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

AI has transformed every industry, and the banking and financial sector are no exception. The new wave of AI has completely transformed how the financial industry functions. 

However, many banks still struggle to incorporate advanced technological solutions because of a lack of clear strategy and investment plans. This results in the practice of traditional methods, which leads to delays in customers’ banking services.

“ Banks that haven’t started to think about AI are falling further and faster and faster behind.”-Alexandra Mousavizadeh, co-founder & CEO, Evident Insights

Incorporating AI in banking made the sector more technologically advanced and streamlined its complex banking operations. It has also made this sector more customer-centric and adaptable. 

McKinsey’s report shows that AI has the potential to unlock 1 trillion in incremental values for banks annually. And more than 40% of bank executives expect significant AI improvement in risk management.

This report revealed that AI in banking has significantly paved the way for efficiency, productivity, and revenue generation. 

Curious about the practical applications of AI in the banking and finance sector? This article will delve into several key areas where AI can be a game-changer in the banking sector, providing use cases and insights to empower you to make strategic business decisions. 

Growth of AI in Banking and Finance Industry

Across the banking industry, AI has revolutionized the entire financial sector and become an integral part of our world.  From providing 24/7 customer services to cybersecurity and fraud detection, AI in banking has made businesses customer-centric.  

Despite several benefits of AI in banking, many banks still need help knowing where to start or implement AI services. Adapting new AI technologies is essential for financial institutions to be competitive and fulfill customer expectations to operate more efficiently. 

However, the above report shows that the number of investments made by banks into tech or AI-focused companies has declined over the years. This report indicates that organizations must incorporate AI, risking falling behind in the competition. 

Report: Evident AI innovation

Another report shows that many European banks drove 45% of deal flow last year despite the fall-off, and 66% of funding goes to AI startups. 

Goldman Sach leads in the AI-focused investments among the top 10 banks. Most of the investment involves products or services catering to financial services. 

Moreover, 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%. 

Regarding global comparison, the largest market size will be in the United States (US$87.18 billion in 2023). Approximately 60% of financial companies opt for AI in their business processes.

These numbers show the potential of AI in the banking industry, so you must incorporate AI-powered solutions to stay caught up in the competitive market. Investing in this niche is profitable and possesses several benefits, which we will discuss in the next section. 

Benefits Of Integrating AI In Banking 

AI has become an integral part of our lives, and 80% of banks are highly aware of the benefits of integrating artificial intelligence in the finance sector. The potential of AI in banking apps is not limited to retail banking services; it also improves back-office processes and has several benefits; take a look. 

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 

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

3. Better Decision Making 

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

4. Robust Security 

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

6. Improved Risk Management

AI analyses a vast pack 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.

By integrating AI into the banking and finance industry, banks and credit unions can leverage all the above benefits and hold their position in the competitive landscape.

Use Case Of AI In Banking Sector

AI in the banking and finance sector enhances financial companies’ and banks’ performance and competitiveness. Multiple banks already implemented AI to detect fraud, track customers, improve customer experience, and 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, which help customers in many ways. With a chatbot, users can quickly resolve their queries and access various services with modern chatbots. These chatbots can assist customers 24/7 and respond swiftly and accurately to user queries. 

However, incorporating chatbots in banking eliminates long waiting times and tedious web searches. This helps attract more customers, optimizes service quality, and expands brand value in the market.  

Want to streamline banking operations? Build a chatbot with us and provide 24/7 assistance to your customers.

2. Compliance Management 

As the industry is vast, banks mostly face issues complying with stringent regulations, including monitoring the transactions and submitting them to the respective departments on time. 

Incorporating AI in banking can shift this burden to the trained systems for complaint management. One of the most common practices nowadays is that bankers use Generative AI to analyze customers’ data to ensure they comply with the KYC Act before approving the account.

3. Financial Advisor 

AI 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 to the user and analyzing their historical data. This increases signup rates and 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. To devise a fitting portfolio, the banks then use this system to predict future trends based on changing financial variables, including currency rates, inflation, and more things. This can be done without providing 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 reduces human errors when changing the software to newer programming.

6. Loan Score Management 

The bank oversees and evaluates several things before approving and rejecting the individual’s loan application. Artificial intelligence assists in credit scoring by analyzing the applicant’s current data and financial history. For instance, the AI can be trained as required 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 face enormous pressure to secure the customer’s interests and prevent fraudulent attempts. Data breaches are the most common issue banks face; the most significant data breach in the financial industry was detected in 2019 in the USA at the First American Financial Corporation. 

AI is trained to identify abnormal patterns in large volumes of transactions, and it can raise alerts. This leads to quicker identification of theft and fraudulent transactions, reducing banks’ financial loss.

Also Read: Guide to Banking App Development

How Artificial Intelligence Will Change the Future of Banking?

Traditionally seen as a central pillar of ledgers and physical transactions, the banking sector is radically transforming. Artificial Intelligence (AI) is responsible for this transformation, creating new opportunities in the industry.

There are multiple customers each day growing in the banking sector. The simple question is how banks can assist many users without increasing the workforce. This is the first thing that can be affected by positively using AI.

AI can be trained with the help of appropriate data, and it keeps on growing knowledge; this helps banks identify data quickly, manage data, and more. Other technologies can also enhance the banking sector.

As we stand on the threshold of a new era in banking, let’s delve into the potential of AI in banking and how it’s set to reshape the industry.

1. Machine Learning

Machine learning in banking automates vast amounts of data, whether it’s the history of transactions or chat logs with bank representatives. Machine learning models help banks process and analyze this data and understand customers’ needs. 

By incorporating machine learning in banking, financial institutes can quickly detect fraud, optimize credit, and strengthen customer engagement. 

Take a look at potential machine learning holds:

• Hyper personalization: Machine learning helps banks identify customer patterns and behavior, which allows them to understand their needs and wants. And provide users with hyper-personalization services.

• Task automation: Bank documentation and auditing are daunting, but banks can save time and money with machine-learning task automation solutions. 

• Risk management: Machine learning can enhance risk management, as ML can see how a bank will react to specific economic conditions, allowing decision-makers to introduce more informed strategies. 

2. Computer Vision

Computer vision is an essential aspect of banking, streamlining the complex operations of the financial industry. As per Research and Market, computer vision will significantly impact finance services and is expected to be a $17 billion market by 2027. This shows the potential of this technology in the banking sector. 

Therefore, this AI technology trains machines to interrupt information from images and videos and show results accordingly. In the financial sector, computer vision is utilized in areas like fraud control, authentication, and data extraction to increase operational efficiency. 

Let’s understand how computer vision is streamlining complex banking operations.

• KYC processing and onboarding: Computer vision can enhance KYC ( know your customer ) process by enabling banks to search and match customers’ pictures to identify media reporting and negative information on social media and the web. 

• Smarter branches: With computer vision, traditional banks can convert into bright branches, improving the banking in-branch experience. 

• Insurance: This technology is crucial in the insurance industry as well. Regarding property and casualty insurance, computer vision can help insurance companies remotely analyze properties’ images, eliminating the need for physical inspection. 

3. NLP(Natural language processing)

NLP is a branch of AI that helps machines or digital devices to automate human responses. With the help of NLP, banks can gain insights, manage repetitive tasks, and improve customer experiences by predicting future outcomes. NLP can reduce human error and help banks to save time and money. 

Moreover, NLP-powered chatbots in banks provide personalized customer support and assist with customers’ various financial transactions. These bots can understand NLP inputs and respond in human-like conversations, improving customer satisfaction. 

Let’s discover the benefits of NLP in banking:

• Scaling: NLP technology helps in analyzing vast amounts of data within seconds or minutes, including a variety of emails, documents, and social media data. 

• Portfolio management and risk modeling: This technology assists in analyzing earning reports, company filing, and news articles to evaluate investment opportunities. Helping portfolio managers to make intelligent decisions. 

• Compliance and risk assessment: NLP techniques help automate compliance processes by analyzing regulatory documents. It also assists in risk assessment by analyzing textual data to identify fraudulent activity.

4. AI-powered Robotic Automation 

AI-powered robotic automation is used to simplify repetitive and rule-driven work in banks. Some tasks can be easily manageable, such as sending emails, online applications, and copying and pasting information from one system to another. 

Therefore, RPA helps manage manual, repetitive tasks, track accounts, and send automated notifications so that employees can shift their focus on essential tasks. 

Let’s understand the benefits of integrating RPA in banking:

• General ledger: For maintaining general ledger RPA technology for preparing financial statements. RPA can automate the amount of data and reduce the errors and handling time.

• Account origination process: This is the most time-consuming process of implementing RPA, eliminating the need for data collection and removing errors across all banking channels, resulting in faster loan approval. 

• Underwriter support: With the help of an automated underwriting process in banking, it automates loan-related decisions and eliminates the risk of human error. 

5. Quantum Computing in Banking

Quantum computing, often called the next frontier in computational power, holds immense promise for the banking sector. This technology allows complex calculations to be performed within a fraction of time.

Quantum’s financial system challenges the traditional banking system and addresses issues like manipulation and corruption in the financial sector. 

Take a look at the benefits of Quantum computing in banking:

• Speed and Efficiency: Quantum computers can process vast amounts of data at speeds previously thought impossible. Complex banking transactions, which take traditional computers hours or even days, can be completed in 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, many other computer science technologies are still working on everyday improvement in the banking sector. Keep updated for further changes and upcoming advancements.

Top Apps Owning The Market In The Banking and Finance Industry 

Multiple apps and companies use AI in the banking and finance sector market. Here are some of them mentioned below.

1. Capital One 

Location– McLean, Virginia

It is one example of banks embracing AI to serve their customers better. In 2017, it released ENO, a virtual assistant that allows users to communicate with the help of text, email, desktop, or a mobile app. It ensures 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 improves the field’s performance. AI also helps the bank detect fraud prior and helps the banks gain the customers’ trust.

4. Vectra AI 

San Jose, California

With its AI-enabled platform for detecting cyber threats, Vectra helps financial institutions. The software speeds up investigations after incidents, automates threat detection, discloses covert attackers, mainly 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, to generate summaries for bankers to use in updating customers.

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

Digital technology is impacting almost all the industries out there. Banking is also one of them, and AI is used in multiple cases to succeed in the tech-driven world. In the above section, we have discussed popular banks leveraging AI in banking to improve the productivity and efficiency of banking operations. 

Look at the banks that successfully implement AI services and leverage AI to their full advantage.

1. Ally Financial 

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

Problem- The problem of Ally Financial was that they could not manually manage the large amount of data on loan applications. Before, it took a considerable amount of time to verify everything individually. Thus, the process takes a lot of time to sort, and some things are also left behind as it is done manually. So, implementing AI can resolve this issue quickly. 

Solutions- Ally is using the Informed IQ software, which provides multiple benefits and overcomes the issues the banks face. Firstly, it is faster than manually picking the document and checking 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, response time and personalized experiences improved, and the customer satisfaction score increased 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 Nordic region’s leading financial institutions 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 prevalent. As the bank goes digital, it faces new levels of fraud or fraud threats that must be checked quickly. They picked up 1,200 false positives daily 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 Think Big Analytics, a Teradata Company decided to make its open-source modules. 

This overcomes the issue by reducing 60 % of false positives and a 50 % 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 data quality due to security issues. Let’s have a look.

1. Data Security Concerns

A considerable amount of data is involved in the banking industry due to the presence of many users. So, it is essential to look for the perfect technology partner to 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. High-quality data is needed to guarantee that the algorithm works in real-world scenarios. 

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

3. Explainability of AI Decisions

Many 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. Minor discrepancies in AI systems quickly become major issues that endanger the bank’s reputation and ability to function.

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

How To Become An AI-First Bank: Key Steps To Follow

As we know, AI is one of the essential things that can entirely change the banking and finance sectors. Thus, this section will look at the steps banks should take to adopt AI broadly.

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 essential to keep in mind all the things that must be improved with the help of AI. Also, the AI strategy must comply with industry regulations and standards.

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 forming an AI strategy.

2. Planning With Use Case-Driven Processes

This step finds the highest-value AI opportunities while coordinating with the bank’s operations and strategies.

Banks must handle 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 with the best chance of success.

The mapping of AI talent is the last step in the planning process. To create and deploy AI solutions, banks need several 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 making a fully developed AI system. Banks must gather and submit all relevant data to the algorithm 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?

AI in banking evolves core banking tasks by streamlining them like never before. This technology has changed the banking industry workforce and reshaped the user experience, boosting productivity and efficiency. 

A report from IBM shows that 86% of banking organizations are in production or preparing to go live with generative AI use cases. Thus, several banks have implemented AI solutions to leverage the competitive advantage of AI in banking. 

This shows the promising future of AI in banking, so implementing artificial technology has several advantages, such as streamlining complex operations, reducing errors, automating repetitive tasks, and saving time and money simultaneously. 

Every bank must invest in AI-powered solutions to offer the best customer-centric experience. Emizentech, a top AI development company, provides AI-powered solutions for all complex banking operations. We have worked with banks and develop custom AI and ML models, which help them reduce costs and increase their revenue generation.
We can also help you create fintech development solutions that enhance business operations, reduce risk, and improve customer-centric interactions. If you want to build AI-powered solutions, book a free 60-minute consultation and leverage the best of AI in banking.

Conclusion 

AI has been one of the hot topics in the market for the past few years and is also gaining considerable benefits in the banking and financial sectors. AI can streamline business processes. This also indicates that the future of AI is promising and bright. Thus, implementing AI in banking and finance 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 the banking sector. For more information, you can contact us.

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