AI and Machine Learning in the FinTech Industry


We may have come to a point in history where the needs of the financial industry are over the heads of mere humans. Risk management alone requires automation to process the daily threats constantly attacking organizations and customers are quickly learning that there are moments when speaking to a real person is no longer preferable — if only for avoiding long wait times. AI and machine learning are becoming increasingly present in any and all conversations about FinTech; some would argue that AI will be the most widely adopted new technology in the industry’s coming years.

What AI and machine learning can look like in FinTech

The simplest, and most visible, application of AI has been the introduction of the chatbot. Head to your bank’s website and you’ll probably be met with a chatbot eager to help you by answering questions and cutting down on the flood of calls to their customer service department. Organizations love these little pieces of AI because they cut staffing costs and reduce wait times for anyone actually needing to speak to a human.

AI gets a little more complex when we start seeing it in other customer service applications like predictive analytics, credit score data and wealth management, but every area is becoming more and more sophisticated because of machine learning. As AI literally becomes more intelligent, the FinTech industry is able to rely on its ability to advise as well as protect.

Trading, especially high-frequency trading, and money management make use of AI and machine learning through better data modelling, thanks to Generative Adversarial Networks (GANs) and algorithms. This technology enables modelling of realistic market behaviour, something that in the past hasn’t been possible and can be used to make predictions to impact investing decisions. The ability to function with unstructured data is a game changer that produces results the financial industry are getting excited about.

How financial advisor roles may change

Most people still trust a real person with their finances, but this may start to shift a little as results with robo-advisors and algorithmic trading keep getting better. Financial advisors would do well to understand and adopt this technology as tools they can use to better their service as opposed to seeing them as competitors. Robo-advisors can do the heavy lifting with risk assessment, data aggregation, asset allocation and reporting, making financial advisors more efficient and better informed. Algorithmic trading removes the need to constantly monitor the markets, all while keeping emotions out of the mix, sticking to set rules and reducing slippage.

There’s no doubt that some of the tasks a financial advisor would be performing on a regular basis will be taken over by AI, but at the moment, complicated wealth management still seems to be sticking with financial advisors. The future is probably somewhere in the middle. Robo-advisors make investing more accessible, but financial advisors are relied upon for more holistic planning and advice. Some consider robo-advisors only for passive investing, but that may just be where it starts, not where it’s headed. As machine learning shows that it can predict with better accuracy, robo-advisors will be leaned on more heavily.

Machine learning application: digital footprint credit scoring

One of the interesting ways that AI and machine learning have popped up in FinTech is in lending and credit scores. Some lenders are using non-traditional data for credit scoring and using AI to find it. Lenddo, for example, uses a borrower’s digital footprint, like social media activity and geo-data, to analyze their behaviour, assess their risk and establish a credit score. They then pass this score onto lenders to secure credit for individuals that may not yet have a traditional credit history.

The fight against fraud

Cybersecurity is never more relevant than in the financial industry. Machine learning, specifically GANs, are able to continually train systems to detect threats and fraud. The smarter AI becomes, the better-protected customers’ information is and the easier it is to ward off cyberattacks. The beauty of machine learning is that it is constantly improving and constantly adding to its knowledge base.

FinTech AI and machine learning adoption

As with most technology, there seems to be a soft adoption by established organizations before the big disruption happens, but AI and machine learning in FinTech is already well on its way. 95 percent of respondents in the SAP Hybris survey felt that their usage of chatbots would grow in the coming years – and with natural language processing, there may be a day that customers don’t even notice that they are chatting with AI. In FinTech based companies, like Lenddo, AI and machine learning are at the centre, setting an example for the industry and demonstrating how this technology can be used to better serve their customers.