AI in banking has tremendously changed the way banks operate. It has modified the banking and finance sectors, by providing improved fraud detection, automating tasks, predicting market trends, and assisting in better decision-making. It has made online banking platforms more secure, robust, and customer-centric.
So in this blog, we’ll uncover the use cases of AI in banking to improve users’ experience. Let’s step into the world of AI in banks and discover how this smart technology is making a big difference in the banking and financial sector.
Market Stats for Growth of AI in Banking & Financial Sector
Here are some of the market statistics of AI showing its speedy growth in the banking and financial industry.
- According to Business Wire, the global AI in banking market size was valued at $3.88 billion in 2020 and is projected to reach $64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030.
- According to Forbes, 70% of financial firms use machine learning to predict cash flow events, adjust credit scores, and detect fraud.
- A study from Business Insider predicts that artificial intelligence applications will save banks and financial institutions $447 billion by 2023.
- McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year.
Use-Cases of AI in Banking & Finance
Traditionally, banks used to take a lot of time to do a single task, such as depositing or withdrawing money, updating account details, etc. But with the help of AI, these tasks can be executed quickly and with utmost accuracy.
Let’s discuss various AI use cases in the banking and finance sector, which are:
- Process Automation
Robotic process automation (RPA) algorithms are very helpful in banking. They do tasks that are tedious and time-consuming. This way, employees can concentrate on harder jobs that need human intervention. Banks use these tools to make transactions faster and do processes more efficiently.
- Data Collection & Analysis
Banks and finance companies deal with a massive number of transactions every day. Keeping track of all this information is a big challenge for the employees because there’s just so much of it. It’s tough to organize and manage all this data perfectly.
But here’s where smart AI banking solutions come into action. They help collect and understand all this data well, making things easier for everyone. It not only helps the banks but also enhances customer experience. This smart technology can also verify fraudulent is going on, and it helps select the person for loan credit.
- Cyber Security & Fraud
Every day, people use digital tools to pay bills, withdraw cash, access their transaction history, and more. Because of this, banks need to work harder to detect any kind of fraud like cheating or stealing. This is where AI in banking can play a major role.
AI and machine learning-enabled software can help banks find out if someone is trying to cheat them, find problems in their systems, make processes safer online, and decrease the chances of errors in detecting any fraud at its early stage.
Deep Learning, a significant AI banking solution, can enhance the bank’s ability to detect fraud better by 50% and safeguard the bank system from any cyber attack or fraud by 60%. The AI system also helped the bank make some informed decisions on its own, without any human invasion until required.
AI can also help banks to keep cyber-attackers away. A few years ago, almost a third of all computer attacks happened to banks and money-related businesses. But with AI always watching, banks can stop cyber attackers before they can disrupt the bank, its people, or its computer systems.
- Predictive Analytics
AI is really helpful in banking as it can analyze enormous data in less time and minimizes repetitive tasks. It often uses predictive analysis to understand and analyze how people communicate to predict their future actions. Unlike regular technology, AI can find patterns and connections in data that were hard to spot before.
These patterns might show opportunities to make more sales, suggest related products, or give insights into how processes are running, all of which can help make more money.
- Loan & Credit Decisions
The use of AI in banking can help make better and more informed decisions when giving out loans or credit. Right now, many banks mainly rely on factors like your credit history, credit scores, and your background to decide if they should give you money.
But the truth is, these credit reports often have mistakes and miss some real financial history of users, and hence, misclassify creditors.
AI in banking systems for loans and credit can verify how you handle money and spot patterns, even if you don’t have much of a credit history. They can also let banks know about users’ behaviors that might make it more likely for someone not to pay back their loan. In short, these technologies are changing how banks decide who will get loans and credit in the future.
- Risk Management
Sometimes, incidents happening around the world, like money value changing, natural disasters, etc., can really affect banks and finance businesses. When these times are uncertain, it’s super important for businesses to be extra careful when making crucial financial decisions.
This is where special AI tools can help banks significantly. They can access the data and predict what might happen in the future. This helps businesses be ready and make smart choices on time.
Chatbot, an AI-enabled tool, can streamline banks’ operations significantly. They are 24×7 virtual agents that provide assistance to users according to the data they have been trained with. They also keep learning with their increased usage, so they can resolve more issues.
By leveraging their app with chatbots, banks allow you to connect with them anytime.
- Tracking Market Trends
Banks can process enormous amounts of data using AI and Machine Learning, and predict the latest trends of the market by evaluating them. With the help of this advanced technology, they can examine the market trend more closely and analyze the future market growth with utmost accuracy.
These AI-enabled tools in banking can also suggest a good time to invest in any stock or give an alert when the market is facing any kind of downfall. As they can process a large quantity of data quickly, these AI use cases in banking make it faster and easier for banks to make decisions and help their customers with trading.
- Customer Experience
Customers always want things to be easier and more convenient. Think about ATMs – they were a hit because people could do banking activities like depositing or withdrawing money from their accounts, even when the bank was closed.
Plus, because chatbots learn from how you use them, they can give you personalized suggestions that enhance the customer experience. Along with this, banks can also help in processing the loan of a user in less time, unlike how they have to wait for months previously.
Even in customer service, AI banking is great at getting your details right and setting up your account without any problems. This makes sure your experience with the bank is smooth and easy.
- Regulatory Compliance
Banking is a heavily controlled part of the economy all over the world. Governments have rules to make sure that people aren’t using banks for illegal transactional activities. Usually, banks have their team following these rules, but it takes a lot of time and money when they do it manually. Also, the rules change a lot, so the banks have to keep updating how they process their tasks.
Machine learning and AI in banking use smart tech like deep learning and understanding language to set new rules for banks. This helps them make better choices. While AI can’t replace the people who set up the rules, it can make their work faster and more efficient.
- Trading and Portfolio Management
AI in banking can understand the market and find chances to make money by trading. It gives helpful ideas for making better investment choices.
AI can also detect the change in the market, and provide suggestions regarding investments in real time. This makes managing your investments better, lowers the chances of risks, and helps gain market insights. Therefore, with the help of AI in banking and finance, banks can manage their investment portfolios better.
Top Banks Using AI-enabled Solutions
Now, you have read the different use cases of AI in banks. Now, let’s step towards the top banks that have enabled their app/solutions with AI. Some of them are:
American Express, commonly known as AMEX, uses various technologies, such as AI, blockchain, cybersecurity, and others to safeguard their app from fraud. Amex has utilized these technologies for their daily operations, such as process automation, loan & credit decisions, cyber security & fraud, etc. The use of AI in banking also helps reduce repetitive and tedious human tasks.
Barclays uses AI technology for their business in different forms, such as machine learning, biometrics, smart contracts, etc., which help to prevent and detect fraud. They use an effective fraud management system that helps to assist in spotting and preventing scams and other financial fraud.
- Master Card
MasterCard uses AI to detect fraud and reduce the number of unauthorized transactions. Decision Intelligence, a fraud detection tool created by the firm, employs AI algorithms to examine transaction data and spot patterns of fraudulent behavior. Due to this, both Mastercard’s and its customers’ risk of fraud has decreased, and the payment industry’s overall security has improved.
- JPMorgan Chase
JPMorgan Chase has empowered their business using AI-enabled solutions. It has helped them to streamline their business processes, enhance customer experience, and provide better quality outputs. The best AI banking solutions can provide an early warning if your system is detected with any virus.
- Capital One
Capital One uses artificial intelligence to speed up the loan underwriting process. CreditWise, an AI-powered tool created by the bank, analyzes consumer credit data using machine learning algorithms, automating credit checks and approvals. As a result, human credit checks take less time and money to complete, and the bank’s lending operations are now more effective.
Guide to Develop an AI-enabled Solution for a Bank and Financial Institution
Now that you have to know how the use of AI in banking can help them in different ways, it’s time to know the process of developing an AI-enabled solution for banks and financial institutions. Let’s get started:
Step 1: Plan an Effective AI Strategy
To enable AI in banking and finance services, you need to develop a solution that can fulfill the multifaceted needs of an organization. While doing so, you should also keep the goals and objectives of the organization in mind. Verify that the AI approach adheres to all applicable rules and regulations. The current global industrial standards can also be assessed by banks.
Step 2: Set a Wireframe
For the next step, you should design the features and functionalities that you want to embed in your AI solution. Banks should list the processes they want to enhance with the help of AI. They should include all the essential operations, such as transactions, process automation, risk management, and others.
Step 3: Develop the Solution & Deploy
After planning all the features and functionalities, and other scenarios of it, start the development process of the AI-enabled banking solution.
They must create prototypes to comprehend the limitations of the technology before creating a fully developed AI banking system. Banks must gather pertinent data and store it in the database so that the algorithm can test the prototypes. The data must be correct since the AI model learns and develops on it. Once the AI solution is ready, deploy it in various banking systems.
Step 4: Operate & Monitor
After deploying the AI solution, it starts getting data. As more and more data gets poured in, banks can make use of that data for various operations, such as predictive analytics, tracking market trends, and others.
AI-enabled systems can monitor this data and provide effective results. It can also help in monitoring cybersecurity threats, and reduce fraudulent cases, and unauthorized transactions.
Cost of Developing an AI-enabled Solution for a Bank and Financial Institution
The cost of developing an AI solution for the banking and finance sector depends on various factors, such as the complexity of the system, its features and functionalities, its design, the use of technologies, and others.
However, it may cost you around US $50,000 to US $100,000 and even more to develop an AI-enabled solution, depending on the above-mentioned factors.
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