AI in Banking: Real Use Cases of AI in Banking

How AI Is Revolutionizing Banking: Transforming Customer Experiences and Enhancing Financial Security

Banking is a huge industry with a global Banking market likely to achieve a Net Interest Income of USD 10.34 trillion, with Traditional Banks holding a huge stake of USD 8.30 trillion. According to Statista’s projections suggest an annual growth rate of 4.82% (CAGR 2024-2028), culminating in a market volume of USD12.48 trillion by 2028. And FYI, Despite economic challenges, banks worldwide are embracing digital transformation to cater to evolving customer demands.

Navigating through the complexities of facilitating a smooth and secure online banking experience for customers requires a delicate balance between innovation and security. The introduction of AI in the banking sector has helped strike this balance. Want to know how? In this blog, we will help you understand how Artificial Intelligence (AI) has revolutionized the banking sector to enhance banking experiences, build security defenses, and foster trust with financial institutions while upholding the highest standards of professionalism. 

AI in Banking – Current Market Statistics 

The banking sector is considered a highly delicate sector because of its inherent responsibility in managing people’s finances, which demands a proactive approach to innovation while following all security and regulatory compliance. With the ever-evolving land

According to McKinsey’s report-  “Among industry sectors, banking is expected to have one of the largest opportunities due to AI: an annual potential of $200 billion to $340 billion (equivalent to 9 to 15 percent of operating profits), largely from increased productivity (exhibit).”

All banking segments and operations are expected to benefit economically from the impact, with the corporate and retail sectors expected to gain the most overall ($56 billion and $54 billion, respectively).

Value Created by AI at stake by segment and function

Generative AI can deliver a significant new value to banks- somwhere around $200 billion to $340 billion.

Now, we will explore the use cases of AI in banking and finance industry, as well as How AI is changing the way bank customers interact with technology.

Key Applications of Artificial Intelligence (AI) in Banking 

Artificial Intelligence is a revolutionary force in the banking and finance industry. From personalized customer experiences to robust risk management, AI is ramping up every aspect of banking and finance infrastructure. Now,  Let’s dig into some of the key applications driving this transformation:

1. Cyber Security & Fraud Detection 

By leveraging Artificial Intelligence (AI) and Machine Learning, banks can seamlessly identify frauds and mitigate potential risks in real time. Many renowned tech companies such as Amazon, Apple, and Google have proactively adopted AI and Machine Learning into their systems. 

This adaptation of AI by these tech joints has also prompted banking and fintech companies to follow suit. As per the figures, Artificial Intelligence has increased the bank’s fraud detection capability by 50% and reduced false positives by 60%. 

Now the question here is How? How does AI help in fraud detection? AI can analyze the data in real time, detect anomalies, and adapt and identify emerging fraud tactics enabling proactive fraud prevention. To dive in deeper, let’s understand How AI is helping banks strengthen their security measures and become more efficient in fraud detection: 

(i) Anomaly Detection

The sophisticated AI algorithms can analyze a large amount of transactional data in real time and identify abnormal or unusual patterns or deviations in the usual behavior. AI can spam suspicious activities in real-time and send timely updates to banks so they can respond to potential security threats. 

(ii) Behavioral Biometrics

AI-backed banking systems can analyze user behavior patterns like transactional habits, typing speed, and mouse movements to understand their behavioral biometric profiles for the users. A change in these patterns may trigger alerts for possible fraudulent activity. 

(iii) Natural Language Processing (NLP)

To identify and stop social engineering scams, phishing attacks, and other fraudulent activities, natural language processing (NLP) algorithms analyze the textual data from customer’s chat logs, emails, and customer support conversations, NLP algorithms can identify suspicious patterns, anomalies indicating towards fraudulent activities and language cues. In simple words, NLP serves as a powerful tool in the arsenal of banks, enabling them to proactively combat fraud and safeguard their customers’ financial security.

(iv) Advanced Authentication

Artificial Intelligence improves authentication by using various data sources such as biometric data, user behavior analysis, and device information. In simple words, AI facilitates the multi-layered approach to improve security by authenticating the identity of customers more precisely.

(v) Machine Learning Models

Banking systems can use machine learning to learn from historical data or fraudulent behavior. This enables them to detect the new fraud tactic and prevent it in real-time. With their great accuracy in identifying fraudulent activity, these models reduce false positives and enhance security in general.

(vi) Predictive Analytics

In order to detect fraud and maintain cybersecurity in the banking industry, AI-based predictive analytics uses complex algorithms to analyze transaction data from the past and detect trends and anomalies that could be related to illegal activity. Models are trained on labeled datasets to identify fraud indicators and estimate the probability of fraud in real-time. Over time, these models adjust and change constantly to detect new fraud trends. 

(vi) Regulatory Compliance

By automating compliance procedures like Know Your Customer (KYC) and Anti-Money Laundering (AML) checks, artificial intelligence (AI) helps banks stay compliant with regulations and reduces the risk of financial crime.

2. ChatBot Assistance for Customer Service

Just like other industries focused on customer satisfaction, the banking sector also relies highly on its customer satisfaction level to set the standard of its banking services. Banks can serve customers more promptly and improve the customer satisfaction rate. Banking institutions should embrace AI-powered chatbot assistants as it can enhance their online customer service experience.

One of the foremost advantages of integrating chatbots and AI virtual assistants in banking is its ability to respond in real time. Hence, it eliminates the need for customers to wait in long telephone call queues during periods of high call volume. Furthermore, chatbots can handle a large volume of inquiries simultaneously. This frees up human agents to focus on more complex tasks and delivering a higher level of service. Artificial Intelligence also empowers the chatbots to engage with the bank customers in natural language facilitating personalized assistance and recommendations. Overall, chatbot assistance revolutionizes customer service in the banking sector by combining speed, accessibility, and personalized interaction to meet customers’ evolving needs in the digital age.

3. Personalized Customer Experience

Another revolutionary benefit artificial intelligence is bringing on board for banks is the enablement of personalized customer experiences. AI algorithms can categorize customers based on their behavior and preferences, enabling banks to offer customized offers, products, and services based on their preferences and behavior. For instance, a customer might receive tailored suggestions for investment opportunities, credit cards, or savings accounts based on their financial behavior and goals.

4. Loan and Credit Decisions

Many banks have incorporated AI-powered systems to improve their decision making such as loans and credit decisions. In the age where you can leverage artificial intelligence as a modern solution to streamline their loan and credit processes, many banks are still using credit history, credit scores, and customer referrals to identify whether the individual or company is creditworthy. These AI systems use sophisticated algorithms to analyze vast amounts of data. This data may include customers’ spending patterns, financial transactions, and even social media activities to evaluate the worthiness and credit risk more effectively. 

Artificial intelligence (AI) can offer more nuanced insights into an applicant’s creditworthiness. AI takes a wider range of variables into account and identifies minor trends that would not be visible using traditional approaches. It can therefore provide more nuanced insights regarding an applicant’s creditworthiness.

However, some banks might still be unwilling to completely use these technologies owing to regulatory concerns, privacy difficulties, or a lack of understanding of how AI works, even though it is evident that AI streamlines loan and credit operations. The use of AI in traditional banking organizations may also be hampered by organizational or cultural constraints.

5. Tracking Market Trends

As already discussed, banks can analyze vast amounts of financial data. This data includes market indicators, news sentiments, and economic trends to analyze the market sentiments promptly. AI can make informed decisions such as investment opportunities, risk management, and asset allocation. Moreover, AI-powered banking solutions can offer customers the best time to invest in stocks and other financial assets. AI-powered systems continuously monitor market conditions to analyze historical data and identify the ideal entry and exit points. This enable banks and their customers to identify emerging market opportunities and minimize loss risks.

Additionally, the robust data processing capabilities of AI speed up decision-making. Banks can promptly make investment decisions and respond quickly to dynamic market conditions. Moreover, banks can boost their efficiency, and serve their clients more effectively by receiving real-time investment recommendations and make trading decisions effectively.

Considering all these things, the financial services industry is being revolutionized by AI and ML technologies. This innovative revolution is empowering banking organizations with the ability to analyze data, forecast market trends, and make well-informed investment decisions. 

6. Risk Management 

Artificial Intelligence is significantly transforming risk management in banking systems. It provides advanced analytical capabilities that find, evaluate, and mitigate various risks effectively. 

By leveraging sophisticated algorithms, Artificial intelligence facilitates fraud detection. It analyzes the transactional data instantly to prevent financial losses and protect the user against identity theft.  Additionally, AI-enabled banking systems can also analyze market data and modify investment strategies proactively, 

The icing on the cake is that AI also has the superpower to mitigate the monitoring processes and automate tasks. And it can also tackle the compliance risks through continuous monitoring and ensure regulatory compliance.

Finally, Banks and Finance companies can use AI to optimize portfolio risks by rebalancing the portfolios dynamically based on the risk-return profiles and ensuring optimal performance while keeping the maintainable risk levels in check. Overall, AI empowers banks to strengthen their risk management frameworks. It enhances decision-making processes, and safeguards financial stability in a dynamic business environment.

7. Regulatory Compliance 

Banks operate in a strictly regulated environment where the government has regulatory authority to safeguard against financial fraud and mitigate the risk of high-level finance defaults. Banks have internal compliance teams that are responsible for monitoring compliance with legal requirements to enforce these regulations. But as we know, regulation changes from time to time. Compliance procedures require a manual workforce and resources and even after this, the process is prone to human error. This is where Artificial intelligence and Machine Learning enter the game. Both of these technologies offer optimal solutions to automate and streamline compliance operations in banking.

Utilizing deep learning and natural language processing (NLP) in AI systems within the banking infrastructure is a good move to make. It can analyze and implement the latest compliance requirements for financial institutions. AI systems can help banks to automate the reading and comprehension of regulatory updates. This further enhances the decision-making process within compliance departments.

Although artificial intelligence (AI) cannot completely replace human compliance analysts, it greatly speeds up and increases the effectiveness of their work. AI frees up analysts’ time to concentrate on higher-priority tasks that require human judgment and expertise by automating repetitive chores like going through regulatory paperwork and finding pertinent modifications. Furthermore, banks can respond more swiftly and nimbly to changing regulatory environments by utilizing AI-driven insights, which lowers the risk of non-compliance.

Use Of AI in Banking- Real-World Examples

Many leading banking institutions are harnessing the capabilities of Artificial Intelligence. Below we have mentioned the real examples of the use of artificial intelligence in banking. These instances serve as compelling evidence of AI’s profound impact, demonstrating its ability to revolutionize traditional banking practices:

1. Bank of America 

Commonwealth Bank of Australia encountered many challenges while analyzing the vast amount of data (which includes both structured and unstructured data). This data analysis practice helped them to identify new opportunities. To resolve this problem, the bank partnered with H2O.ai and used their Document AI product. As a result, within 4 years, the bank automated document processing (able to process millions of documents in a day). The bank’s customer onboarding procedure became faster along with compliance with regulations.

By extracting all the critical details such as name, date of birth, and residential and postal addresses from documents such as driving licenses, passports, and other identity-proof documents. This in turn helped the bank to efficiently facilitate customer verification and avoid any human errors.

H2O.ai provided predictive analytics services to the bank so that it could forecast cash flows and invoices for both business and retail customers. They improved their smartphone app with artificial intelligence (AI), allowing users to handle erratic income or unforeseen expenses instantly. The bank also used AI models to provide services like the Green Loan, which finances solar panels or battery purchases. These initiatives are intended to lower energy use and carbon emissions. Additionally, they coordinated a range of services for clients throughout the bank’s network, working with partners like CoGo and Amber Energy to deliver all-inclusive solutions.

2. Federal Bank Limited

Federal Bank developed an advanced AI personal assistant that achieved over 100% response accuracy and maximized customer satisfaction by 25%, increasing the bank’s trustworthiness for complex AI banking transactions with Dialogfloe. 

By reinventing the banking experience, Federal Bank set out to significantly increase customer satisfaction in June 2020. The banking institution teamed up with a pioneer AI company whose products have been utilized by 36 million users in 160 countries and 27 languages. The company developed an AII-powered virtual assistant to manage intricate banking operations seamlessly and satisfy client queries.

This adaptation of Artificial intelligence by Federal Bank resulted in a remarkable 98% accuracy in answering customer queries. They doubled their virtual banking capacity, handling 1.4 million queries annually. Additionally, they project a 50% reduction in customer care costs by 2025 through AI automation.

3. Citi Bank

Citi Bank has made significant technological advancements by automating trade-related documentation. The bank deployed artificial intelligence systems to reduce the pressure and regulatory standards of trade compliance. Citi has also created a product named Citi® Smart Match. It combines Citi’s assets with a fintech partner’s AI and machine learning technology to create substantial benefits for its clients’ businesses. 

4. Capital One

Capital One is also a good example of using AI in banking efficiently. The bank introduced a virtual AI assistant Eno in 2017. Users can interact with this assistant using a mobile application, text, email, or on a desktop. Eno handles all the queries in text, lets the users receive fraud alerts, and takes care of various tasks. These tasks include paying credit card bills, tracking account balances, and checking available credit and transactions. This virtual AI assistance can replicate the human tone and even uses emojis.

5. JP Morgan

JPMorgan Chase admitted to using the underlying AI-powered large language models to validate payment for over a few years now. Artificial intelligence is also helping JP Morgan Chase accelerate processing through reduced fraud levels and improved customer satisfaction with a 15 to 20 percent reduction in the rejection of account validation. J.P. Morgan also uses AI to provide clients with timely insights, such as cash flow analysis, based on their needs.

Final Words 

In simple words, the fusion of AI with banking infrastructure is giving birth to a new era of innovation and efficiency, As we stand there to witness this transformation, it’s obvious that the impact of AI in banking will extend far beyond just automation. Artificial Intelligence is resourceful in elevating customer experiences, upgrading security defenses, and empowering bank organizations to steer over complex regulatory compliances. 

The road to banking powered by AI is not without its difficulties, though. Organizational obstacles, privacy issues, and regulatory concerns need cautious navigation and calculated application.

Yet, amidst these challenges lies limitless opportunities. By utilizing the revolutionary potential of AI, banks can open the doors of growth, differentiation, and sustainable value creation.

In the end, the true measure of AI’s impact in banking lies not just in its ability to optimize processes or mitigate risks, but in its capacity to humanize banking, fostering deeper connections, and delivering meaningful value to customers and communities alike. Together, let us embark on this journey of innovation, collaboration, and empowerment, as we usher in a new era of AI-driven banking excellence.


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