AI in Banking – How is Artificial Intelligence Used in Banking?

AI’s amazing capabilities are impacting almost every industry including the banking and finance sector. The emergence of AI in banking operations has made the sector more customer-focused and technologically relevant.

AI-powered solutions are streamlining operations, boosting productivity, and enabling data-driven decisions that put the customer at the center. Also, intelligent algorithms are helping banks spot fraudulent information in a matter of seconds.

According to a Business Insider report, 80% of banks recognize the potential benefits of AI in banking. Another report by McKinsey estimates that AI's impact on banking and finance could reach a staggering $1 trillion.

These figures clearly show how the banking and finance sector is rapidly embracing artificial intelligence to enhance efficiency, service, and productivity, all while reducing costs.

In this article, we’re going to dig deeper into artificial intelligence in the banking sector, discussing some important use cases of artificial intelligence in banking, how some major banks like SBI have already started using AI in their operations, and some challenges banks might face when adopting AI.

 

Introduction of AI in Banking

In recent years, globalization and increased economic openness have increased competition and customer demands for exceptional service in the banking industry. To meet these challenges and enhance customer loyalty, the sector is embracing advanced technologies like artificial intelligence (AI).

AI applications are transforming nearly every aspect of banking, from deposit-taking and lending to investment banking and asset management. The solutions based on this new technology in banking are boosting speed, accuracy, and efficiency across the board.

Fintech companies are also experimenting with AI-powered chatbots that can interact with customers via text or voice, eliminating the need for human intervention in some situations.

Additionally, AI is being used to extract valuable insights from various data sources, such as annual reports and legal documents, to identify trends and inform better decision-making.

AI algorithms have the remarkable ability to analyze data and learn from past errors through back-testing. This empowers financial professionals to make more informed decisions.

Many existing financial tools, like online financial planning and robo-advisors, have evolved into sophisticated AI solutions, further automating tasks and personalizing experiences.

A recent PWC report predicts that AI could contribute a staggering $16 trillion to the global economy by 2030. These figures highlight the profound impact AI will have on various industries, with banking and finance being at the forefront of this transformation.

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Some Major Use Cases of AI in Banking

Artificial intelligence has become an integral part of our daily lives, and banks have already started using this technology in their products and services. The following are some of the major use cases of AI in the banking sector:

Cybersecurity and Fraud Detection

Many digital transactions are happening daily as users pay bills, withdraw money, deposit checks, and do much more via UPI payment applications or online banking. Therefore there is a growing demand for the banking sector to enhance its fraud detection efforts.

In this case, AI in banking comes into play. AI and machine learning (ML) help banks identify fraudulent activities, track loopholes in their systems, reduce risks, and improve the overall security of online finance.

For example, Danske Bank uses AI for fraud detention. The largest bank in Denmark, Denske implements a fraud detection algorithm in its business.

The deep learning tool enhanced the bank’s fraud detection capabilities by 50% and reduced false positives by 60%. The AI-powered fraud detection solution also automated a lot of big decisions while routing some cases to human analysts for further inspection.

In 2019 the finance industry accounted for 29% of all cyber attacks, making it a more targeted sector and now AI has the power to help banks manage cyber threats.

With the amazing continuous monitoring capabilities of AI in financial services, banks can respond to cyberattacks before they affect employees, customers, or internal systems.

Related Read: What is the Future of Cyber Security? Latest Trends, Threats, and Tips to Stay Ahead

AI Chatbots

When it comes to the practical use of AI in banking, one of the best examples is Chatbots. Once implemented, they work 24*7, unlike humans with fixed working hours.

They also keep learning about a particular customer’s usage patterns, which helps chatbots to understand the requirements of a user efficiently.

By using chatbots in bank app development, banks can ensure they are always available for their customers. Moreover, chatbots can offer personalized customer support reduce the workload on emailing and other channels, and recommend suitable financial services and products,  by understanding customer behavior.

One best real-time examples of AI chatbots for banking apps is Erica, a virtual assistant from the Bank of America.

This artificial intelligence chatbot efficiently handles credit card debt reduction and security updates. This helped Erica to manage over 50 million client requests in 2019.

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Loan and Credit Decisions

Another goal of artificial intelligence in the financial sector is to use AI-powered systems to make more safer, and profitable loan and credit decisions. 

Today, many banks are too confined to the use of credit history, credit scores, and customer references to decide the creditworthiness of an individual or company.

However, these credit-reporting systems are frequently riddled with errors, missing real-world transaction history, and misclassifying creditors.

An AI-powered loan and credit system can look into the behavior and patterns of customers with limited credit history to discover their creditworthiness.

Moreover, the system sends warnings to banks about specific behaviors that may improve the chances of default. In short, AI in banking is playing a major role in changing the future of consumer lending.

Tracking Market Trends

Artificial intelligence in finance assists bank institutions in processing large volumes of data and predicting the latest market trends.

Advanced machine learning algorithms help evaluate market sentiments and suggest investment options. Artificial intelligence software also suggests the best time to invest in stocks and warns when there is a potential risk.

Thanks to its high data processing capacity, this emerging technology also helps enhance the speed of decision-making and makes trading convenient for banks and their clients.

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Customer Experience

One of the biggest roles of artificial intelligence in banking is improving customer experience. Customers are always looking for better experiences and higher convenience.

That’s the reason why ATMs were a success as it allow customers to avail of essential services depositing and withdrawing money even during the non-working hours of banks.

The more convenience customers look for, the more companies are seeking innovative ways. Implementing AI in the banking and finance sector enhances the consumer experience as well as the level of convenience for users.

AI reduces the time required to record Know Your Customer (KYC) information and eliminates errors. Also, it allows banks to launch new products and financial services on time.

The technology also automates the eligibility for cases like applying for a personal loan or credit card, which means clients no longer need to manually go through the entire process.

AI-based banking software also reduces approval times for facilities such as loan disbursement

In short, there are many ways artificial intelligence in the banking sector in India helps banks improve their customer services and overall experience.

Data Collection and Analysis

Banks and finance institutions record millions of transactions every single day. And as the volume of information generated is huge, its collection and registration become overwhelming for employees.

Additionally, structuring and recording such vast amounts of data without any error becomes impossible.

In such scenarios, AI in banking helps in efficient data collection and analysis. As a result, this improves the overall user experience. The information can also be used for spotting fraud or making other important decisions.

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Risk Management

External global challenges such as currency fluctuations, natural disasters, and political unrest have a substantial impact on the banking and finance sectors.

During such turbulent times, making business decisions with extreme caution is critical.

Generative AI services in banking deliver insights that paint a fairly accurate picture of what is to come, helping you to be prepared and make timely decisions.

AI finance solutions can also help identify risky applications by assessing the possibility of a client failing to repay a loan. It predicts future behavior by analyzing past behavior patterns and smartphone data.

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Regulatory Compliance

Banking is one of the most closely regulated industries of the global economy.

Governments use their regulatory authorities to ensure that banking customers do not use banks to commit financial crimes and that banks maintain appropriate risk profiles in order to avoid large-scale defaults.

Banks typically have an internal compliance team to handle these issues, but manual processes take far longer and need significant investments.

Compliance requirements are also susceptible to regular modification, therefore banks must constantly update their procedures and workflows to ensure that they comply with these regulations.

AI and machine learning software in banking uses deep learning and NLP to read new compliance requirements for financial institutions and improve their decision-making process.

Even though AI in banking can’t replace compliance analysts, it can make their operations faster and more efficient.

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Predictive Analytics

One of the most popular use cases of AI in the banking industry involves general-purpose semantic and natural language applications and broadly applied predictive analytics.

Solutions of AI in financial services can find specific patterns and correlations in the data, which traditionally was not possible.

These patterns can tell about untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue stream.

Process Automation

Robotic process automation (RPA) algorithms improve operational efficiency and accuracy while lowering costs by automating time-consuming and repetitive processes.

This also allows users to focus on more complex operations that require human intervention.

Today, banking organizations successfully use RPA to speed up transactions and improve efficiency.

For example, JPMorgan Chase's CoiN technology processes documents and extracts data far faster than people. Read the linked blog to read about how RPA is altering the insurance industry.

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How Some of the Leading Commercial Banks in India are Using AI Applications

Now that we’ve covered some of the key AI applications in the banking industry, it’s obvious you would be interested in learning about some of the real-time examples.

If we talk about especially India, AI is becoming increasingly popular, with 32% of financial services providers using this technology.

Artificial intelligence is being used by some big commercial banks, including SBI, HDFC, Axis Bank, ICICI Bank, and others, to streamline their business processes.

According to some latest reports,83% of Indian bankers and 77% of them feel that banks must effectively create and/or use AI solutions. 

On that note, let’s take a look at some leading Indian banks that use AI in banking processes:

State Bank of India (SBI)

SBI, the largest public sector bank with 420 million customers, has started using AI by launching “Code For Bank” to focus on technologies such as predictive analytics, fintech/blockchain, digital payments, IoT, AI, machine learning, BOTS, and robotic process automation.

The bank has also launched SIA, an AI-based chat assistant that handles customer inquiries quickly and helps them with everyday banking tasks, just like a bank representative.

ICICI Bank

Another major bank that is using AI in banking operations is ICICI Bank. This is India’s second-largest private sector bank and has deployed a software robotics (robotic software) that focuses on automating office work.

ICICI Bank is the first bank in India to deploy the technology, which emulates human actions to automate and perform repetitive, high-volume, and time-consuming business processes.

The software has allowed the bank’s employees to focus more on value-added and customer-related activities.

The Bank has also introduced an AI-based chatbot, named iPal( chatbot ) which has interacted with 3.1 million customers and has responded to about 6 million queries, with a 90 percent accuracy rate.

ICICI is also considering integrating iPal with existing voice assistants such as Cortana, Siri, and Assistant.

HDFC Bank

HDFC, India's largest private sector bank by assets and the world's tenth-largest bank by market capitalization has also created an AI-based chatbot, “Eva” which stands for Electronic Virtual Assistant.

Eva is capable of understanding knowledge from thousands of sources and providing simple answers in less than 0.4 seconds.

This AI solution allows customers to get information on its products and services instantly, eliminating the need to search, browse, or contact.

HDFC is also experimenting with in-store robotic apps, HDFC’s IRA which stands for “Intelligent Robotic Assistant” robot.

Axis Bank

India’s third largest private sector bank, Axis Bank has released an AI & NLP (Natural Language Processing) enabled app, Conversational Banking, to help their consumers with financial and nonfinancial transactions, answer FAQs, and contact the bank for loans and other products.

Aside from these banks, a survey conducted by the RBI at the end of June 2023 found that almost three-fourths of Indian banks and several NBFCs have developed chatbots and virtual assistants, highlighting the increasing use of artificial intelligence in the banks.

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Challenges in Implementing AI in Banking

No technology is without challenges. When it comes to the use of artificial intelligence in the banking sector, there are some challenges for banks. These include

Lack of Trained Manpower

One of the biggest challenges of bank and finance institutions looking to leverage AI technology is a shortage of trained staff. There aren’t so many data scientists with strong expertise who can work on AI.

Because of this, banks also lack personnel that is up to date on the newest equipment and software.

In solution, the financial services industry must collaborate with Indian institutions to recruit qualified data scientists and create internal training programs that would teach employees how to deploy AI technology for banking operations successfully.

Data Security

As mentioned earlier, the amount of data collected in the banking and finance industry is huge. This data also requires adequate security measures to avoid data breaches or violations.

Therefore, when developing an AI application, it’s crucial to partner with the right technology partner who understands banking well and offers various security options to ensure customer data is safe and secure.

Lack of Quality Data

When using AI in banking, institutions need structured and quality data for training and validation before deploying full-scale AI-based banking software. High-quality data is essential to ensure the algorithm applies to real-life situations.

Moreover, if the data available is not in a machine-readable format, it may result in unexpected AI model behavior.

Hence, banks considering the adoption of AI need to modify their data policies to mitigate all privacy and compliance risks.

Lack of Explainability

AI-based fintech applications offer several ways to improve efficiency, productivity, and customer satisfaction, reduce costs, help make better decisions as well as eliminate errors, and save time.

However, these AI bank apps may follow biases learned from previous cases of poor human judgment.

Minor inconsistencies in AI software don’t take much time to escalate and create large-scale problems, risking the bank’s reputation and functioning.

To avoid such disasters, banks must offer an appropriate level of explainability for all decisions and recommendations presented by AI models.

Banks should understand, validate, and explain how the model makes decisions.

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Why Banks Must Embrace AI-First World?

Although there are some challenges currently, the ideal of embracing AI-first banks is still good for many reasons.

For several years, the banking sector has been transforming from a people-centric business to a customer-centric one.

This shift has pushed banks to take a more holistic approach to fulfill the demands and expectations of customers.

With a customer-centric approach, banks must begin finding ways to serve customers better.

Customers now want a bank to be there for them wherever they need it, this means banks must be available 24 hours a day, 7 days a week, and they also expect their bank to do it at scale.

To meet these customer expectations, banks must face and overcome their internal challenges, legacy systems, data silos, asset quality, and limited budgets, and can leverage the full potential of AI.

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Future of AI in Banking

Now that we’ve discussed the importance of AI in banking and how banks and finance institutions can use it in their operations, it’s clear that the amazing features of AI will make it even more popular in the future.

Artificial intelligence will not only help banks automate several daily tasks, but it will also make the whole banking process smart enough to do away with cyber risks and competition from FinTech players.

AI in the banking sector keeps evolving and innovating with new capabilities and overcoming challenges.

In the next 10 years, we can expect AI to allow banks to leverage human and machine capabilities optimally to drive operational and cost efficiencies and deliver personalized services.

However, all these benefits of AI are no longer a futuristic vision to accomplish for banks. By integrating AI, leading banks in India, as mentioned above, have already taken action with due diligence to reap these benefits.

And now, if you’re intrigued by the idea of using AI in your business, it’s the perfect time to start building your own AI app with the help of a reputable AI app development company.

Feel free to reach out to us, Arramton Infotech, a leading AI app development company, and start developing your banking AI solution today!

Frequently Asked Questions

Q. How can AI be used in banking?

Ans. AI tackles fraud, streamlines loan approvals, personalizes services with chatbots, manages risk, and analyzes data to understand customer needs better.

Q. How does JP Morgan use AI?

Ans. JPMorgan is an AI leader, using it for fraud detection, risk management, high-speed algorithmic trading, and even customer service with AI-powered chatbots.

Q. How do central banks use AI?

Ans. Central banks leverage AI for economic trend analysis from massive data sets, monitoring financial stability for risk identification, and automating regulatory compliance tasks.

Q. How is AI used in payments?

Ans. AI makes payments faster and safer. It identifies fraud in real time, personalizes authorization processes, and even helps develop new payment methods like biometric authentication.

Q. What is the future of AI in banking?

Ans. The future of AI in banking will be super-personalized. AI will tailor everything to your needs, automate tasks for efficiency, and constantly improve security with advanced fraud prevention.

Q. How AI can help investment banking?

Ans. AI can help investment banking by analyzing vast amounts of market data for opportunities, optimizing portfolios with intelligent management, and using AI algorithms for risk assessment and automated trading.

Q. What are the advantages of AI banking?

Ans. AI banking automates tasks for efficiency, provides 24/7 personalized service, strengthens security with fraud prevention, and allows for better risk management with data-driven insights.

Q. How does AI improve customer experience in banking?

Ans. AI chatbots provide constant support, while AI analyzes data to recommend products that suit you. It also automates tasks for faster service and proactively identifies potential issues before they become problems.