Historically, the fintech industry has been among the earliest Artificial Intelligence adopters. As of today, AI is becoming the main driver of digital transformation in traditional finance and the golden standard for fintech services. In fact, according to a recently published report authorized by the World Economic Forum and conducted in partnership with the Cambridge Centre for Alternative Finance, by 2022, we can expect mass adoption of AI in the financial industry on a global scale. In other ways, legacy financial services will become obsolete in as little as two years.
AI and Data analytics go hand in hand, and nascent technologies like Machine Learning, Neural Networks, and Natural Language Processing, continue to improve data-crunching capabilities for financial industry players.
So, how exactly do these technologies apply in fintech? In this article, we will explore how AI and Data analytics add value to financial services, account for better customer experience, and help generate revenue.
Examples of AI and Data Analytics in Fintech
As per the WEF report, the finance sector executives increasingly perceive AI as their strategic asset, and the scope of application of AI continues to expand. The fintech business domains which are actively leveraging AI include:
– Generating new revenue streams by launching new products and services
– Process re-engineering and automation
– Risk management
– Client acquisition
However, the leaders in AI adoption invest heavily in the digitization of Customer service, making it a priority when it comes to implementing AI and analytics.
Successful AI adopters target their digital services to tech-savvy millennials, who happen to be the majority of today’s world population. The benefits that the use of AI brings customers are as follows:
Personalization: by using AI to make sense of customer data fintech companies can tailor their financial offering to customer’s individual needs. The banking app, for example, can track users’ demographics, history of spendings and transactions, and offer them financial products based on their personal needs and preferences.
As per recent findings of Boston Consulting Group, by adopting a personalized approach to customers, banks can win up to $300 million for every $100 billion of their asset funds.
Instant tech support: using Natural Language Processing, chatbots can process human queries and give immediate answers to the frequently asked customer questions. The rate of query resolution increases, and so does customer satisfaction.
Some banks, however, go as far as building advanced virtual assistants integrating technologies like personalized data analytics and natural voice recognition. For example, the Swiss investment bank UBS has launched its support service based on the use of voice interface. The clients communicate with digital avatars who provide answers even to the complicated questions using Natural Language Processing (NLP).
Loan assignment and credit scoring: AI also accounts for facilitating the loan assignment procedure for clients. Customers no longer have to wait until bank workers assess their creditworthiness. Based on data they collect from various sources, including online shopping, social media, and customer’s internet activity, Machine Learning algorithms evaluate if an applicant qualifies for a loan. These algorithms are self-learning. As they improve over time, they can predict the client’s future spendings and possible changes in creditworthiness.
Fintech players are also building solutions to help customers improve their credit scores. Payoff, for example, assigns personal loans that help customers pay off their credit cards and improve their financial ratings.
Secure transactions and data protection: Furthermore, by analyzing real-time data, AI algorithms help detect non-typical activity and protect user’s data from hijacking and ensure safer transactions. ML algorithms track typical user behavior patterns. If any deviation from these patterns appears suspicious, they automatically protect customer accounts and data from hacking and fraud.
AI Adoption Benefits for Business and Customers
The benefits of AI adoption are difficult to overlook. Even on a superficial level, it’s obvious that the acceleration and precision that AI and data analytics bring to the table will account for better business outcomes. Focusing on the advantages, companies stand to benefit from data-driven management and predictive analytics helping them make better business decisions. Enhanced security and data protection, are yet another benefit, along with automated customer service helping companies achieve better efficiency with a smaller workforce.
For customers, the adoption of AI and data analytics in fintech offers tangible benefits as well. Apart from the enhanced personalization and data protection, AI and data analytics reduce the overall costs of financial services. Another benefit is financial inclusion since technologies help bring financial services like consumer loans and insurance to world regions with a vast number of the unbanked population. The COVID-19 pandemic has stressed the importance of contactless communication, and here’s where fintech solutions have yet another advantage over the brick-and-mortar finance.
AI in Fintech in 2020 and Beyond
Admittedly, the COVID-19 crisis has had a detrimental effect on some of the fintech startups. In the long run, however, the shift to the ‘next normal’ will entail the accelerated adoption of new gen-tech. Moreover, in the foreseeable future, the quality of fintech services will be the defining metric for their competitiveness. Hence, AI and data analytics adoption rates are set to grow. More specifically, here are the AI trends that we expect to witness in 2020 and beyond.
Mass adoption of AI by financial organizations
The WEF report indicates as much as 85% of financial companies are already using AI on some level. Most of them plan to increase their investments in AI R&D in the near future, focusing on process innovations and customer services.
Fintech companies shifting the focus of AI initiatives
Fintech companies, on the other hand, initially focused on customer experience, will be looking for means to expand their portfolio of offerings. In 2020 and beyond fintech companies will be leveraging AI to discover new business areas and launch new products and services.
Data-driven cybersecurity becoming mainstream
One of the implications of the digitization of the financial industry is the increasing number of security threats. In a bid to protect customer’s data and financial integrity, finance industry players will be investing more in robust data-driven security systems based on Machine Learning.
Payment technology getting more sophisticated
Not only will self-learning algorithms help secure digital payments: the use of AI in payment systems will also help build payment technology solutions that will enable businesses to benefit from payment data. By using ML to pinpoint the trends in transactions – for example, seasonal decreases, companies can finetune their business strategies, manage inventory and come up with unique offerings for each customer.
The rise of autonomous AI-driven asset and investment trading
In trading, the advancements in ML and Deep learning will account for the emergence of trading algorithms created with no human supervision. On top of that, AI will allow for multiple transactions to run simultaneously, accelerating the trading process and raising its efficiency of asset and investment trading to an unprecedented level.
The advent of Fintech 2.0.
As early as in 2015, fintech enthusiasts have been hailing the advent of Fintech 2.0. and contemplating what the future of finance will look like. Today’s view of Fintech 2.0 implies convergence – the integration of business, technology, finance and even healthcare into a single entity with interconnected segments. These segments will include Regtech, Insurtech, Investech, Martech and a number of other segments. By using AI and due to the interchange of data between each segment, companies will bring truly personalized services to businesses and customers.
Early AI-adopters will have advantage over laggards
As the demand for AI in financial services grows, the leaders in AI adoption will be shaping the market for AI and data analytics services, to the point when they can sell their solutions to other finance market players. For early AI-adopters, selling AI-as-a-service on B2B markets may act as another source of revenue, while late adopters will end up in a less favorable situation.
In spite of the generally optimistic outlook, the rate of AI adoption in fintech is halted by factors like the overall quality of data within organizations, lack of full access to data, legacy systems standing in the way of full-scale AI and data analytics implementation, and lack of buy-in from employees and management.
Financial executives perceive the scarcity of AI and data analytics talent as the top second obstacle to AI implementation. With this regard, offshore and nearshore AI solutions development might prove a viable alternative in the long run.