How is Machine Learning influencing the Best of the FinTech sector?

Humans have always been fascinated by the concept of Artificial Intelligence. Much before it came into existence, the idea of AI can be seen in sci-fi novels or movies. And now our lives are unimaginable without the use of AI. Initially, it evolved as a concept to handle bizarre mathematical calculations, robot-based advice counselors, and financial mates. And it forms an integral part of the most demanding and fast-paced industries.

While AI is the forerunner for the futuristic world, its’ pivotal role in the financial sector proves how it’s changing the business landscape even in conservative areas. In banking, only AI or its progeny machine learning (ML) based application is predicted to save $447 billion by 2023. While FinTech anticipates a growth by 19% in their workforce due to AI in 2023. Although the adopter does not have any particular modus operandi, 64% of them believe in becoming the most significant users of this switch. Thus confirms AI as a crucial business driver across the industry in the short term.

In 2019, Deloitte gauged 206 US financial services executives to get a better understanding of how their companies are using AI technologies, and the impact AI is having on their business. Out of these, 70% registered as clients employing machine learning, and 60% are using Natural Language Processing (NLP). Almost half of the total participants have implemented a broad, thorough, companywide strategy in place for its adoption. Their goals include delivering revenue and cost gains quicker than competitors. This looks encouraging, yet a lot needs to be done.

In integration with Machine learning, AI can help FinTech firms by preventing fraud and money laundering. For every dollar lost due to fraudulent activities, the compensation charges are way more. Machines equipped with ML can recognize suspicious activity and help to cut the expenses of investigating the alleged money-laundering schemes. The algorithmic approach of ML has boosted automation of the trading processes and led to smoother trade settlements. Plus stock markets, financial crises, and investments prediction are now more accurate. ML studies historical market behavior and can figure out an optimal market strategy. Other than the benefits mentioned above, machine learning enables stricter network security to minimize breaches, more opulent customer service via chatbots. It also offers customized solutions for improved risk management. Investments firms are opting for ML to identify risks and to help set premiums for high profits and lesser loan underwriting risks for banks.

Although forecast and predictive analysis using machine learning are accurate to a greater extent, it is not mandatorily right tool for every situation. Company leaders must know the difference and scope of advanced analytics, ML, and AI. Most of these estimates are drawn from more straightforward tools like exponential smoothing. It begins with the experimentation of a local set using a few ML models. Then it is further built upon using past data and validations and comparative procedures. However, it is true that without machine learning, AI being the only driver cannot bring the said benefits. Also, it is believed that as the adoption of blockchain and cryptocurrency expands, finance companies are likely to witness a transformative boom. The earlier they recognize the vitality of incorporating such practices, the quicker they will have a 10% fiscal growth edge over the late adopters. Rather than shying from modern technology features, it is best to invest in what Forbes befittingly calls as ‘Drug for Digital Transformation’.