AI and ML in fintech are the gold mines with automated and enhanced tools used in the financial sector globally. Read on and find out more!
Meet Machine Learning
Machine Learning (ML) is a subset of Artificial Intelligence that uses algorithms and various techniques to teach computers to act on data. It learns from an amount of information, understands specific data patterns, and builds predictions based on these patterns. Machine Learning is powered by Artificial Intelligence (AI) and, this way, makes a computer automatic in its learning abilities and experience improvement. In machine learning, computer experience is based on and generated by Artificial Intelligence. The best two examples of ML in daily life are spam detection built-in every email and Facebook face recognition on pictures, where you tag friends, family, and colleagues. In both cases, data patterns are being analyzed and each time you receive a suspicious letter it will be shifted to a spam folder or if you’ve once tagged your friend, AI will visually tag this person for you on every other picture.
ML is similar to human learning to some extent. Human learning also requires data input and certain requirements to let the person identify what is being searched for. In the computer, data sets are fed into the system to adjust and optimize algorithms that will carry out the work further. Hence, it requires human help to put rules into the computer, and then the system will recognize patterns according to these rules and perform the specified task. After completing the recognition pattern data input into the computer, ML can independently carry out such commands as:
- find/extract/summarize the data needed
- predict the course of action based on the data analysis
- calculate the probability of this data appearance while searching for it
- adapt to the search processes and changes in patterns on its own
- optimize the processes based on patterns it recognized
Much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations. — Jeff Bezos about ML on amazon.com
Today, ML is being applied in several industries and machine learning in Fintech is no exception here! Fintech-based AI-powered apps are predicted to be of approximately $7 million value by 2022. Why and how exactly is explained below. Do read on!
Machine Learning in Fintech
Fintech is an industry still being “under construction”. With the technological pace and daily innovations, Fintech sets goals that require specific solutions. Hence, ML being the core of AI is the exact disruptive technology that can meet the goals of the financial industry. How? By increasing the level of accuracy, accelerating the existing financial processes, and applying different methodologies to escalate positive results. Some of the needs of machine learning for the banking industry are:
- to increase customer experience
- to propose cost-effective solutions
- to have the ability of data integration within the real-time frames, and
- to enhance security levels
Gradually adopting machine learning in financial services makes the industry turn into an engaging environment for the new generation of tech-savvy customers. Approximately 50% of the fintech market has shifted towards artificial intelligence in finance and banking on a global level. And this number is not final.
Machine Learning Solutions in Fintech
As machine learning penetrates fintech with great speed, it proposes newer and newer features to help the financial industry get ahead of time. Machine learning use cases in finance are:
- Algorithmic Trading and Wealth Maintenance
Strategic or algorithmic trading and wealth management are some of the most important elements of financial literacy and machine learning projects in finance. Being able to maintain profits and save money as well as making strategic investments that can blossom twofold in the future is great knowledge to have. One of the best examples of financial intelligence is the electronic wallet, which aims at tracking your expenses and analyzing your shopping behavior. Hence, by receiving information according to your spending, the wallet predicts your future expenses and shows sectors, where you overspend. Quite a smart way of teaching financial literacy and a convenient handy app! The other example, which belongs to algorithmic trading is an app that helps predict successful investment deals. Here, the Citi Private Bank (USA) is promoting machine learning investment banking and shares its investors’ profiles among its customers. Anonymously, of course. And it works, as fresh investments and business’ deals are always on the way!
- Forecasting Future Trends
With the help of machine learning algorithms that analyze financial patterns, learn data, and create knowledge based on this data, sales may be improved, resource usage optimized, and operations should become more efficient. Any business can thrive gathering huge amounts of data to make better financial trend predictions and promote customer satisfaction. According to Harvard University, by analyzing historical data over a certain period, ML learns about the customer’s budget based on particular economic indicators and creates better solutions for business performance improvement. Having complete knowledge of the financial market business beforehand makes forecasting potentially cost-effective!
- Automated Customer Services
Before automating customer financial services and applying machine learning in financial data, all of them were done manually. Hence, it took longer to respond and resolve customer issues. Besides, in the hectic environment that required fast solutions the probability of human error was higher. Thus, a big need to customize fintech, make it safer and better to meet customer satisfaction, introduced such AI and ML technologies as chatbots and AI interfaces. These AI/ML-supported features help by giving specific advice online and, this way, decrease the costs spent on staff augmentation. AI aims at automating processes done by humans and making them outstanding. Automation of customer services saves costs and time as well as reduces human errors. No more ambiguous pieces of advice, only better support and higher demands for it! What is more, by making a manual helpline automated, $1 trillion of operating costs can be cut with the help of machine learning applications in finance. Also, machine learning in banking is used to optimize communication with customers. This is done with the help of natural language processing (deep learning algorithms). The Swedbank has created “Nina”, the natural language processing (NLP) chatbot to solve contact center overloads – the issue of 2 million customer calls annually.
- Prevention of Cyber Fraud
Fintech is supposed to be convenient for its users and at high speed. However, whenever there is talk about money and personal information, there is always a place for fraud. To decrease the level of fraud and eliminate it, the fintech industry invests in AI and ML immensely to stop transactions using fraudulent methods. The benefit here lies in ML’s ability to react faster and analyze large scopes of data. If there are strange patterns and suspicious activity, ML can recognize them and learn that such behavior will do only harm. AI and ML block unnatural behavior and warn customers about potential hazards. So, software development companies are more than eager to build secured fintech apps using machine learning technology and artificial intelligence in banking. With the help of these two technologies, financial services become safer every day!
- Customer’s Risk Profile
Profiling customers and understanding their needs is one of the machine learning projects in finance and in great demand now. However, detecting risky profiles isn’t just about customer needs. It’s rather about banking security and wise, predicted decision-making. Risk profile detection and segmentation are also done based on customer banking activity to ensure the person is trustworthy, and the history of loans is successful. This way, the bank can approve loan applications of any customer, whose banking history is “crystal-clear”. The patterns of the services customers choose or the financial activity they do online can be traced in real-time with the help of AI and ML. What’s more, banks might even trace the patterns of social media usage, web browser history, geo-location pins on maps, and other metrics used when questions of trust arise. Thus, the fintech industry tries to automate customer risk evaluation techniques by AI/ML tools and detect creditworthy customers effectively.
- Clear-Cut Decision-Making
Right decision-making in the financial sector is very important as management decisions can be crucial both for the employees and the customers. By implementing AI-powered technologies and machine learning projects in finance, fintech benefits right away. ML has the potential to effectively analyze numerous data sets and propose the best-predicted outcomes to cut costs and save money. It makes organizations decision-solving effective enterprises.
I predict that, because of artificial intelligence and its ability to automate certain tasks that in the past were impossible to automate, not only will we have a much wealthier civilization, but the quality of work will go up very significantly and a higher fraction of people will have callings and careers relative to today. — Jeff Bezos on AI for CNBC
What tasks have software development teams dealt with while building a web application for the fintech industry? Firstly, the main goal is to substitute manual work with automatic optimization. It can be automatic generating of sheets with exchange rates data or automated analysis of fluctuations in cryptocurrency prices. Second, the development team has to implement financial strategy within code and make hourly updates as soon as world major stocks update info about rates. Thirdly, it is the most critical task to build logic for forming orders or arranging deals with investors or brokers. The faster the deal management goes, the more chances to get a bargain. Besides, engineers should have a profound understanding of trading, stocks, currency diagrams, or financial operations. With a particular background knowledge, the team doubles success to carry out the project without overworking and stressing about it. You can see the scope of responsibilities, tasks, and challenges that teams face during delivering a high-level trading platform in the case study. Discover how Trading Automatization Platforms are built by experts.
To conclude, artificial intelligence in finance and banking and machine learning are the bright future of fintech services. They will not only put the financial industry one step ahead of the other industries but will polish fintech algorithms and techniques to achieve even better automated processes. Fintech should, by no means use AI and ML as they make banking and trading faster, easier, predictable, and more efficient. Artificial intelligence and Machine learning are the technologies of tomorrow being used in fintech and advanced with cutting-edge solutions today!