With the financial world in a constant state of disarray and uncertainty, technology becomes the saving grace to navigate through the complexities and resolve the problem of predicting what’s next to come. While concepts like neural networks and fuzzy logic may require companies to raise their budgets in terms of technology and experts, the truth is the payoff is massive. Let’s take a look at some of the uses machine learning can have.
Stock Price Movements
Using the right tools and algorithms with the best learning-testing schemes can help in creating a perfect portfolio for predicting price movements in a multivariate environment. Several online binary options trading platforms, as well as options trading platforms, are now employing such methodologies in delivering solutions with more refined figures.
Loans, Insurances and Interests
The average person doesn’t have the time or interest to properly gauge the various schemes for loans and insurances and compare the numerous differences between different plans. Machine learning helps in delving deeper into these data frames in a more meticulous fashion that gives them an edge over what even the most successful of speculators can do.
Deep learning algorithms can dissect the nooks and crannies to discover possibilities of risk, fraud and other factors that may affect the decision making steps of loans, insurances and the interests associated with them.
Also Read: What’s Machine Learning All About?
Machine learning has become even more famous in the sector of biometrics to create systems that have stronger security protocols and entry methods with augmented identity confirmation steps. One such pioneer is Aimbrain whose machine learning algorithm becomes a part of the user’s interaction online and keeps track of everything from typing speed to click-rates and even how the user reacts to content. Any sign of an anomaly will immediately result in the system asking for a facial or voice confirmation.
Fintech companies have benefitted in using machine learning for cybersecurity purposes as well such as DarkTrace whose AI learns the mechanisms of the human immune system in replicating similar defence strategies against network attacks in servers.
Accounting and Record Keeping
Verifying statements, transactions and records is a crucial part of Fintech companies which rests upon the accuracy of data. Machine learning algorithms cut down on the time which would normally be much longer for a human. The modern-day software even allows for better accuracies with a minimal human error for just an additional fee and allow users to process data across various data formats, thus ending the conundrum of incompatibility as well. The Cube system developed by Duco, for instance, lets companies and users work on any data, in all formats in mere minutes. Data can be loaded instantly, compared and debugged quickly without passing it over to separate teams.
Simple AI learning algorithms have been in brokerage firms to draw results from arbitrageurs and speculators as well as investors looking for a nice deal. Traders often set predefined tasks such as price setting, short selling, buying long stocks, selling long stocks, buying short stocks, selling short stocks, hedging, risk management, portfolio evaluation and much more.
As the trading floors become more replete with machines that replace the crowded nuances of stockbrokers, machine learning will help in finding correlations and patterns which are otherwise unknown to those in the financial services sector. Even on the battlegrounds of Wall Street, Trafalgar Square, Bombay Stock Exchange and Silicon Valley, better results are guaranteed to those with advanced deep learning systems.
Banks often set aside certain capital as part of regulatory implementation without which they wouldn’t function with much profit. Such regulations are instilled to introduce risk control measures, keep a steady supply of capital and make the financial sector more transparent to users. Such drastic demands require technology that has drastic tools and measures.
Machine learning comes to their aid by providing real-time insights into any issues, to warn them about any impending risks and to identify any regulatory problems beforehand. Breaches, phishing, thefts, forgeries and scams become a thing of the past as machines filter through data at great speeds to keep decision makers ahead in formulating more effective strategies.
The promise of technology in any sector has always been that of awe and hope. Machine learning’s best use comes to those in FinTech who have the proper investments in the best machines with the best technology with the adequate amount of workforce behind it to create meaningful decisions. Critics of machine learning may dismiss it by calling it another step in a totalitarian regime where machines rule, but such technologies will inevitably become an indispensable part of our lives to account for a rapidly growing population that generates unlimited data each day. The signs point towards the same direction that machine learning is the way to go for any FinTech company.