Artificial intelligence applications such as machine learning are the essential technologies put forth by banks to develop more advanced and efficient banking methods with a competitive edge. It turns difficulties into lucky pretexts for making money through innovations like digital trading, risk management, fraud detection, and regulatory compliance among others.
The forecasted worldwide market for machine learning in 2020 is approximately US$158 billion with a compound annual growth rate of 18% which will lead to a stunning $528 billion in 2030. This makes machine learning one of the most significant instruments used by financial institutions for process optimization.
Machine learning is fundamentally helping to address the sectors’ pressing problems. Fraud, for example, will not be limited just to insurance crimes alone or in cryptocurrencies only. What to hear more is that robust standards of compliance go without bounds across the spectrum. Machine learning in finance proves to be diverse in all industries and business types, where using these concerns as tools to gain has been greatly discovered.
It would be easy to assume that ML, which made a grand entrance into public awareness a year ago, is just the latest trend of the day if the year of AI, its most striking public appearance, were to second.
Nonetheless, that is as impossible as not a single word will be true. Although it may not be as fashionable anymore, the whole concept of machine learning is a very profitable thing today. This will be done to enable deep learning to be optimized for training AI. FinTech is no exception.
1) Algorithmic trading
Much of the business world’s success is derived from highly effective algorithmic trading programs to mechanize how they invest and increase the number of transactions they can process. The institutional field of investing involves the execution of trading instructions without having to make any manual trades as these tasks are automatically performed following human-made trading directives which are machine learning algorithms based. Since many of the transactions done by the ML are dissimilar from the frequency done manually, therefore, almost every big and small financial agency is investing in algorithmic trading.
2) Preventing and detecting fraud
In every Fintech machine learning solution, actions are built to constantly learn and adapt to the new fraud patterns that are devised, making both your operation and your clients safe. This is different from the classic precision rule-based fraud detection.
Machine learning algorithms can detect many types of suspicion and complex faults with a sufficiently accurate outcome by retrieving and analyzing vast datasets.
IBM shows that ML can find fraud occurrences in any transaction in real-time, hence banks can lessen their losses and take action immediately if danger presents itself.
The use of ML for FinTech solutions allows their systems to detect several fraud patterns, including identity theft, credit card fraud, payment fraud, and account takeovers. Therefore, instead of a piecemeal approach, the entire system architecture can be made robust to various threats.
3) Regulatory compliance
RegTech (regulatory technology) solutions are not only the most common machine learning application cases but also can be treated as one the most popular machine learning in banking.
ML algorithms can spot correlations between recommendations since they can master vast regulatory books, thereby they can read and learn from them. Consequently, cloud solutions with embedded machine-learning algorithms for the finance industry will be capable of figuring out government regulations and providing suitable adjustments correspondingly. Banks can also look at their information about transaction happenings to observe peculiarities too. In this manner, AI can exactly make sure that the company’s customer transaction records meet all the applicable regulatory requirements.
4) Data processing
FinTech software development companies might benefit from technologies like OCR (optical character recognition) and other automated document processing systems to get real-data-driven insights as machines learn to manage millions or billions of spreadsheet cells or database rows that they need to process and analyze.
It helps to minimize the level of data analysis depending vastly on a large team and the related costs by automating repetitive processes such as credit portfolio loan application processing, KYC checks, and regulatory compliance.
Automating repetitious processes
Automation is likely the most obvious machine learning benefit for FinTech, having several advantages. To validate client information in real-time without requiring manual input, for example, machine learning algorithms can expedite the customer onboarding process.
Furthermore, by doing away with the necessity for human data entry, automating the reconciliation of financial transactions saves time and money. The rest of your team will benefit from automation in more subtle ways. ML-driven automation removes the tedious work that prevents your professionals from working on more important projects.
Use case example of machine learning adoption in the Fintech Industry
Machine learning technology is bringing benefits to the Fintech web development industry. The case below will show you what is happening around the globe.For example in 2022, Credgenics, an Indian SaaS startup with focus areas on legal automation and debt collection, has managed to become the largest information collection system in the region for the retail loan segment now; it has aggregated more than 40 million retail loans in the loan book of $47 billion.
Over 100 clients from the enterprise customer segment have been able to save up to 60% of costs and reduce delinquency cycles by over two-thirds as well as have experienced 50% higher first-time resolution and collection rates thanks to the machine learning algorithms behind these solutions.
Conclusion
While the faster process of achievement in technology may not be exclusive to Fintech, Fintech still ranks among the primary drivers of technological advancement. Without the technological force, FinTech as we know it may shatter and never stand in the same place again. It is the FinTech industry that brings it to its tech pioneers’ competitiveness among all the other industries. To some, an improved experience in terms of ease of use and convenience is, after all, the very reason they started FinTech in the first place. Our autonomous skilled personalities are completely with reality.
