Artificial intelligence (AI) and machine learning (ML) have led the inception and rapid growth of the global financial technology or fintech industry. Industry data suggests that by 2026, the global market size for AI in fintech will account for an astonishing USD 26.67 billion while maintaining a CAGR of 23.17% from 2021 through 2026.
With increasing levels of process automation and digital transformation of financial services, the use cases of AI and machine learning in the finance industry are everywhere, from secured digital transactions to personalized financial advice.
Do you want to know more about how AI is used in fintech? Keep reading this article, then. We will explore some leading use cases of AI and ML in the fintech sector. But before we do that, here is a question for you—are AI and ML the same? Let’s find out.
In This Post:
The Difference Between AI and ML for Fintech
On the web, social media, and even in the news, you will often see that writers tend to use the terms AI and ML interchangeably. Yet, contrary to popular belief and widespread use, those two terms do not carry the same meaning.
Simply put, machine learning is a subset under the wider domain of artificial intelligence. By using artificial intelligence, machines can practice the skill of intelligent decision-making while behaving more like humans. In contrast, machine learning expresses the skills for machines to collect new information and identify patterns without any external supervision from engineers or developers.
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Now, let’s check out some modern-day uses of AI and ML in the financial industry.
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How Are AI and ML Used in Fintech?
The Cambridge Center for Alternative Finance found that more than 90% of global fintech companies are already relying heavily on artificial intelligence and machine learning. The statistic also shows how financial firms are leveraging AI systems. Here, we have listed a few of the uses of artificial intelligence in fintech.
Intelligent Decision Making
Using AI in fintech has enabled financial institutions to make intelligent decisions by analyzing a massive amount of data gathered in real-time from national and global financial markets. The process of collection, computation, and analysis of data happens instantaneously.
Financial advisers and analysts get a clear picture of the financial industry. They can use the data to offer valuable advice on loans, insurance, and wealth management to generate higher customer satisfaction.
Instant visibility and data analysis with artificial intelligence also helps global fintech companies make proactive decisions by anticipating financial sector trends in advance. As a result, financial institutions can propose better quotations for customers based on individual financial goals leading to greater convenience, cost reduction, and a lower workload for all parties.
Artificial intelligence for fintech has given birth to AI-driven chatbots capable of conducting detailed sentiment analysis. Call centers worldwide have to spend a bulk portion of their time and resources by answering a set of frequently asked questions from customers. It is possible to find specific patterns for customer queries and interactions through big data analytics. Fintech companies then use learning to train chatbots with machine learning and artificial intelligence.
As an added benefit, sentiment analysis enables chatbots to answer a set of pre-programmed questions. Instead, it develops a deep understanding of customers’ relationships with financial services. In turn, it leads to a better quality of process automation, eliminating waiting time for customers. For example, when Bank of America deployed chatbots for the first time to interact with customers, the size of their customer base increased more than a million within two months of launch.
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Financial service providers can provide a superior customer experience by using AI for fintech. The main advantage is that it consumes fewer resources to train a chatbot with artificial intelligence than training a person. The situation carries more importance for modern-day financial institutions because governments and regulatory bodies frequently update rules and regulations related to financial services, which are difficult to follow for humans. AI technology directly addressed this shortcoming.
Industry experts estimate that banks and financial institutions will save 826 million work hours in 2023 by introducing automated customer support through artificial intelligence in fintech. At the same time, customers get their queries answered within a fraction of a second, and they do not see any difference with human intelligence.
The increasing use of blockchain technology has led to the popularity of smart contracts and smart wallets. However, with higher convenience for the customers, security concerns for financial services have also multiplied. Javelin identified that financial theft accounted for 56 billion USD in 2020.
However, artificial intelligence has made fraud detection easy for fintech apps by continuously keeping an eye on the entire system and all user accounts to identify and restrict any suspicious activity early.
Illegal activities such as money laundering are also a major financial security concern for financial regulators worldwide, and they spend a lot to deploy effective anti-money laundering mechanisms. ML in fintech helps regulatory bodies detect money laundering activities quickly and take necessary measures.
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By automating frequent insurance management and underwriting tasks, insurance companies can produce a greater customer experience through machine learning. Companies can also prepare detailed reports on customer profiles through credit scoring from various data points. Similarly, using AI in fintech allows customers to access various market data, leading to more informed and data-driven decision-making for choosing an optimal insurance package.
Both the customers and the insurance providers will see a significant reduction in processing time and costs with AI-driven insurance underwriting. Artificial intelligence will use data from various sources, even outside the customer’s medical records, to create links and forecasts.
AI-Powered Financial Assistants
AI in fintech enables the customer support team to train automated digital financial assistance for interacting with customers. The team members can use the saved time to perform more value-adding tasks for financial services. With the ability to use data analytics from internal and external sources, automated assistants also provide a wide array of information to the customers that are difficult and time-consuming for humans to learn.
More interestingly, developers also utilize Natural Language Processing (NLP) for internal processing to help the digital assistants learn customers’ emotional states and facial expressions during interactions. During client onboarding, it is particularly useful because customers do not feel like they are interacting with a programmed bot; as a result, customer satisfaction is higher from the instant communications powered by speech recognition software.
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Predictive analysis has revolutionized the way financial companies make decisions by integrating data science within the process. AI and ML can help companies analyze a wide array of customer and market data, streamlining the overall business development and management process. Every activity, from nurturing leads to converting revenue in financial services, takes advantage of predictive analytics.
With predictive analytics, programmers can also develop algorithms to compute credit scores based on the individual risk profile of the customers and offer financial services accordingly to reduce the probability of incurring bad debts in the future.
Fintech firms can also apply predictive analysis to marketing and branding campaigns. For instance, fintech organizations can use it to proactively evaluate the effectiveness of a branding strategy on the customer's mind.
Wealth Management Advisory
Personalized asset and expense management advisory services are in high demand from customers. However, offering those services to lower net worth customers is not financially lucrative for the fintech companies, as they get their sales commission depending on their net worth. Fortunately, artificial intelligence and machine learning have changed the situation completely.
Fintech companies can now program their apps to utilize AI for providing users with personalized financial advice based on spending patterns and financial conditions. This allows financial institutions to offer services to a much higher number of users without incurring the additional cost for maintaining a larger workforce. As a result, they can reduce service charges to a great extent.
More importantly, users will also benefit from significantly reduced cost, processing time, and service charges. AI-driven smart wallets can learn from users’ spending behavior and generate real-time visual statistics to support their financial advice.
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AI in fintech has a great range of applications for financial institutions, such as higher quality customer support in real-time, scam prevention, insurance, asset management, and personalized financial advisory services. In addition, artificial intelligence and machine learning also contribute greatly to increasing the efficiency and accuracy of analytics, making customer interaction faster and more value-adding.