Do you see a tight connection between fintech & Machine Learning? Not yet? Then this article is created for you.
Over the last few years, the value of Machine Learning in banking has strengthened globally, allowing businesses to qualitatively process tons of data, draw helpful insights from it, and come up with informed decisions. 2022 was the peak of this trend: half of the global executives confirmed the adoption of AI in their daily operations and product.
Why so? Keep reading, and you’ll find it out!
Importance of Machine Learning in fintech and banking
Well, let’s stop for a moment to point out the key benefits of implementing Artificial Intelligence in fintech:
✅ Calculated risks and avoided costly mistakes
✅ Streamlined business operations due to real-time visibility
✅ Actionable insights that hint at the optimal business decision
✅ Enhanced security standards towards data protection and anti-fraud resilience
✅ Latest user identity mechanisms that minimize human manual involvement
✅ Process automation thanks to effective data management
✅ Fast virtual onboarding and enhanced customer support
✅ Improved user experience and product perception
✅ Customer loyalty
✅ Increased sales and revenue.
Can you wish for anything else from the fintech product? Or your customers? Arguments are solid, but now let’s move to Machine Learning use cases in banking.
Main uses of Machine Learning in finance
Though the spheres of using ML in the financial sector are becoming more diverse each year, today we’d like to draw your attention to the four most widespread cases. Any guesses before we start?
Maybe you’re thinking of payments, bills, or spam filtering. But the merge of banking & Machine Learning is way deeper these days. Grab the key Machine Learning use cases in finance for 2023 right below.
Fraud detection and prevention
As you’ve learned from the list of benefits, security is one of the prominent reasons for using ML in banking. With the help of algorithms, companies are able to analyze high volumes of data, identify hazardous signs, and send alerts to their users to preclude the involuntary consequences of data manipulation. In other words, by implementing ML in the product or service, you raise the chances of opposing cyber-attacks and financial losses.
To prove the importance of this action, consider the average cost of a data breach. For the last 3 years—from 2019 to 2022—it raised from $5.86m to $5.96m. Even in the UK, one of the most powerful fintech hubs in the world, the statistics remain alarming. In 2020, the annual losses from fraud among British online banking users reached over £159m.
So, now that you’ve got no doubt about the significance of ML fraud protection, take a couple of famous examples:
📌 Riskified. This public organization has turned into the SaaS helping other businesses prevent cyber crimes. Also, they solve chargeback cases and provide excellent security protection for enterprises.
📌 Sift. If you’re searching for software to preclude fraud and various types of digital abuse, Sift is a good solution. The easy-to-use control panel will make the task simpler for your company.
Customer service and personalization
Though you may be still hearing that virtual assistants don’t compare to live consultants, the former will save you a fortune in the speed of answering users’ questions, amount of requests processed simultaneously, and the 24/7 work schedule. Hence, chatbots are still to be, and their spreading in finance isn’t accidental. Here, they consult users in regard to payments, account issues, transactions, etc.
Moreover, in addition to speedy answers, ML-powered chatbots can also provide customization perks for your customers. For example, if the algorithm classifies the user’s request as complicated enough, it may automatically assign the human operator to help a user solve the issue on a deeper level. Also, remembering the history of questions, a chatbot can offer special offers one is more likely to be interested in.
A few companies that practice this approach in banking:
📌 Wells Fargo. By the way, that's the first American bank that started using an AI-driven chat with users through Facebook Messenger.
📌 Bank of America. Their AI-based virtual assistant, Erica, created a boom over their service in 2018 and helped over 1m users during the first quarter of its existence.
Risk management
Are you surprised by the fact that Machine Learning & banking have another common target—risks? By this, we imply that ML algorithms masterfully analyze a number of critical factors, such as social profiles, bank history, tax payments, and more to check the individual’s suitability for loan approval. This process happens quickly, showing banks your credit scoring and segmenting customers into ‘valid’ and ‘non-valid’ in a few minutes.
The most famous instances in this connection include:
📌 Citi. After building an ML-driven risk forecasting model, this big global financial provider spread its operations in 98 countries.
📌 Aire. This fintech player offers credit assessment services through AI as the key to realizing this goal.
Trading and investment decisions
The massive adoption of ML in trading and investment started not so many time ago—in 2020. It was a moment when predictive analytics and virtual assistants entered the scene to optimize trending and investment companies’ operations.
Specifically, with the help of ML, trading providers got access to quick news and price monitoring and could timely react to the identified hazards. Alternatively, robo-advisors contributed to taking investment to a completely new level—daily savings, protection from inflation, and customer preferences counted.
Just look at the most recent cases in this regard:
📌 Precision Alpha. The company can supply you with ML-focused market signals for over 85+ global markets.
📌 Deserve. This trading company is famous for using ML to provide users with credit cards, even if have no credit score now or had some issues with it before.
Fintech examples of Python and ML in web development
It all sounds good, but you might want to hear some examples of how the blend of ML with Python works in practice. As Patternica is primarily specialized in fintech software development, we’ve decided to search for illustrative cases in this industry:
📌 Investment platforms—market research and predictions;
📌 Insurance claims processing—process automation;
📌 Autonomous decision-making—independent intelligence reads prompts and executes actions;
📌 Risk management—loss minimization;
📌 Fraud detection—safety of using a platform and data protection;
📌 Insights generation—smart data handling;
📌 Chatbot assistance—high-quality service to users… etc.
If translating it to the brand names, the most prominent Machine Learning Python program examples are Venmo, Robinhood, and Anaconda.
Future outlook on fintech & artificial intelligence under its hood
We believe that 2025 will keep this fashion and stir an even greater number of volunteers to create ML-based applications in finance. That’s why it’s time for you to reinforce your business with the ML technology exactly for your profile. We’ll find the proper way to implement ML for your fintech product. Think of the benefits of augmented data analytics and contact Patternica to develop the data-driven product mindfully.
FAQ
What are the benefits of using machine learning in finance?
Key benefits include improved accuracy in forecasting, faster decision-making, enhanced fraud prevention, cost savings, increased operational efficiency, and personalized customer experiences.
Is machine learning safe and secure to use in banking applications?
Yes, when implemented correctly. ML models can actually strengthen security through real-time anomaly detection and risk monitoring, although they must be audited regularly to ensure compliance and ethical use.
What’s the difference between machine learning and traditional financial analytics?
Traditional analytics relies on predefined rules and static models, while ML adapts and improves over time by learning from new data, enabling more dynamic and predictive insights.
Can small or mid-sized financial institutions adopt machine learning?
Absolutely. With scalable solutions and cloud-based ML platforms, even smaller institutions can implement ML for specific use cases like credit scoring or customer service automation.
What are the challenges of adopting machine learning in banking?
Challenges include data privacy concerns, lack of skilled talent, integration with legacy systems, regulatory compliance, and the need for transparent, explainable models.
Which programming language is most compatible with Machine Learning (ML)?
Though experts are still debating on this, we consider Python the best option for all.
