From the home to the office, the impact and innovation of artificial intelligence (AI) can be seen in nearly every aspect of everyday life. According to a study, 63% of worldwide customers have used one or more fintech platforms. Because of these trends and their digital ecosystems, fintechs have particular advantages to the incredible advances in AI and machine learning (ML).
There are so many ways for companies in the financial sector to leverage artificial intelligence, from analyzing and managing large amounts of data to providing valuable insights. Machine learning techniques in fintech are able to collect and analyze extremely large data sets in real-time, and more importantly, have the ability to learn from the results and make improvements.
An AI-powered, personalized approach to finance is highly beneficial to both the company and the client. Along with making day-to-day services run more smoothly, AI technology also helps companies approach unexpected issues with creative new solutions. As the demand for online banking services continues to increase, the adoption of AI and ML is becoming key to growth and sustainability in the fintech industry. Here are seven ways that artificial intelligence will change fintech in the near future.
1. Stronger Security
Over the years, artificial intelligence has provided massive boosts to data security in the financial technology sector. Many banks and fintechs provide support to their customers via chatbots enabled by AI. These AI models can offer a variety of solutions designed to enhance security precautions such as resetting forgotten passwords and granting additional access.
Another way AI is improving security in finance is by way of speech, facial, and fingerprint recognition. This added layer of security makes it more difficult for trespassers to bypass than traditional passwords. While these types of processes are still being perfected, it is possible that AI-backed security solutions will completely replace usernames and passwords in the near future.
As the world becomes more online and less in person, cyber security is becoming more and more important. Fintechs, banks, and other financial institutions are most at risk as they handle billions of dollars of money every day. Keeping on top of the innovations in AI and machine learning ensures your company will have the strongest security available.
2. Greater Fraud Detection
From credit card and loan application scams to false insurance claims and fraudulent wire transfers, deceptive financial activity has continued to increase over the past few years. These counterfeit banking transactions can cost individuals and businesses millions of dollars. In addition to financial losses, companies must handle the negative customer experience and face potential damage to their reputation.
Much of AI venture funding in the banking industry utilizes machine learning solutions to target fraud and cybersecurity. With such a large scale of financial operations occurring on a daily basis, it is nearly impossible to manually review each and every transaction for erroneous or potentially suspicious activity.
Efficient AI systems can monitor banking transactions in real-time while AI algorithms detect anomalies or unusual patterns deviating from a user's typical spending behaviors. With AI, fintechs are able to respond more quickly to potentially fraudulent activity and with greater accuracy. Superior AI systems can often react to suspicious activity before the fraud actually happens.
3. Improved Customer Service
As artificial intelligence has already made major improvements in customer service for banks and fintechs, clients are becoming more accustomed to receiving quick responses at any time of day. Financial institutions need to be available 24 hours a day, seven days a week, in order to answer questions and facilitate transactions. If customers don't receive rapid attention, you risk losing their business to a competitor.
Call centers are often under pressure to handle backlogs in addition to offering constant support. AI-powered chatbots, virtual helpers, and other AI interfaces help minimize the workload by fielding basic queries and most frequent user problems. This frees up time for the customer service teams to focus on more complicated requests. They can be more attentive to customers' needs and ultimately create a better working relationship.
AI continues to improve fintech customer service with the use of complex sentiment analysis which focuses on understanding the customer’s experience, identifying shortfalls, and training chatbots to address those issues. Communication between customers and fintechs is becoming easier and more accessible with AI-based systems. Happier customers and happier customer service employees culminate in a more successful business.
4. Personalized Banking Services
Large financial institutions have thousands, if not millions, of customers. Providing personalized services to so many unique individuals would be an incredibly daunting task if it weren't for AI-powered technology. Personalization can make clients trust your company more and add to your brand loyalty.
When clients download banking apps, AI algorithms collect and analyze consumer information. Based on the data, relevant pre-approved products and personalized financial advice can be recommended. AI in banking apps can also help clients track individual financial goals and expenses.
Because AI systems are self-learning, they will only get better at reading customer data and providing personalized experiences. AI models analyze voice and speech characteristics and are able to distinguish real patterns from mindless chatter to create meaningful insight. Transcript analysis by AI can even predict the mood of customers and advise agents on solutions to offer them.
5. Enhanced Algorithmic Trading
Although algorithmic trading has been around since the 1970s, it has come a long way in recent years with even more exciting enhancements happening all the time. Algorithmic trading uses AI technology like machine learning to execute stock trades using a pre-programmed set of instructions that analyze data to make trading decisions.
AI has transformed the traditional trading desk by crunching millions of data points in real-time while developing a set of insights that statistical models are unable to detect. Because of AI in fintech, consumers are able to trade stocks and shares via user-friendly mobile apps with the assistance of AI-powered decision-making.
Because a machine learning model is constantly taking in new data and learning from it, an AI system adapts more quickly to a changing trading environment. In the next generation of algorithmic trading, AI in finance will further reduce trading risks with more informed decision-making. Systems will be able to pick up on, and account for, anomalies more effectively, analyze financial reports to detect trading trends, and quickly adapt to execute appropriate trades in the market.
6. Save Money and Resources
By adopting AI-powered applications, fintechs can save billions of dollars in capital, labor expenses, and resources. Procedures performed manually are generally more time-consuming and expensive due to labor costs. AI automation saves time and money by taking over these processes and responsibilities. With AI technology handling smaller inquiries, customer service teams will see a reduction in labor expenses.
Artificial intelligence saves banks money in the front office with personalized insights, in the middle office with anti-money laundering, and in the back office with credit underwriting. With the money saved by using AI systems, banks are able to offer better deals and attract new clients. The overall result is saving money while increasing revenue and profit.
For a bank's day-to-day operations to run smoothly, it is essential there is always sufficient cash flow to meet its short-term operating costs and debt obligations. Data-driven AI and machine learning algorithms enhance capital optimization by maintaining and organizing the near-constant flow of cash records.
7. Automated Loan Approvals
Getting approved for a loan used to take hours, sometimes even days or weeks, and involved piles of paperwork. Thanks to AI-based machine learning and automation technology, loans are being approved faster and more accurately than ever before.
Specialized AI applications speed up the costly and laborious process of evaluating and approving loan applications by performing real-time analysis to make informed credit decisions. AI models go through verification checks, ensure financial status requirements are met, and evaluate the creditworthiness of an individual.
As AI continues to optimize loan approval automation, client risk profiling will become more extensive. While previously based on earnings, repayment history, gender, race, and lifestyle expenses, AI uses a wider variety of data points to create a more accurate view of a customer's financial position. Along with conventional data sources, AI systems evaluate data from collection agency comments, field visits, and campaign data.
Future of AI in Fintech
As far as AI in fintech has already come, the immense possibilities are just starting to be realized. Artificial intelligence systems are becoming more efficient and effective with every update.
In the near future, any fintech, bank, or financial institution that isn't using AI will be left behind. If you want to learn how you can implement artificial intelligence in your company, take a look at BBVA’s success story, or even better take a product tour.