FinTech companies are vigorously disrupting the financial and banking sectors with machine learning start-ups. Their bottomless capabilities of AI have made banking and finance have made institution operations as AI-critical. According to Deltec Bank, Bahamas - “AI is proving to drive companies to be more secure, better streamline operations, deliver greater accuracy, utilize analytics to make decisions, make future forecasts and so much more.” Machine Learning is now the hotbed for FinTech, and developed use cases picked up by institutions shows no signs of slowing.
AI FinTech is anticipated to help financial institutions save more than $1 trillion in operations, with $490 billion saved through resources and security. Machine Learning AI technologies has helped IBM to save $1 billion. These are some of the forecasts of how financially impactful machine learning can have. On top of this, customers are more willing to trust machine learning instead of using human interaction.
FinTech is taking advantage of developing technologies to meet the demands of the financial customer. Here are some of the popular use cases of Machine Learning.
- Fraud Prevention and Protection
Financial service companies must protect their clients from any fraudulent activity. Fraud detection applies to many other industries, not just finance, such as banking and insurance. Identifying fraudulent activity is difficult for a human to perform. This is an outdated approach that can now be replaced with a sophisticated solution that identifies and analyses potential fraudulent transactions with data. The benefit is that the algorithm would be developed to ensure a highly accurate response. In comparison to rule-based fraud detection, machine learning-based fraud detection will detect hidden or irregularities of correlations in real-time. Software providers such as Feedzai and Ravelin are popular machine-learning software that performs fraud prevention efficiently.
- Stock Market and Investment Predictions
Machine Learning delivers advanced financial marketing insight. With machine learning, market changes can be forecasted, identified and predicted earlier with machine learning than traditional investment methods. Machine learning develops chatbots and virtual assistants to perform automated advisory or admin tasks. This goes for financial institutions such as JPMorgan and Morgan Stanley, who currently invest in auto investment advisors through machine learning. The algorithms predict stock market pricing, economic status and revenue margins etc.
- Customer Experience
Improving customer experience is one of the key successes taken from machine learning. “Customer experience is the root of the overall business and digital transformation and the use of big data offers an opportunity to develop machines that efficiently deliver exceptional customer experience”, says Deltec Bank, Bahamas. This eliminates the concern of internal resourcing, and Natural Language Processing (NLP) assists in developing an intelligent interaction with the computer and human languages through analysis and processing of large amounts of data. Through virtual assistants, customers can expect an accurate response to their queries through a prescribed set of rules and configurations to improve the human customer experience interaction.
- Process Automation
Financial companies are well-versed in the use of spreadsheets. This outdated approach to does not maximize operational efficiency. Machine learning can transform operational processes by performing automated admin tasks, analysing data through the interpretation of trends and deliver intelligent responses from queries. The predictability of process automation is dependent on data accuracy. This is known as robotic process automation (RPA), known to solve complex business and operational problems.
The apparent urge for machine learning is getting higher by the day due to its evolving ability to improve strategy and operational efficiency in finance. FinTech is the new term given to perform machine learning services such as financial analytics and forecasting, customer services and other use cases defined above. Machine Learning in finance is now not a choice, but a necessity.
To sum up, the FinTech industry is benefitting from machine learning technologies for a wide range of purposes, and financial institutions have reached the point where they see the benefit in utilizing the power intelligence to streamline operations. So much that financial institutions depend on it. AI and machine learning are offering a new paradigm to the way financial institutions operate and the cutting-edge technology and use cases explored are transforming how finance does business.
Disclaimer: The author of this text, Robin Trehan, has an Undergraduate degree in economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.
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