AI in Fintech: Use Cases of Artificial Intelligence and Machine Learning in Fintech

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. 

Are you building a digital wallet or trying to implement the right payment system? Geniusee, as a financial software development company, is ready to take your challenging project and make it rock with state-of-the-art uses of AI and ML.

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.

AI-Powered Chatbots

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 AI chatbots with machine learning and artificial intelligence.

As an added benefit of AI, 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 by more than a million within two months of launch.

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Customer Support

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 to train 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 FinTech 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.

Fraud Detection

The increasing use of blockchain technology has led to the popularity of smart contracts and smart wallets. However, with higher convenience for the customers, data security concerns for financial services have also multiplied. Javelin identified that financial theft accounted for 56 billion USD in 2020.

However, artificial intelligence detects fraud easily for FinTech applications 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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Insurance Management

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 cost with AI-driven loan 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 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 Analytics

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 banking 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 of 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 costs, 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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Reasons for Embracing AI in the FinTech 

FinTech companies are increasingly turning to Artificial Intelligence as a powerful tool to transform the landscape. AI's ability to analyze data arrays, automate processes, and make intelligent and accurate predictions has revolutionized the way financial services are delivered. Here are some compelling reasons why embracing AI in FinTech is becoming essential for staying competitive and driving innovation.

Enhanced Customer Experience

AI chatbots and virtual assistants enable personalized customer interactions, offering real-time support and instant responses to queries. This fosters a seamless and efficient customer experience, resulting in increased satisfaction and improved customer loyalty.

Data-Driven Insights

FinTech companies deal with enormous volumes of data. AI-driven analytics can swiftly analyze this data to identify patterns, trends, and valuable insights. These data-driven insights help FinTech businesses make more informed decisions, tailor offerings to individual preferences, and refine their strategies for enhanced performance.

Fraud Detection and Prevention

The financial sector faces significant challenges related to fraud and cyber threats AI algorithms can detect anomalous behavior and patterns, spotting potential fraudulent activities in real-time. This proactive approach helps minimize financial losses and safeguards customer trust.

Smart Risk Management Strategies

AI-driven risk assessment models can analyze borrower data and creditworthiness more accurately and efficiently. This enables lenders to make data-backed decisions on loan approvals and minimize default rates, leading to a healthier loan portfolio.

Speed and Efficiency

Artificial intelligence works as a tool for the automation of manual processes in FinTech, reducing operational bottlenecks and increasing overall efficiency. Tasks like document verification, customer onboarding, and payment processing can be streamlined, saving time and resources.

Personalized Financial Advice

AI algorithms can analyze customers' financial behaviors and goals to provide tailored financial advice. This personalized guidance helps customers make better financial decisions and achieve their objectives effectively.

Improved Regulatory Compliance

FinTech companies operate within a complex regulatory environment. AI can ensure adherence to compliance requirements by continuously monitoring transactions, detecting suspicious activities, and generating accurate reports, reducing the risk of penalties and legal issues.

Algorithmic Trading

AI-powered trading algorithms can analyze market trends, identify profitable opportunities, and execute trades with remarkable speed and precision. This allows FinTech firms to capitalize on market fluctuations and generate superior returns for investors.

Cost Savings

By automating routine processes and reducing the need for manual intervention, FinTech companies can achieve significant cost savings. These cost efficiencies can be reinvested in product development and customer-focused initiatives.

Competitive Advantage

Embracing AI in financial services allows companies to stay ahead of the competition. Early adopters of AI technologies can gain a competitive edge by offering innovative products, faster services, and superior customer experiences.

So AI presents FinTech companies with immense opportunities for growth, efficiency, and customer-centricity. Embracing AI-driven solutions can lead to enhanced customer experiences, improved credit risk management, and strategic advantages in the fast-evolving financial landscape. As AI technologies continue to advance, FinTech firms that harness their potential will be better positioned to thrive in the digital economy.

Integrate AI in FinTech with Geniusee

With a team of highly skilled AI developers and data scientists, Geniusee creates advanced AI-powered security solutions and algorithms that enable financial institutions to make data-driven decisions with unprecedented accuracy and speed. Our AI-driven analytics offer valuable insights into customer behavior and market trends, enabling financial technology companies to identify lucrative opportunities and make informed investment choices. As the FinTech landscape continues to evolve, Geniusee remains committed to pushing the boundaries of AI integration, helping clients stay ahead of the curve and achieve sustainable growth in this rapidly changing industry. 

Wrapping Up

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. 

Need a professional financial software development team? Drop a note to Geniusee to make your project a reality.