Benefits of Machine Learning in Fintech & Top 3 Spheres of Use
The article was initially published on Vilmate Blog.
The banking and finance sector is a good example of how businesses can put contemporary concepts to their service. Machine learning in fintech, combined with AI, has a huge impact on workflow and customer care in the industry.
So let’s briefly cover this topic in the following post.
Why banking & finance are eager for AI/ML
Predictive and decision-making abilities of machine learning algorithms, with the capabilities of AI systems, assist in a variety of spheres in banking and finance:
- Fraud analysis
- Risk management
- Sales forecasting
- Customer support
- Asset management
- Service personalization
- Product recommendations
- Stock price prediction
Such a vastness is conditioned by the ability of AI/ML tools to cope with large amounts of diversified data. It allows to automatically keep track of multiple corporate processes and form adequate reports regarding the steadiness of operating systems.
Major benefits of implementing AI and ML in fintech
Smart solutions are currently an essential part of fintech services.
Finextra, an independent fintech newswire, states that the decline of interest in AI/ML solutions in the foreseeable future is totally unexpected. But what are the benefits that ensure such tendencies?
Basically, they correspond to what any business enterprise would like to observe under the hood:
- increased cost-efficiency
- advanced fraud prevention
- reduced biases
- boosted customer engagement
- enhanced scalability
- improved time-management
Let’s overview a few spheres of finance where these benefits find their practical implementation.
Decision-making & Credit scoring
Old, previous-generation credit scoring systems used to work with the general data of an applicant only, i.e., age, gender, occupation. At the same time, ML-based scoring systems are able to provide more personal and accurate decisions relying on a variety of settings.
Person’s saving habits, traveling location tracks, expense preferences, and other digital activity set markers for an ML model to form a relevant weighted decision.
By expanding data sources beyond stereotypical ones, ML tools, in their turn, increase the numbers of the performance of loans. What makes it real is that they can provide more ‘trust’ to social groups, like pensioners, that outdated credit-scoring systems wouldn’t.
Security, Risk Management & Fraud detection
To feel secure in the fintech market, one should be confident about their fraud detection capabilities and the expediency of any investment decision made.
Machine learning algorithms, with their predictive features, are aimed at giving proper feedback on the market fluctuations and assist in the right time for buying or selling a particular stock.
On top of it, any abnormality will easily be spotted, leaving no chance for fraud, money laundering, or any kind of malicious and illegal activity.
Quantitative & Algorithmic trading
To make a profit in the market, trading companies have started utilizing ML algorithms with mathematical models. They are built for analyzing vast amounts of historical data and building statistical models to track patterns and optimize investment strategies.
The trades predicted in such a way have minimum risks and can be made simultaneously on multiple markets. Moreover, they can vary from short- to mid- to long- term, in correspondence with the ongoing market environment.
You may find it interesting: How to Build a Trading Platform like E*TRADE or Robinhood?
Words to finish
If you’re interested to know more about the topic, feel free to read the full text: Machine Learning in Fintech: Top 5 Uses & Overview. You’ll find out about a couple more financial spheres of typical AI/ML uses there.
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