29 AI in Banking Statistics To Help You Plan Your Future


AI in Banking Statistics

If you’re like most people visiting this page, you’re probably looking for statistics on AI in banking. We know that it can be hard to find the latest numbers on the subject, and that’s why we’ve put together this list of AI in banking statistics.

You’re looking for some answers, but you can’t find them. You know that AI is transforming the banking industry, but you don’t know how to quantify the change it will make or how quickly it will happen. You want to be able to talk about AI in banking with your coworkers, but the only thing you have to go on are a few outdated articles.

It doesn’t have to be this way. We know how frustrating it can be when there aren’t any good resources out there that help answer your questions about a topic as important as AI in banking. 

So that no matter what question you have about the state of AI in banking, this article can help answer it. If you just want a quick overview of how big this industry is getting, or if you need to get more specific details about what kinds of roles people with a background in math or science tend to gravitate toward when they first enter the field, then we’ve got exactly what you need.

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Powerful AI in Banking Statistics To Enthrall You [Editor’s Choice]

  • HDFC’s Electronic Virtual Assistant (EVA) has responded to more than 5 million inquiries with at least 85% accuracy.
  • AI technologies can potentially provide an additional $1 trillion each year in the value of global banks.
  • The global AI in banking market is expected to grow at a CAGR of 32.6% between 2021 and 2030.
  • In 2019, North America accounted for 35.3% of the total market size of global AI in banking.
  • 63% of investment banks worldwide use machine learning.
  • 60% of investment banks around the world use AI in predictive analysis.
  • Banks have reported six times more leads collected using chatbots compared to traditional lead generation.
  • By 2023, banks are expected to save $7.3 billion in operational costs due to the use of chatbots.
  • Artificial intelligence expands loan accessibility, approving 27% more loan applicants and yielding 16% lower interest rates.
  • In 2020, around 32% of banks were using artificial intelligence.
  • 80% of banks are highly aware of how AI and machine learning can potentially benefit them.

Statistics of AI in Banking in the United States

1. Three-quarters of banks with more than $100 billion in assets report applying AI strategies.

(Business Insider)

One of the roadblocks to AI implementation is the cost. With larger-sized banks, they have more resources available to apply AI strategies to their systems and processes. While 75% of banks with more than $100 billion in assets are currently using AI strategies, only 46% of banks with less than this value in assets are currently implementing it.

 

2. 80% of banks are highly aware of how AI and machine learning can potentially benefit them.

(Business Insider)

A huge majority of banks worldwide are already beyond aware of the potential benefits of AI implementation. However, there are roadblocks that need tending to. For one, AI and machine learning are not cheap systems. That’s why a majority of banks that can implement AI are those with a high value of assets.

 

3. In 2020, around 32% of banks were using artificial intelligence.

(Business Wire)

This included the use of AI applications and technologies in voice recognition, image analysis, RPA, and predictive analysis, among others. Banks did this mainly to gain an advantage against their competitors in the market. AI has allowed banks to manage massive volumes of data at faster speeds.

 

Benefits of AI in Banking Statistics

4. The potential savings banks will get from AI applications is an estimated $447 billion by 2023.

(Insider Intelligence)

Artificial intelligence is shaping the way companies and consumers access and monitor their finances. With this kind of savings, banks are looking for new methods to implement this technology into their products and services, ranging from task automation to fraud detection.

 

5. By using AI, banks are generating nearly 66% more revenue from mobile banking compared to traditional banking.

(Finextra)

The revenue from mobile banking has increased drastically compared to the time when customers would visit banks physically—all thanks to AI implementation. Nowadays, you can send money to your friend by simply voicing a command through Siri and confirming the transaction via Touch ID.

 

6. Banks using Big Data analysis to generate their estimated profit have stated an average of 8% increase in revenue and a 10% reduction in costs.

(Finextra)

Applications that use artificial intelligence can gather and analyze huge amounts of data to help improve user experience. Data this huge can then be used in loan approval or fraud detection, among other things.

When it comes to credit cards, AI can help reduce delinquencies, which have risen by 1.4% in the US.

 

7. 35% of banks stated that data science and AI positively impacted the technologies around remote working and overall security.

(Allied Market Research)

Banks have seen the benefits of AI, particularly during the pandemic when consumer queries increased exponentially. Because of their foreseen benefits, these technological developments are expected to offer lucrative opportunities for the market to expand.

 

8. Artificial intelligence expands loan accessibility, approving 27% more loan applicants and yielding 16% lower interest rates.

(Datamation)

AI also has a tremendous impact on the lending industry. Lending institutions are now able to approve more loans at record speed.

What does this mean for these companies? Customers are more satisfied, interest rates are lower, and revenue has increased. Where we had to wait days or weeks for a loan to be approved by a lender, nowadays, digital options allow loans to be approved within minutes.

 

The Application of AI Chatbots in Banking

9. The prediction is that 95% of customer interactions will be supported by AI by 2025.

(Alacriti)

This will come as a result of increased investment in chatbots for customer service as more companies become interested in this AI technology. However, the increase in AI-supported customer interaction has to be effective and relevant.

Otherwise, this could irk customers and decrease customer satisfaction. Some people just prefer to speak to a live human being, after all.

 

10. The use of chatbots in the banking and health care sectors saves agents four minutes per inquiry compared to traditional call centers.

(Juniper Research)

The study was conducted in 2017, but it’s still applicable today. This time savings translates to monetary savings, particularly to the tune of $0.70 per inquiry.

 

11. By 2023, banks are expected to save $7.3 billion in operational costs due to the use of chatbots.

(Haptik)

During this year, bank customer service agents are projected to save around 862 million hours, which equates to almost 500,000 working years. The outlook for conversation AI in banking seems promising as it helps banks acquire and serve more customers.

 

12. Banks have reported six times more leads collected using chatbots compared to traditional lead generation.

(Haptik)

These lead-generation chatbots are embedded on the bank’s mobile app or website to start conversations with customers. Their goal is to find out what the customers want to buy and then pique their interest with various solutions that will appeal to them. The leads are then endorsed to the sales team to complete the sale.

 

13. By 2027, the revenue share contribution of chatbots is projected to dominate other solutions in global AI in banking.

(Emergen Research)

The revenue share of chatbots is expected to increase at a whopping CAGR of 43.1%. Among all AI solutions applicable to banking, this holds the highest revenue share contribution.

This coincides with the previous statistic that customer service has the highest revenue share contribution when it comes to the usage of AI as well. Chatbots help banks streamline repetitive back-office tasks, so the segment is expected to grow within the forecast period.

 

In Which Sectors of Banking Is AI Used?

14. 56% of financial companies implemented artificial intelligence in risk management.

(Business Insider)

Artificial intelligence contributes to risk management by being more effective in understanding and mitigating risks. This technology helps banks monitor streams of big data and provide insights in real-time to help protect banks from losses and improve ROI for customers.

 

15. 52% of financial companies implemented artificial intelligence in revenue generation by coming up with new processes and products.

(Business Insider)

Banks use artificial intelligence to drive up revenue. AI helps finance companies come up with new products to offer to customers and new processes that will build on existing solutions to solve complex problems. This adopted technology also helps banks provide personalized services, which drives up customer satisfaction.

 

16. 63% of investment banks worldwide use machine learning.

(Statista)

Investment banking is a high-stake industry, and machine learning is used broadly in front, middle, and back offices. Machine learning can be used to increase a firm’s transparency in line with ESG regulations, improve risk management practices, and enable firms to perform better in trading by keeping their finger on the pulse of the market.

 

17. 60% of investment banks around the world use AI in predictive analysis.

(Statista)

In 2017, the Dutch bank ING launched a tool called Katana. This piece of technology was designed to use predictive analytics in helping traders come up with a price when buying and selling bonds on behalf of their clients. The basis of this analysis was both real-time and historical data.

Dutch bank ING’s use of Katana increased the speed of pricing decisions in around 90% of all trades and reduced the trading cost by at least 25%.

 

18. Globally, 58% of investment banks use virtual assistants, such as chatbots.

(Statista)

To offset rising costs and declining revenues, investment banks have implemented various artificial intelligence strategies in their processes—one of which is virtual assistants or chatbots.

These computer programs can hold conversations with customers via text or audio. Highly advanced chatbots can hold human-like conversations and are highly efficient and accurate.

 

19. 45% of investment banks worldwide have implemented robotic process automation.

(Statista)

RPA or robotic process automation requires low investment and not much time to automate manual processes. Despite this, the benefits are massive. RPA can reduce cost and improve efficiency in back-end processes. It also helps provide a controlled and secure environment, which is vital in investment banking.

 

What Does the Future Hold For AI in Banking?

20. By 2024, the use of online and mobile banking in the US will increase to 72.8% and 58.1%, respectively.

(Insider Intelligence)

Although the transition from traditional banking platforms to digital and mobile banking had already started before the pandemic, it has undoubtedly amplified the transition as lockdowns were implemented globally and more consumers looked for self-service options.

The increased need for online and digital banking is a critical reason why AI implementation needs to be successful in this dynamic industry.

 

21. The global AI in banking market is expected to grow at a CAGR of 32.6% between 2021 and 2030.

(Business Wire)

In 2020, the global market size of AI in banking was $3.88 billion. By 2030, it’s expected to reach a little over $64 billion in value. This is due to the increased adoption of AI to advance digitization in banks and other financial services companies.

By region, North America held the biggest market share in AI in banking, owing to the fact that the demand for modernization in banks has been increasing as of late.

 

Global Statistics for AI in Banking

22. In 2019, North America held the largest market share in global AI in banking.

(Emergen Research)

North America accounted for 35.3% of the total market size of global AI in banking. This is expected to continue in the coming years due to banks in the region increasing their focus on enhancing banking processes using advanced technology.

It should be noted that there is also an increasing demand for risk management solutions that are highly effective. Europe is also expected to have significant revenue growth.

 

23. In 2019, deep learning and machine learning accounted for the biggest market share in AI in banking.

(Emergen Research)

That year, this segment in AI applications accounted for 38.9% of the total market size. Other segments include natural language processing, computer visions, and more.

The machine learning and deep learning segment are anticipated to have the biggest revenue share in the market because of their increasing implementation for risk analysis. It will mainly be used to effectively predict which borrowers are likely to default on their loans.

 

24. The customer service application of AI is expected to have the biggest revenue share contribution to AI in banking around the world.

(Emergen Research)

By 2027, this application segment of AI is anticipated to reach $56.94 billion in value when it comes to revenue. Other applications include back office, financial advisory, compliance and security, risk management, and others.

However, all these other AI uses fall far behind in value compared to customer service. AI has helped banks provide 24/7 customer service to address customer complaints and queries promptly.

 

25. AI technologies can potentially provide an additional $1 trillion each year in the value of global banks.

(McKinsey & Company)

Despite knowing this, many banks worldwide struggle to migrate from experimenting with AI for specific processes to scaling it across the organization. They mostly lack a clear AI strategy, which is also attributed to an outdated collaboration strategy between technology and business teams in the banking sector.

 

26. At the beginning of the COVID-19 pandemic, the use of mobile and digital banking worldwide increased by 20% to 50%.

(McKinsey & Company)

With lockdowns implemented globally as a result of the pandemic that shook the economy of the world, people turned to online options for their banking needs. Globally, an estimated 15% to 45% of consumers want to lessen their branch visits even after the pandemic ends.

This increase in digital options is supported by AI applications, and the increase in demand will also drive up the use of AI applications in various banking processes. It’s safe to say that banks should brace themselves for this change.

 

Real-Life Successes of Artificial Intelligence in Banking

27. The biggest bank in Denmark, Danske Bank, implemented a deep learning tool for fraud detection that proved 50% more accurate at fraud detection and 60% more effective in reducing false positives.

(Datamation)

In line with the increased accessibility of banking services, fraud became commonplace in banks as well. Machine learning is typically faster than humans when it comes to data point connection, and they’re more accurate too. This attribute helps machine learning be an effective way to combat identity theft and fraud.

The larger the data they process, the more accurate these systems become. So, the more banks use them, the better they become. As more banks implement AI for fraud detection, the occurrence of fraud should decrease significantly.

 

28. By the time 2020 started, 12.2 million customers of Bank of America were already utilizing Erica, the bank’s chatbot.

(Alacriti)

Along with other financial institutions, Bank of America has been investing in virtual voice assistants or chatbots to improve their customers’ online experience and significantly reduce costs. While some financial services companies hire their own staff to develop their chatbots, others outsource development.

 

29. HDFC’s Electronic Virtual Assistant (EVA) has responded to more than 5 million inquiries with at least 85% accuracy.

(Haptik)

EVA uses natural language processing to understand user inquiries and respond with appropriate and accurate information fetched from thousands of sources—all in a matter of milliseconds.

The bot has been deployed on Google Assistant, Alexa, and several other platforms.  HDFC’s AI-powered chatbot holds more than 20,000 daily conversations with customers worldwide.

 

Related Questions (FAQ)

How many banks are using AI?

As artificial intelligence technology grows, more industries and companies are starting to adopt it as they see its advantages in their respective fields. In banking and finance, almost 40% to 50% of providers currently use AI in their processes. They’re taking advantage of technology as they see fit.

 

How is AI being used in banking?

Artificial intelligence is used in almost every process in the banking sector. It is implemented in risk management, revenue generation, customer experience, fraud detection, monitoring market trends, loan origination, data collection and analysis, regulatory compliance, and more.

 

How is AI changing the banking sector?

Since its inception and usage, AI has helped in numerous facets of banking. For one, it helps banking and financial service providers streamline their processes, manage customer requests, and make smarter and faster decisions. They also help reduce risks by preventing fraud and money laundering.

 

What is the future of AI in banking?

Regarding the banking industry, artificial intelligence has a wide array of practical uses. It has already gone beyond something experimental. Banks will continue to use AI to improve products and digitize their processes. By 2023, AI is forecasted to save banks a whopping $447 billion.

 

What are the challenges of using AI in banking?

In implementing AI technology in banks, a few challenges are involved. For one, legacy banking systems may make it harder for banks to implement AI. Additionally, there’s a shortage of experience and skills to implement AI accurately. Lastly, AI can be quite costly, which can be a hindrance for some.

 

Conclusion

You now have a ton of information at your fingertips. You know the state of AI in banking and how it is being used across the industry. You’ve learned about many of the statistics on AI in banking, and you can use these to help you make smart decisions about what kind of AI to invest in as a bank or lender.

But if there’s one thing we hope you take away from this post, it’s that this technology is still very new and rapidly changing. There will always be more data to collect, more research to conduct, and new ways for banks to use this technology. As you gain experience with AI in banking, don’t be afraid to experiment with different strategies and find the ones that work best for your business!

We hope you’ve found this information helpful and interesting—if so, please share it with your friends and colleagues who might also be interested in AI in Banking!

If you have any questions or comments about this information, feel free to reach out via email. We always love hearing from you.

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