Machine learning and algorithmic trading
- Machine learning and fraud detection and prevention
- Machine learning for regulatory compliance
- Machine learning for better customer service
- Machine learning in the stock market
- Machine learning for loan automation
- Machine learning for data analytics and decision making
Let’s get into the detail.
#1 Machine learning and algorithmic trading
Algorithmic trading is a highly effective strategy used by many companies to automate their financial decisions and increase the volume of trades. It involves executing trading orders in line with pre-programmed trading instructions created using machine learning algorithms. Every major financial company invests in algorithmic trading, as the frequency of trades executed by ML technology would be impossible to replicate manually.
For example, 8topuz uses ML algorithms and AI to help investors grow their wealth. The company provides an automated trading system powered by artificial intelligence. By using ML algorithms to analyze massive sets of historical data from different stock markets, 8topuz helps investors design better algorithmic trading strategies.
#2 Machine learning and fraud detection and prevention
PwC’s Global Economic Crime and Fraud Survey found that 46% of surveyed organizations reported experiencing fraud, corruption, or other economic crimes within the last 24 months.
But sophisticated deep learning models, trained with enormous data sets, allow organizations to find hidden relationships between various data points and quickly identify anomalies.
For example, Danske Bank, one of the largest banks in Denmark, previously using a legacy rule-based system for fraud detection, reduced false positives by 60% and increased the detection of fraud by 50%.
Check out one of our recent success stories – an intelligent platform that leverages ML for analytical purposes and helps NFT enthusiasts avoid fraud
#3 Machine learning for regulatory compliance
Large companies are now reporting that the average cost to maintain compliance can be as high as $10,000 per employee.
Among the top machine learning use cases in finance are Regulatory Technology (RegTech) applications. Because ML algorithms can read and learn from a pile of regulatory documents, they can detect correlations between guidelines. As a result, cloud solutions with ML algorithms integrated for use in finance can automatically track and monitor regulatory changes as they occur.
Moreover, banking institutions can also monitor transaction data to identify anomalies. In this way, ML can ensure that customer transactions comply with regulatory criteria.
#4 Machine learning for better customer service
Personalization is the key to building customer trust and loyalty. Particularly when dealing with finances, people value transparency and accuracy. In this case, machine learning for FinTech can analyze customers’ data and predict what services they might like, or provide helpful advice.
For example, Capital One has launched Eno, which can, among other things, monitor expenditure patterns. After detailed analysis, the program can detect if a customer has been charged twice for the same product or service and notify them about it.
Furthermore, due to the remarkable progress in natural language processing, FinTech companies are also using chatbots to solve customer issues. However, today’s advanced chatbots go beyond answering simple customer queries, and can offer more personalized and valuable financial advice.
#5 Machine learning in the stock market
The vast volumes of trading operations generate tons of historical data, providing unlimited learning potential. However, historical data is merely the grounds on which predictions are made.
ML algorithms analyze data sources available in real time, such as news and trade results, to identify patterns explaining stock market dynamics. Traders are then tasked with deciding which ML algorithms to include in their strategies when making a trading forecast, and selecting a behavioral pattern.
Have you heard about MLOps? Read our article to find out what it is and what benefits it brings to software development
#6 Machine learning for loan automation
Machine learning algorithms manage to process more layers of data without being limited to FICO scores and income data. Such applications of machine learning in finance have opened alternative data sources to lenders. For example, many diverse indicators are now being considered to establish an accurate risk score — from social profiles, telecommunications companies, utilities, rent payments, and even health checkup records.
Algorithms compare aggregated data points with thousands of customers to generate an accurate risk score. A loan will be issued automatically if the risk score falls below the lender’s threshold.
Machine learning algorithms can evaluate borrowers without emotion or bias, unlike human credit assessors. According to the Harvard Business Review, financial companies can make lending more equitable by removing racial, gender, and other biases from the models when they are being developed.
#7 Machine learning for data analytics and decision making
Machine learning for FinTech, regardless of the vastness of data, provides thoroughly analyzed insights for real-time decisions, thus saving time and money. However, it also facilitates quicker and more accurate predictions of future trends in the market. Based on these predictive analytics, FinTech companies can stay ahead of the curve with innovative and future-ready solutions that can meet the changing requirements coming from customer and market trends.
Does your financial institution need to apply machine learning?
We will see a computational arms race in the coming years as businesses evolve and new models arise, and this process will go hand in hand with machine learning technology.
More accessible machine learning tools, a variety of algorithms, and decent computing capacity will only increase the number of interactions between machine learning and FinTech, so now is the time to catch up with this trend.
And since FinTech machine learning is still a developing technology, there are no limits on how finance and technology can interact and create much better customer experiences in the future.
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To realize the full potential of machine learning in FinTech and meet future demands successfully, you need to restructure, redefine, and adapt to change.
As traditional finance goes digital, the PixelPlex machine learning company is your ideal starting point. When FinTech tools started to emerge, we recognized history in the making and leveraged their power. Garnering field proficiency en route, we’ve learned to nail any finance-related challenge, plan exclusively legit strategies, and build purpose-driven solutions.
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