Intelligence artificielle et apprentissage automatique dans l’industrie de la technologie financière

We may have come to a point in history where the needs of the financialindustry are over the heads of mere humans. Risk management alone requiresautomation to process the daily threats constantly attacking organizations andcustomers are quickly learning that there are moments when speaking to a realperson is no longer preferable if only for avoiding long wait times. AI andmachine learning are becoming increasingly present in any and all conversationsabout FinTech; some would argue that AI will be the most widely adopted newtechnology 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 theintroduction of the chatbot. Head to your bank's website and you'll probably bemet with a chatbot eager to help you by answering questions and cutting down onthe flood of calls to their customer service department. Organizations lovethese little pieces of AI because they cut staffing costs and reduce wait timesfor anyone actually needing to speak to a human.

AI gets a little more complex when we start seeing it in other customerservice applications like predictive analytics, credit score data and wealthmanagement, but every area is becoming more and more sophisticated because ofmachine learning. As AI literally becomes more intelligent, the FinTechindustry is able to rely on its ability to advise as well as protect.

Trading, especially high-frequency trading, and money management makeuse of AI and machine learning through better data modelling, thanks toGenerative Adversarial Networks (GANs) and algorithms. This technology enablesmodelling of realistic market behaviour, something that in the past hasn't beenpossible and can be used to make predictions to impact investing decisions. Theability to function with unstructured data is a game changer that producesresults 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 maystart to shift a little as results with robo-advisors and algorithmic tradingkeep getting better. Financial advisors would do well to understand and adoptthis technology as tools they can use to better their service as opposed toseeing them as competitors. Robo-advisors can do the heavy lifting with riskassessment, data aggregation, asset allocation and reporting, making financialadvisors more efficient and better informed. Algorithmic trading removes theneed to constantly monitor the markets, all while keeping emotions out of themix, sticking to set rules and reducing slippage.

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

Machine learning application: digital footprint creditscoring

One of the interesting ways that AI and machine learning have popped upin FinTech is in lending and credit scores. Some lenders are usingnon-traditional data for credit scoring and using AI to find it. Lenddo,for example, uses a borrower's digital footprint, like social media activityand geo-data, to analyze their behaviour, assess their risk and establish acredit score. They then pass this score onto lenders to secure credit forindividuals 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 todetect threats and fraud. The smarter AI becomes, the better-protected customers'information is and the easier it is to ward off cyberattacks. The beauty ofmachine learning is that it is constantly improving and constantly adding toits knowledge base.

FinTech AI and machine learning adoption

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