How is machine learning changing the landscape of FinTech?



Machine learning in FinTech is a critical enabler in tech-driven banking, where efficiency and innovation are key to staying ahead of the competition. It transforms obstacles into lucrative possibilities by revolutionizing crucial areas such as risk management, fraud detection, algorithmic trading, and compliance.
In the year that artificial intelligence (AI) had its most spectacular public debut, it might look like machine learning (ML) has been reduced to a fad.
However, it is the furthest thing possible from the truth. Even if it might not be as popular as before, machine learning is still very much in demand today. This is so that deep learning may be used to train generative AI. FinTech is no exception.
With a projected global market size of about US$158 billion in 2020 and rising at an 18% compound annual growth rate (CAGR) to reach a staggering $528 billion by 2030, machine learning is one of the most valuable tools available to financial institutions for process optimization. And in the end, as our most recent State of AI study goes into great depth, save expenses.

Use cases of machine learning in FinTech

Machine learning is solving some of the industry’s core issues. Fraud, for instance, affects more than simply insurance or cryptocurrencies. Furthermore, strong regulatory compliance transcends domain boundaries. Regardless of your industry or type of business, machine learning in finance offers a variety of ways to convert concerns into gains.

1. Algorithmic trading

Many businesses employ the very successful tactic of algorithmic trading to automate their financial choices and boost transaction volume. It entails carrying out trading orders following pre-written trading directives made possible by machine learning algorithms. Since it would be hard to replicate the frequency of trades done by ML technology manually, every significant financial company invests in algorithmic trading.

2. Detecting and preventing fraud

Machine learning solutions in FinTech constantly learn and adapt to new scam patterns, improving safety for your company’s operations and clients. This is in contrast to the static nature of classic rule-based fraud detection.
Algorithms for machine learning can identify suspicious activity and intricate fraud patterns with great accuracy by examining vast datasets.
IBM demonstrates how machine learning (ML) can identify fraud in up to 100% of transactions in real-time, allowing financial institutions to minimize losses and take prompt action in the event of danger.
FinTech systems that use machine learning (ML) can detect numerous forms of fraud, including identity theft, credit card fraud, payment fraud, and account takeovers. This allows for complete security against a wide range of threats.

3. Regulatory compliance

Regulatory Technology (RegTech) solutions are among the most popular use cases of machine learning in banking.
ML algorithms can identify correlations between recommendations since they can read and learn from huge regulatory papers. Thus, cloud solutions with integrated machine-learning algorithms for the finance sector can automatically track and monitor regulatory changes.
Banking organizations can also keep an eye on transaction data to spot irregularities. ML can guarantee that consumer transactions meet regulatory requirements in this way.

4. Stock market

The massive volumes of commercial activity generate large historical data sets that present endless learning potential. But historical data is just the foundation upon which forecasts are built.
Machine learning algorithms look at real-time data sources such as news and transaction results to identify patterns that explain the functioning of the stock market. The next step for traders is to choose a behavioral pattern and determine which machine learning algorithms to incorporate into their trading strategy.

5. Analysis and decision making

FinTech uses machine learning to handle and understand large amounts of data reliably. Through the integration of data analytics services, it offers thoroughly investigated insights that expedite real-time decision-making while saving time and money. Additionally, this technology improves the speed and accuracy of forecasting future market patterns.
FinTech companies can also use predictive analytics technologies to develop innovative, forward-thinking solutions that adapt to shifting consumer demands and market trends. With the help of data analytics and machine learning services working together, FinTech companies can foresee and successfully address new financial needs thanks to this proactive strategy.

How do companies benefit from machine learning in FinTech?

The above points highlight the use cases of machine learning, but what about the specifics? How may the main advantages of ML in FinTech be best summarized if limited to a small number of objective bullet points?

1. Automating repetitious processes

Automation is likely the most obvious machine learning benefit for FinTech, having several advantages. To validate client information in real-time without requiring manual input, for example, machine learning algorithms can expedite the customer onboarding process.
Furthermore, by doing away with the necessity for human data entry, automating the reconciliation of financial transactions saves time and money. The rest of your team will benefit from automation in more subtle ways. ML-driven automation removes the tedious work that prevents your professionals from working on more important projects.

2. Allocation of resources

Through pattern recognition, machine learning establishes the best allocation of funds, labor, and technology. As said before, robo-advisors use machine learning (ML) in FinTech investment management to assess each client’s risk profile and allocate assets ensuring each client’s portfolio is in sync with their financial goals and risk tolerance.
Furthermore, chatbots powered by machine learning offer round-the-clock customer care by allocating resources efficiently to handle a high volume of consumer inquiries. In this way, FinTech companies can increase the scope of their offerings without significantly increasing operating costs.

3. Reducing cost through predictive analytics

FinTech companies can find opportunities for cost reduction with the help of machine learning-driven predictive analytics. For instance, in lending machine learning (ML) can predict loan defaults, enabling lenders to spend resources more effectively to reduce prospective losses.
Another financial location uses customer pattern research to create a similar situation. Businesses may proactively retain customers and lower the cost of recruiting new ones by using machine learning to predict customer turnover.

4. Data processing

FinTech software development companies can leverage technologies like optical character recognition (OCR) and other automated document processing systems to extract important data-driven insights, as machine learning handles large-scale data processing and analysis.
This greatly reduces a company’s reliance on sizable data analysis teams and related costs by automating processes such as processing loan applications, Know Your Customer (KYC) checks, and regulatory compliance.

Case studies of implementation of machine learning in FinTech

Machine learning has been offering value to the FinTech software development industry. Here are some great case studies around the globe.

1. Credgenics

In 2022, Credgenics, an Indian SaaS startup specializing in legal automation and debt collection, attained a $47 billion total loan book, having processed over 40 million retail loans.
Over 100 enterprise customers have benefited from lower costs and collection times, increased legal efficiencies, and higher resolution and collection rates because of their machine learning-powered solutions.

2. The contract intelligence of JPMorgan Chase

In 2017, the biggest bank in the US unveiled a contract intelligence (COiN) platform that heavily leveraged natural language processing (NLP) to enable computers to understand voice and handwriting.
The primary goal of COiN was to automate labor-intensive, repetitive manual processes, like reviewing commercial credit agreements, which was estimated to require up to 360,000 labor hours in the instance of JPMorgan Chase. COiN could complete the task in a few seconds.

3. Wells Fargo

Wells Fargo is a worldwide financial services firm headquartered in the United States that employs machine learning solutions such as NLP, deep learning, neural networks, and predictive analytics enablers to handle individual and bulk client data points.
What makes this noteworthy? The capacity to identify the intent behind a customer’s phrasing in complaints, which may be overlooked during a typical transcript reading. This enables the organization to streamline operations, provide more efficient services, and foster stronger client relationships.


FinTech is not one of several professional industries concerned about AI apocalypses. That is not to say that trading organizations aren’t concerned about the potential ramifications of AI-powered false data — or that FinTech professionals aren’t keeping an eye on things.
However, none of the faster rate of modernization forced by technology is unique to FinTech. It’s in the name of technology that drives FinTech ahead and keeps it together. It is what differentiates the FinTech workforce as one of the most technologically advanced in any industry. To many, that is what drew them into FinTech in the first place. Our experts are intimately familiar with the situation.




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