Artificial intelligence (AI) and machine learning (ML) have driven the inception and rapid growth of the global financial technology (Fintech) industry. Industry data suggests that by 2026, the global market for AI in banking will reach USD 26.67 billion, with a CAGR of 23.17% from 2021 through 2026. 

With increasing levels of process automation and digital transformation in financial services, AI and machine learning are used across the banking industry, from secure digital transactions to personalized financial advice. 

Do you want to know more about machine learning and artificial intelligence in banking? Keep reading this article, then. We will explore some leading use cases of AI and ML in the BFSI sector. But before we do that, here is a question for you — are AI and ML the same? Let’s find out.

The difference between AI and ML for Banking

On the web, social media, and even in the news, you will often see that writers tend to use the terms AI and ML in banking 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 of the broader domain of artificial intelligence. By using artificial intelligence, machines can practice intelligent decision-making while behaving more like humans. In contrast, machine learning enables machines to learn from new information and identify patterns without external supervision from engineers or developers. 

Are you building a digital wallet or implementing the right delivery channel system? Modefin, as a Digital banking 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 algorithms in the financial industry.

How are AI and ML used in Banking?

A study found that more than 90% of global financial institutions are already relying heavily on artificial intelligence and machine learning. The statistic also shows how financial firms are leveraging AI systems. Here are a few uses of artificial intelligence in banking.

Intelligent decision making

Using AI in banking has enabled financial institutions to make intelligent decisions by analysing massive amounts of real-time data gathered from national and global financial markets. The process of collecting, computing, and analyzing data occurs instantaneously. 

Financial advisers and analysts get a clear picture of the financial industry. They can use the data to offer more valuable advice on loans, insurance, and wealth management, thereby generating higher customer satisfaction.

Instant visibility and data analysis with artificial intelligence also help global Financial institutions make proactive decisions by anticipating financial sector trends. As a result, financial institutions can offer better quotations to customers based on individual financial goals, leading to greater convenience, reduced costs, and a lower workload for all parties.

AI-powered chatbots

Artificial intelligence in banking has given rise to AI-driven chatbots capable of conducting detailed sentiment analysis. Call centers worldwide spend a significant portion of their time and resources answering frequently asked customer questions. It is possible to identify specific patterns in customer queries and interactions using big data analytics. Financial institutions 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 better-quality process automation, eliminating customer waiting time. For example, when a Bank in Africa deployed chatbots for the first time to interact with customers, its customer base grew by more than a million within two months of launch.

Customer support

Financial service providers can deliver a superior customer experience by leveraging AI in banking. The main advantage is that it consumes fewer resources to train a chatbot with artificial intelligence than to train a person. The situation is more critical for modern-day financial institutions because governments and regulatory bodies frequently update rules and regulations governing financial services, which are difficult for humans to keep up with. AI technology directly addressed this shortcoming. 

Industry experts estimate that banks and Fintech institutions will save 826 million work hours over the coming years by introducing automated customer support powered by artificial intelligence in banking. At the same time, customers get their queries answered within a fraction of a second and do not notice any difference from human intelligence.

Fraud detection

The growing use of blockchain technology has driven the popularity of smart contracts and smart wallets. However, with greater convenience for customers, data security concerns in financial services have also multiplied. However, artificial intelligence easily detects fraud in banking applications by continuously monitoring the entire system and all user accounts to identify and restrict suspicious activity early. 

Illegal activities such as money laundering are also a significant financial security concern for financial regulators worldwide, and they spend a lot to deploy effective anti-money laundering mechanisms. ML in banking helps regulatory bodies detect money laundering activities quickly and take necessary measures. 

Predictive analytics

Predictive analysis has revolutionized how financial companies make decisions by integrating data science into the decision-making process. AI and ML can help companies analyse 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 AI algorithms to compute credit scores based on customers’ individual risk profiles and offer financial services accordingly to reduce the risk of future bad debts. 

Financial institutions can also apply predictive analysis to marketing and branding campaigns. For instance, banks can use it to proactively evaluate the effectiveness of a branding strategy on the customer’s mind.  

Reasons for embracing AI in the Banking

Companies are increasingly turning to artificial intelligence as a powerful tool to transform the landscape. AI’s ability to analyse data arrays, automate financial processes, and make intelligent, accurate predictions has revolutionized the delivery of financial services. 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, efficient customer experience, resulting in greater satisfaction and stronger customer loyalty.

Data-driven insights

Financial institutions deal with enormous volumes of data. AI-driven analytics can swiftly analyse this data to identify patterns, trends, and valuable insights. These data-driven insights help banks to 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 behaviour and patterns, spotting potential fraudulent activity in real time. This proactive approach helps minimize financial losses and safeguards customer trust.

Smart risk management strategies

AI-driven risk assessment models can analyse 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 automates manual processes in banking, reducing operational bottlenecks and increasing overall efficiency. Tasks like document verification, customer onboarding, etc., can be streamlined, saving time and resources.

Personalized financial advice

AI algorithms can analyse customers’ financial behaviours and goals to provide tailored financial advice. This personalized guidance helps customers make better financial decisions and achieve their objectives effectively.

Improved regulatory compliance

Banks operate within a complex regulatory environment. AI can ensure compliance with requirements by continuously monitoring transactions, detecting suspicious activity, and generating accurate reports, thereby reducing the risk of penalties and legal issues.

Cost savings

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

Competitive advantage

Embracing AI in banking 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 companies with immense opportunities for growth, efficiency, and customer-centricity. Embracing AI- and ML-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.

The future of ML and AI in Banking

AI and machine learning in banking not only enhance existing solutions and processes but also open up new possibilities for the banking sector. AI can create new products and services that meet customers’ needs and expectations in the era of digital transformation. Moreover, ML & AI can foster collaboration among market participants and contribute to social and environmental well-being. 

Here are some examples of how AI & ML algorithms will be used in the future BFSI sector.

Deep learning and big data in financial forecasting

AI can produce more accurate and reliable financial forecasts using big data and deep learning, analysing various variables influencing financial markets, including economic, political, social, psychological, and others. AI can also employ neural network-based ML models to identify complex patterns and signals in data that may be concealed by traditional methods. 

This enables investors and traders to make more informed and profitable decisions in the market. 

AI in personalized banking services

AI and ML in banking can utilize personalization and segmentation to create customized banking services tailored to individual customer needs and preferences. By leveraging customer data such as demographics, financial behaviour, goals, and interests, AI can recommend the most suitable products and services, including loans, deposits, cards, insurance, and more.  Additionally, AI can use NLP to facilitate more natural and convenient communication with customers through voice or text assistants. 

Wrapping Up

AI in banking has a wide range of applications for financial institutions, including higher-quality real-time customer support, scam prevention, insurance, asset management, and personalized financial advisory services. In addition, artificial intelligence and machine learning significantly increase the efficiency and accuracy of analytics, making customer interactions faster and more value-adding.