Even though digital finance was already well-established before the onset of the coronavirus pandemic, the events of the past year have catalyzed a rapid transformation of the sector. The demand for online banking services continues to soar, with the adoption of artificial intelligence (AI) and machine learning (ML) being among the key drivers of growth and sustainability in the industry.

According to a study by Mordor Intelligence, the value of the financial AI market reached $7.27 billion in 2019. It is expected to reach almost five times that over the next five years. Among the many exciting new trends driving change in consumer and business finance is the use of AI to streamline client experiences, automate routine operations, and reduce fraud.

Here is an overview of five of the most pervasive AI trends in Fintech to watch for:

1. Automated Loan Approvals

As people struggle to recover their businesses and livelihoods in the wake of the pandemic, the demand for financial assistance will continue to soar. Consumers are looking to kickstart their return to normal life and financial wellbeing, while businesses are doing everything they can to stay afloat and thrive now and in the years ahead. These market trends have driven a surge of interest in the ‘just-in-time’ lending paradigm in particular—one that cannot be made possible through manual operations alone.

One of the biggest challenges for lenders has long been the amount of work and time it takes to evaluate and approve loan applications. Manual underwriting is an undeniably laborious process, but one that can be automated to a large degree through the use of specialized AI applications. By performing real-time analysis, AI can automate approvals for low-value loans and assist in the evaluation of larger transactions, such as mortgage applications.

Recently, we spoke with James Taylor, a leading expert in decision management using business rules and machine learning. He used a related example about building a predictive model related to loan defaults. Read more and view our conversation here.

2. Fraud Detection

Fraudulent transactions cost the U.S. economy hundreds of millions of dollars every year, with fraudulent wire transfers alone accounting for an annual loss of $439 million. In 2019, there were more than 270,000 instances of credit card fraud, more than doubling over the previous two years. For financial service companies, this is an area ripe for artificial intelligence and machine learning solutions, with roughly 26% of AI venture funding in the banking industry targeting fraud and cybersecurity—more than any other use case. 

Given the enormous scale of today’s financial operations, it is impractical to manually review every transaction for suspicious or potentially erroneous activity, and rules-based systems can only get you so far. But, by using AI to monitor transactions in real-time, financial companies are able to take a deeper and more nuanced approach to detecting loan, payment and account-opening fraud, enabling a quicker response with greater accuracy and fewer people. 

One of the AI challenges that will be interesting to watch, is the issue of explainability, especially in a regulated industry such as banking. As Ian Barkin recently shared, “you can't go to the regulator and say, ‘well, we prevented this person from making this transaction or opening this account because our ML algorithm identified this fraud accurately, but we just don't know why it was accurate.’ ”

3. AI-Assisted Customer Support

Since the rise of digital finance, customer expectations are changing and increasing. People expect lightning-fast responses and are accustomed to doing their banking at all hours of the day, including evenings and weekends. As such, financial institutions need to be available around the clock to provide answers and facilitate everything from transactions to loan approvals. Call centers and customer service teams frequently find themselves under enormous pressure as they manage large backlogs and strive to deliver the experience that people expect. And, cost pressures often prevent the hiring of additional staff.

While the high-touch nature of many financial services still requires in-person consultations, there are many things that AI-powered chatbots can do perfectly well. For example, if a client wants to know how to make a simple transaction or check their balance, there should not be any need for human intervention. Chatbots can deflect tickets from customer service teams to free up time for support teams to focus on more complicated requests, leading to a better banking experience. Plus, chatbots are available 24/7 and don’t need to take days off!

4. Voice-Powered Conversational Banking

Voice-enabled devices with smart assistants like Amazon Alexa or Apple Siri can save time and make everyday tasks more efficient. In fact, natural language processing (NLP) technology has evolved to the point of making it a reliable way to carry out basic banking operations, just like chatbots but by using a voice interface. While some people may currently be wary of using voice recognition and commands for banking, as voice-controlled interfaces become more prevalent in everyday life, this technology will become more common in financial services.

Despite the challenges around consumer trust, fintech firms have made significant strides in delivering voice-powered banking solutions, especially in light of the increasing demand for contactless payments due to the pandemic. Again, similar to chatbots, voice-powered solutions are being used to deliver a better and more personalized customer experience, increasing satisfaction while reducing costs. So much so, that this type of technology has moved from a “nice to have” status to a “competitive differentiator.” 

5. Next-Gen Algorithmic Trading

Introduced in the 1970’s, algorithmic trading executes stock market trades using pre-determined, rules-based instructions, and it has been in wide use by large trading firms and institutional investors for decades. More recently, AI has been transforming the trading desk and helping to crunch millions upon millions of data points in real-time, all while learning and picking up on insights that more traditional statistical models could not see.

AI has come a long way in bringing algorithmic trading to the masses in recent years, with consumers now having access to user-friendly mobile apps that enable them to trade in stocks and shares with the assistance of AI-powered decision-making.

Today, between 70 and 80 percent of trades are carried out algorithmically. However, adoption of AI and machine learning is taking algorithmic trading to the next level by reducing risk and driving more informed decision-making. An AI-based system is able to more quickly adapt to a changing trading environment (e.g., the pandemic) as well as pick up on, and account for, anomalies. This is because a machine learning model isn’t static—it is constantly taking in new data and learning from it. For example, NLP solutions can analyze the content of industry magazines and financial reports to detect trends and adapt quickly to execute the appropriate trades in the market.

Our managed workforce serves as an extension of your team by annotating, processing, and transcribing financial data at scale so that you can focus on building the next generation of AI solutions for fintech. Speak to one of our experts today to learn more.

Download a copy of Financial Service Company's case study.

Data Transcription Data Labeling Finance and Insurance AI & Machine Learning

Get the latest updates on CloudFactory by subscribing to our blog