AI in Banking: How Artificial Intelligence is Used in Banks?

Artificial intelligence has made waves across various industries, and the banking and financial sectors are no exception. It is believed that AI in banking will bring a drastic change in the upcoming years. The implementation of AI in applications and services made the sector more technologically advanced and user-centric. 

AI is helping the sector by improving efficiency, enabling a growth agenda, managing risks and regulatory needs, and positively influencing the growth of the sector. Wondering what things can be improved using AI in the banking and finance sector. In this article, we will cover several topics supporting AI in this sector, along with the use cases and how it can be a successful choice for your business. 

Key Benefits of Integrating AI in Banking

AI in the banking and finance sector is already gaining a lot of benefits after integrating AI into their customer and back-facing processes. Mentioned below are some of them:

1. Personalized Customer Experience

Customers are no longer subjected to generic product offers or lengthy support wait times. Instead, consumers receive rapid service and always have access to relevant information through websites, apps, and other digital channels.

2. Improved Efficiency 

Financial organizations can increase operations and get around bottlenecks that hinder human processes thanks to AI technologies. They automate time-consuming and repetitive operations using AI technology, freeing up the banking personnel to provide greater value to consumers. 

3. Better Decision Making 

With the aid of AI-enabled insights, bankers, fund managers, and other financial stakeholders support their choices. In erratic market conditions, they employ AI algorithms to minimize risks and maximize possibilities.

4. Robust Security 

When customers discuss their financial options with AI, they feel more secure and at ease. They have the option to withhold personal information and ask that all conversational data be deleted following the session.

5. Improved Privacy 

When customers discuss their financial options with AI, they feel more secure and at ease. They have the option to withhold personal information and ask that all conversational data be deleted following the session.

6. Improved Risk Management

AI analyses the large amount of financial data available to banks to help them better manage risks. Instead of jumping to conclusions, bankers use predictive insights to safeguard assets, overcome obstacles, and seize market opportunities.

The Growth of AI in The Banking Industry

AI is growing in the market day by day. Give below are some stats to support the statement: 

• The global artificial market size is projected to reach US $241.80bn in 2023 and is expected to grow at a CAGR of 17.30% from 2023 to 2030, resulting in a market volume of US $738.80 bn by 2030. 
• The global AI in banking market is expected to grow from $6.82 billion in 2022  to $9.00 billion in 2023 at a CAGR OF 32.1%. 
• In a global comparison, the largest market size will be in the United States (US$87.18 billion in 2023).
• Approximately 60% of financial companies are opting for AI in their business processes.

Use Case of AI in Banking Sector

AI in the banking and finance sector is enhancing the performance and competitiveness of financial companies and banks. Multiple banks already implemented AI for fraud detection, tracking customers, enhancing customer experience, and many more. Listed below are the top use cases of AI in banking industries.

1. AI Chatbots 

One of the best advantages of using AI in banking is chatbots that help customers in many ways. With a chatbot, users can get all their queries resolved in some time, and they can also access various services with modern chatbots. These chatbots can assist customers 24/7 and give accurate responses to their queries. However, the use of chatbots in banking attracts more customers, optimizes service quality, and expands the brand value in the market.  

2. Compliance Management 

As the industry is huge, banks mostly face issues in complying with stringent regulations, which includes the monitoring of the transactions and submission of them to the respective department on time. With the use of AI, this burden can be shifted to the trained systems for complaint management. One of the most common practices that nowadays is followed is bankers use Generative AI to analyze the data of the customers to ensure they comply with the KYC Act before approving the account.

3. Financial Advisor 

The use of AI in the system enables the banks and finance sector to dedicate equal and personalized engagement to each customer. The deep learning model of AI analyzes the customer’s spending behaviors and risk appetite before suggesting the product for the user and their historical data. This increases the signup rates and also helps retain existing customers.

4. Portfolio Management 

AI allows the banks to adopt a finer approach when recommending portfolio strategies to customers. The AI model itself gets trained with the help of the vast data present in the market. This system is then used by the banks to predict future trends based on changing financial variables, including currency rates, inflation, and more things to devise a fitting portfolio. This can be done without providing any financial details in a comfortable and more private environment.

5. Legacy Software Maintenance 

Many banks are still using systems made with the help of obsolete programming languages, so in this case, instead of rewriting all the codes from scratch. The developers use AI for large language models to generate the code. This helps improve coding efficiency and also reduces human errors when changing the software to newer programming.

6. Loan Score Management 

Before approving and rejecting the loan application of the individual, the bank takes care of and evaluates several things. Artificial intelligence assists in credit scoring by analyzing the current data and financial history of the applicant. For instance, the AI can be trained as per the requirement and can teach to predict the likelihood of a default by assessing the applicant’s salary, home, and other credit indicators. 

7. Fraud Detection 

Due to widespread data breaches, banks have to face huge pressure in securing the customer’s interests and preventing fraudulent attempts. AI is trained to identify abnormal patterns in large volumes of transactions, and it can raise alerts. This helps the banks to halt the transactions and maintain customer trust. 

Also Read: Guide to Banking App Development

How Artificial Intelligence Will Change the Future of Banking?

The banking sector, traditionally seen as a main pillar of ledgers and physical transactions, is undergoing a radical transformation. At the heart of this change is Artificial Intelligence (AI). As we stand on the threshold of a new era in banking, let’s delve into the potential of AI and how it’s set to reshape the industry.

Digital technology is impacting almost all the industries out there. Banking is also one of them, which is using AI in multiple use cases to succeed in the tech-driven world.

There are multiple customers each day growing in the banking sector. Now, the simple question is how banks can assist the large number of users without increasing the workforce. This is the first thing that can be affected by using AI in a positive way. AI can be trained with the help of appropriate data, and it keeps on growing the knowledge; this helps the banks to identify the data quickly, along with data management and more. Along with it, there are other technologies that can enhance the banking sector such as:-

The Potential of Quantum Computing in Banking

Quantum computing, often called the next frontier in computational power, holds immense promise for the banking sector. Here’s why:

• Speed and Efficiency: Quantum computers can process vast amounts of data at speeds previously thought impossible. Complex banking transactions, which might take traditional computers hours or even days, can be completed in mere seconds with quantum computing.

• Enhanced Security: Quantum encryption methods can provide a level of security that’s virtually unbreakable, ensuring that sensitive financial data remains safe from cyber threats.

• Optimized Financial Models: Quantum algorithms can analyze and predict market trends with unprecedented accuracy, allowing banks to make more informed investment decisions.

Like this, there are many other technologies of computer science still working on everyday improvement in the banking sector. Keep updated for further changes and upcoming advancements.

Apps Owning the Market in The Banking and Finance Industry 

There are multiple apps and companies using AI in the banking and finance sector market. Here are some of them mentioned below.

1. Capital One 

Location– McLean, Virginia

It is one of the examples of banks embracing the use of AI to better serve it’s customers. In the year 2017, it released ENO, which is a virtual assistant that users can communicate with the help of text, email, desktop, or a mobile app. It makes sure that the user can text questions, receive fraud alerts, and take care of tasks like tracking account balances, paying credit cards, viewing available credit, and checking transactions. 

2. Kasisto 

Location– New York 

U.K. Kasisto is one of the companies that’s brought digital-first banking to the U.S. Kasisto’s conversational AI platform, KAI, allows banks to build their own chatbots and virtual assistants. Many UAE-based digital banks Liv., DBS Bank, Standard Chartered Bank, and TD. All these banks use KAI-based bots to walk customers through how to make international transfers, block credit card charges, and transfer you to help humans when the bot hits a wall. 

3. J P Morgan

Location– New York 

A private bank using AI in the system and leading in the market helps the banks in the high trading and payments and also improves the performance in the field. AI also helps the bank detect fraud prior and helps the banks gain the trust of the customers.

4. Vectra AI 

San Jose, California

With its platform for detecting cyber threats enabled by AI, Vectra helps financial institutions. The software speeds up investigations after incidents, automates threat detection, discloses covert attackers, particularly targeting banks, and even detects tainted data. The assault was stopped by Vectra’s platform, which saw behavior like an attacker scanning the footprint for vulnerabilities in a healthcare group to prevent security attacks.

5. The Goldman Sachs Group Inc. 

Location– New York 

The business is experimenting with AI to determine how to extract the most valuable information from massive data sets, such as publicly available regulatory filings and corporate databases, in order to generate summaries for bankers to use in updating customers.

Case Studies of The Banks that Succeeded with The Implementation of AI Services

1. Ally Financial 

Ally Financial, formerly known as GMAC ( General Motors Acceptance  Corporation), has a long history in the financial services sector. To serve the customers better and adapt to changing market dynamics, the company underwent a digital transformation. 

Problem- The problem of Ally Financial was that they were unable to manage the large amount of data of the loan applications manually. Before, it took a huge amount of time to verify all the things one by one. Thus, the process takes a large amount of time to sort, and as it is done manually, some things are also left behind. So, by implementing AI, it can be resolved easily. 

Solutions- Ally is using the Informed IQ software that provides multiple benefits and also overcomes the issues faced by the banks. Firstly, it is way faster than manually picking the document and checking things like the potential borrower’s income. Secondly, it reduces fraudulent activity by identifying unnecessary activity in the bank. Third, it is more accurate than humans can be; AI can quickly check against many spot anomalies and data sources. 

Key Results 

• Enhanced Customer Satisfaction- With the help of AI-powered chatbots and assistants, it improved response time and personalized experiences and also increased the customer satisfaction score by 20%. 
• Cost Savings- Operational optimizations achieved through AI led to a 12% reduction in operating costs. 
• Fraud Prevention- Real-time fraud detection systems helped prevent huge amounts of money in the first year of implementation.  

2. Danske Bank 

Danske Bank is one of the leading financial institutions in the Nordic region and is headquartered in Copenhagen, Denmark. It is famous in the market for its rich history and commitment to innovation; the bank was the first to adopt new technologies to enhance its services and ensure the security of its customers. 

Problem- The issue is very common. As the bank goes digital, it faces new levels of fraud or fraud threats that have to be checked at a very high speed. They were picking up 1,200 false positives per day in its transaction monitoring, and 99.5 percent were false positives. 

Solutions- The company looked for multiple anti-fraud software but could not find one that suits their requirement; thus, with the help of machine learning and deep learning in collaboration with the company Think Big Analytics, a Teradata Company decided to make its own open-source modules. 

This overcomes the issue by reducing 60 percent of false positives and a 50-ish improvement in detecting actual fraud.

Key Features 

• Real-Time Monitoring– Every transaction is instantly analyzed, taking care that it immediately detects suspicious activity. 
• Predictive Analysis- This system can analyze the threats based on current transaction trends and historical data. 
• User Behaviour Analysis– By understanding the typical behavior of users, the system can quickly identify signaling potential frauds and any deviations. 

What Are the Challenges in Adapting AI in Banking Sector?

AI comes with multiple benefits in the banking and finance sectors. However, it also comes up with some challenges. Some of them are mentioned below, such as lacking credibility and quality of data due to security issues. Let’s have a look.

1. Data Security Concerns

There is a huge amount of data involved in the banking industry due to the presence of a large number of users. So, it is important to look for the perfect technology partner that will understand banking and AI and offer various security options to ensure your customer data is appropriately handled.

2. Quality of Data for AI Training

Before implementing a comprehensive AI-based banking solution, banks must first gather organized and high-quality data for training and validation. To guarantee that the algorithm works in real-world scenarios, high-quality data is needed. 

Additionally, data that cannot be read by machines may cause AI models to behave unexpectedly. Therefore, banks moving quickly to implement AI must change their data policies to reduce any privacy and regulatory issues.

3. Explainability of AI Decisions

A lot of decision-making processes use AI-based solutions since they reduce errors and save time. They might, however, adhere to prejudices gleaned from prior instances of subpar human judgment. Small discrepancies in AI systems quickly become major issues that endanger the bank’s reputation and ability to function.

Banks should provide a sufficient level of explainability for all choices and recommendations made by AI models in order to prevent catastrophes. The way the model decides must be understood, verified, and explained by the banks.

Steps to Become an AI-First Bank

As we know, AI is one of the important things that can fully change the banking and finance sectors. Thus, this section will look at the steps banks should take to adopt AI on a broad scale.

1. Developing a Comprehensive AI Strategy

Before implementing any AI strategy in the business, the goals and values of the enterprise should be focused on. It is important to keep note of all the things that must be improved with the help of AI. Also, make sure the AI strategy complies with the industry regulations and standards.

In order to provide clear instructions and guidance for AI adoption throughout the bank’s many functional divisions, the internal practices and policies relating to people, data, infrastructure, and algorithms must be refined as the last step in the formation of an AI strategy.

2. Planning with Use Case-Driven Processes

In this step, the highest-value AI opportunities are found while coordinating with the bank’s operations and strategies.

Banks must take care of this and integrate AI banking solutions into their existing or changed operational procedures. 

The QA team should conduct checks on testing viability after discovering potential AI in banking use cases. They need to investigate every angle and spot any implementation deficiencies. Based on their appraisal, they must choose the instances that have the best chance of success.

The mapping of AI talent is the last step in the planning process. In order to create and deploy AI solutions, banks need a number of specialists, algorithm programmers, or data scientists. They can outsource or work with a technology vendor if they don’t have the necessary internal expertise.

3. Development, Deployment, and Continuous Monitoring of AI Systems

Executing the procedure is the next stage for banks after planning. They must create prototypes to grasp the limitations of the technology before creating a fully developed AI system. Banks must gather all relevant data and submit it to the algorithm in order to test the prototypes. The data must be accurate because the AI model learns and develops using it. 

Banks must test the AI model to evaluate the results after it has been trained and made ready. The development team will benefit from a trial like this to better understand how the model will function.

The trained model must be deployed as the final step. Production data starts to arrive as soon as it is deployed. Banks can continuously enhance and update the model as more and more data come in.

How Can We Help?

Till now, you are able to understand the importance of AI in the banking sector and how much it can make a change in the industry. Thus, hiring an AI mobile application company that can help you with all the processes for integrating AI technologies into banking and finance is also important. Emizentech, as a top AI development company, ensures that solutions for all issues are provided to the clients. Along with this, it also provides personalized solutions that consider all the requirements in mind.

If you are also looking for a good AI development company for AI services integration in your sector, you can contact us now and get your issue resolved.

Conclusion 

AI has been one of the hot topics in the market for the past few years and is also gaining huge benefits in the banking and financial sectors. AI has the capabilities to streamline the business processes. This also indicates that the future of AI is promising and bright. Thus, implementing AI in the banking and finance business can offer customers a novel experience, improving revenue costs and mitigating risks in different departments. 

Hope this blog helped you by providing the benefits of AI in banking sector. For more information, you can contact us. 

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