The banking industry has undoubtedly gone through a revolutionary process over the course of the past few years, with a significant rise in digital banking solutions since COVID-19. On the back of this revolution, many financial service providers have sought out tools and techniques to provide an improved user experience through their Fintech offerings, not unlike Machine Learning. Machine Learning has made Fintechs more efficient by improving internal processes and procedures, offering a better understanding of customers by analysing their behaviour, and reducing operational costs – to name just a few.
It's no surprise then that so many Fintechs employ Machine Learning techniques to improve their products and gain a greater market share. With all the excitement of recent Artificial Intelligence advancements, we wanted to take a step back and share how Machine Learning still deserves your consideration for improved efficiency, so let’s dive right in!
Machine Learning vs Artificial Intelligence
The first step in determining the right Machine Learning opportunities for your Fintech is to identify how Machine Learning differs from Artificial Intelligence. Consider that Artificial Intelligence is an over-arching technology, under which Machine Learning (and Deep Learning) can be found; Machine Learning is thus a small part of Artificial Intelligence.
To delve into the specifics, Machine Learning relies on specific pre-programming that determines which outputs it should compute, based on previously inputted historical data. Its strength lies in making predictions about this historical data, though it cannot provide feedback outside of its’ specific programming. It incorporates significant amounts of structured and semi-structured data to be as accurate and effective as possible, however, without inputting additional data points, it will not provide outputs outside of its scope.
On the other hand, Artificial Intelligence refers to a computer-based system that relies on algorithms to learn or process information in a similar way to human intelligence. Where Machine Learning can provide information based on the set number of formulae it has been given, Artificial Intelligence is able to make its own assumptions and draw conclusions.
To find out more about Artificial Intelligence specifically, read our blog: What is Artificial Intelligence (AI)?
In many instances, Artificial Intelligence applications incorporate Machine Learning programmes to enhance the AI’s capabilities. Understanding this correlation provides better clarity into just how beneficial Machine Learning can be for your Fintech. Here are some ways you could make your Fintech more efficient, through Machine Learning:
Improved Customer Service
Excellent customer service is an expectation of customer relations, regardless of industry, though the uptick in digital banking solutions has seen an even greater demand for improved customer service experiences in Fintech. Machine Learning can assist you with this goal in two ways:
- Chatbots: In a world of instant gratification, responding to customers’ queries and complaints (or compliments) is a high priority. Chatbots provide a painless, swift solution to responding to customers instantaneously. Chatbots also not only provide feedback to customers when they need it, but they also gather valuable insights for Fintechs to analyse and improve their offerings.
- Personalisation: Machine Learning algorithms can also be implemented to collect customer data, analyse that data, and then tweak the user experience for a more personalised interaction. This offers more tailored solutions to different users, promoting brand loyalty and continued product use.
Protection Against Fraud
Fraud is commonly associated with the banking industry, and now that so much of the industry has gone digital, threats are even more prominent. Hackers are amongst the biggest threats to digital products, as they look to exploit weaknesses. Machine Learning cannot necessarily eradicate these threats, but it can certainly assist Fintech’s security measures against them.
- Data analytics: Analysing user information goes further than just assessing behaviours for improved services, it also assists in fraud detection. Machine Learning algorithms can collect data points such as location, the device used and more, to assist in these efforts and isolate any concerns.
- Attack detection: With Machine Learning’s ability to process large quantities of data, it’s no surprise that its algorithms are fully capable of detecting fraud attempts more accurately than humans. Not only this, but they can also alert to potential threats, as well.
- Regulatory compliance support: Another great benefit of Machine Learning, thanks to its capabilities mentioned above, is the timeliness it lends Fintech owners when it comes to keeping policies up to date, which is valuable for maintaining regulatory compliance.
Minimised Loan Approval Risk
Whether peer-to-peer or traditional institutions, lending is an area of the banking industry that has often been shrouded in risk. Machine Learning algorithms can mitigate risks associated with loans:
- Credit-risk models: Algorithms analyse borrower data against other lenders to determine if that borrower would be able to pay back the loan. As previously mentioned, this ML would make use of historical data and data points related to repayments to effectively assess the success rate of loan repayments. Borrowers who do not automatically meet the requirements are referred to customer service advisors.
Credit-risk models greatly minimise the time and resources needed to determine loan safety, though some argue that historical data may be outdated in this instance.
Refined Forecasting
While traders and investors are highly skilled in their craft and able to deduce effectively, to some degree, the ever-moving needle of stock market forecasts, Machine Learning algorithms are unparalleled.
- Stock market forecasting metrics: Machine Learning algorithms are capable of processing vast amounts of historical data while simultaneously monitoring data in real-time. This combination makes their computer processing capability highly accurate and effective with live updates that can be pushed to traders and investors for improved strategy.
Effective Financial Planning Assistance
Effective financial planning assistance is a sought-after financial service, particularly in the field of investing. Machine Learning provides Fintechs with a competitive advantage using robo-advisors.
- Robo-advisors: robo-advisors are ML-driven solutions that analyse consumer data found in surveys (or through automated algorithms) to assess their financial status and make informed investment decisions based thereon, that are in the consumer’s best interests.
Robo-advisors are still developing, and many have argued on the ethics and accuracy behind survey-focussed robo-advisors, though newer models have proven to be more accurate as they assess, not only income but investment habits and level of risk aversion, too.
The Right ML Solution for Your Fintech
The key thing to consider when determining the right Machine Learning tool for your Fintech is not to incorporate multiple solutions all at once, but to rather decide on specific models or algorithms that will further support your unique selling point. If your USP is risk aversion, you’ll want to consider a Machine Learning model that detects fraud. If your core values are set on user experience, you may want to enable an algorithm that personalises their experience or incorporate a chatbot for fast, effective communication.
While Artificial Intelligence continues to make waves across the Fintech sphere, within the creative industry, and the general tech space, Machine Learning still has its place and is highly beneficial for Fintechs looking to make cost- and resource-savings across a range of procedures and functions, that will ultimately result in improved user experience. If you’d like to find out more about Blott Studio’s Machine Learning products, schedule a chat with our experts!
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