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AI in Banking – Explore The Future of Artificial Intelligence in Banks

AI in Banking – Explore The Future of Artificial Intelligence in Banks
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    8 min
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    February 27, 2024 Last Updated: February 28, 2024


According to a survey, the global Artificial Intelligence (AI) in the banking sector is expected to reach 67.57 billion USD in 2025, growing at a CAGR of 23.37% during the forecast period (2020 - 2025).

Another report from Autonomous Next has stated that AI can save 1 trillion USD for the banking industry by 2030.

Moreover, McKinsey’s research stated that AI is going to handle 10 - 25 % of work across bank functions.

Now what do these stats point towards?

Well, these stats indicate the storm that AI is going to bring to the banking sector.

Needless to say, AI in banking is all set to shape the future that promises innovation, efficiency, and personalized experiences like never before. AI is not merely a buzzword but has emerged as a transformative force redefining the banking sector.

From streamlining operations to enhancing customer service, the integration of AI technologies is revolutionizing traditional banking models.

So join us as we embark on a journey where we will be exploring the myriad ways AI is reshaping the banking industry, unlocking new possibilities, and paving the way for a future where banking is smarter, more intuitive, and truly customer-centric.

Let’s Go!

Applications of AI in Banking

From customer service automation to fraud detection, AI is revolutionizing traditional banking processes, enhancing efficiency and security, and doing much more that wasn’t possible before the advent of this technology. Well, let’s go through some myriad applications reshaping the financial landscape.

1Lesser Operating Expenses and Risk

Even though the banking sector operates mostly digitally, it is also rife with manual procedures that involve a lot of paperwork. Banks are exposed to considerable operational risk and expense concerns in these operations because of the possibility of human mistakes.

A large portion of the labor-intensive and prone-to-mistake work associated with inputting client data from contracts, forms, and other sources is being replaced in the banking industry by robotic process automation (RPA) software that simulates rules-based digital processes carried out by humans.

Moreover, when combined with advances in natural language processing (NLP), handwriting recognition, and other AI technologies, RPA bots can handle an increasing number of banking procedures that were previously performed by people. This makes them intelligent process automation tools.

2Better Credit and Loan Decision-making

In a similar vein, banks are utilizing AI-powered tools to assist in making safer, more lucrative, and more informed lending and credit choices. To evaluate whether or not a person or business is creditworthy, many banks still rely too much on credit ratings, credit histories, customer references, and banking activities.

Many would confirm, though, that these credit reporting systems are far from flawless and are frequently rife with mistakes, omitting actual transaction information, and incorrectly categorizing creditors.

Apart from utilizing accessible data, AI-driven loan decision systems and machine learning (ML) algorithms can also analyze patterns and behaviors to ascertain whether a client with a limited credit history could potentially be a good credit candidate or identify clients whose past behavior may raise the risk of default.

The main problem with utilizing AI-based systems to make credit and loan decisions is that they may have bias-related problems akin to those experienced by their human counterparts. This results from the same way, in which models used to make loan decisions are being trained.

To prevent these possible issues, banks wishing to integrate machine learning as a part of real-world, in-production systems must work to eliminate prejudice and include ethical training in their AI training procedures.

3Cybersecurity & Anti-Money Laundering

In the near future, creative financial applications of AI and machine learning will undoubtedly increase banking security. The financial services sector will be able to improve its cybersecurity and anti-money laundering activities with the usage of AI and ML.

Many banks are using AI and ML technologies to spot any suspicious or out-of-the-ordinary transactions that could be connected to money laundering. In the upcoming years, it is anticipated that the use of AI and ML technologies will grow dramatically.

The combination of these technologies will undoubtedly result in a more efficient service that saves money, labor, and time. Furthermore, it will support bolstering banks’, companies’, and people’s security systems to fend off cyberattacks on their networks, accounts, and establishments.

AI development solutions

4Personalized Banking

Personalization is one of the primary advantages of AI and machine learning. The possibility is high that insights, warnings, and suggestions, based on an individual’s spending patterns, existing savings and investment plans, health insurance portfolios, and other variables, will be provided by a bank of the future.

The banking industry is still in the early phases of AI development and application. Predictive analytics, for instance, is already being used by certain banks to identify and anticipate their clients’ financial demands.

This is only the beginning of customized banking; before banks can provide their clients with individualized services, they must first understand how they would like to be contacted. Here, a great deal of work has to be done. Banks need massive amounts of dispersed data from several systems and divisions. Artificial intelligence must be used to combine this in order for it to mine the data and provide customers with pertinent insights or guidance.

5Virtual Assistants & Conversational Chatbots

Unquestionably, customers desire to have greater conversational interactions with their banks, the way they get from companies like Uber, Netflix, and Amazon. This is becoming achievable in banking too. Well, the credit goes to the introduction of chatbots and virtual assistants.

Consumers now anticipate better customer service from their banks, just as they have from other businesses. Artificial intelligence-driven chatbots and virtual assistants carry the ability to offer 24/7 client support on issues like bank account balances and other transactions. Additionally, they can enable users to transmit money by utilizing conversational language.

6AI-Powered Fraud Detection

ML systems can sift through millions of data points to find fraudulent activity. It lowers the number of erroneous rejections while raising the caliber of real-time approvals. Rather than finding them after the fact, it will identify specific bank accounts in real time if there is any questionable activity.

Financial institutions spend at least $2.92 in recovery expenses for every $1 they lose to fraud. This is where the banking industry can be severely impacted by AI and ML. Bank monitoring systems exist, however, they often rely on historical payment information. Large credit card transaction databases are used by the ML-powered system, which makes it simple to classify occurrences as fraudulent or not.

7Automated Investment Process

Some banks are further exploring the realm of artificial intelligence by utilizing their intelligent systems to assist in investment decision-making and to bolster their investment banking research.

AI technologies are being used by companies like the Netherlands-based ING and Switzerland-based UBS in order to search the markets for undiscovered investment possibilities and provide insights for their algorithmic trading systems. Even while humans are still involved in all of these investment choices, AI systems are improving modeling and discovery to find new possibilities.

Furthermore, a lot of financial services firms provide their clients with portfolio management assistance through robo-advisers. These robo-advisers can offer superior advice on investment choices and are accessible to customers at any time through customization, chatbots, and customer-specific models.

Also Read: Blockchain in Banking – 10 Key Reasons Banks Adopting Blockchain Technology

8Loan Application Management

As for applicants, the loan application procedure can be time-consuming. Similarly, banks examining candidates’ applications find it laborious too. Here’s how AI can make this process more efficient for those involved.

A consumer seeking a loan can begin with AI banking chatbots. AI will determine if a user’s application complies with the rules and requests by responding to the automated queries. The application only moves on to management for a human evaluation once AI has reviewed it.

The application of AI will also benefit the customer. For help and information on the loan application, they can consult the chatbot directly anytime they want. They will receive assistance and pointers on how to help the process at any moment.

9Improved Regulatory Compliance

Needless to say, banking is among the economic sectors that are subject to the strictest regulations both domestically and internationally. In order to prevent widespread defaults, governments utilize their regulatory power in order to ensure that banks have appropriate risk profiles and that their clients aren’t utilizing them to commit financial crimes.

As a result, banks are subject to a plethora of rules that demand them to protect consumer privacy, know their customers, keep an eye on wire transfers, and stop fraud and money laundering, among other requirements.

Following banking regulations comes with a high price tag and much greater risk if done incorrectly. Consequently, to keep an eye on consumer behavior, audit, and log data to many compliance and regulatory systems, banks are utilizing intelligent AI virtual assistants to monitor transactions.

Big data-enhanced fraud protection has already had a big influence on credit card procedures. It also had a substantial impact in other areas, such as loan underwriting. AI-based technologies let banks practice proactive regulatory compliance while lowering total risk by focusing on client behaviors and trends rather than particular laws.

10Prediction of Future Outcomes and Trends

Future outcomes and trends are easily predicted by AI, which assists banks in doing so by using its ability to forecast future situations based on historical behavior. This aids banks in spotting fraud, spotting anti-money laundering trends, and offering advice to customers. 

Money launderers provide the impression that the source of their illicit funds is legitimate via a number of their acts. AI recognizes these covert acts and helps banks save millions of dollars. Well, the credit goes to its powers in machine learning and cognition. In a similar vein, AI can handle fraud by identifying questionable data trends within enormous amounts of data.

Additionally, AI uses its main recommendation engines to analyze historical data in order to forecast how data points will behave in the future, thus enabling banks to successfully upsell and cross-sell.

Also Read: Top AI Trends in 2024: How It Can Transform Enterprise Businesses?

Implementing AI in Banking

Similar to how cloud platforms have developed recently, banks need to look past the hype and take into account the real-world uses of AI. Even though there are uses of AI that have been shown to work, many banks still view the technology as experimental, and many of their pilot programs never go beyond the stage of full-scale adoption.

In order to get the best results, banks must give another thought to their approach to machine learning and artificial intelligence and engage in an AI implementation path. Well, here is the procedure for successfully implementing AI in banking.

Implementing AI in Banking

Step 1 :Create an AI plan

Change from being an AI firm and focusing on the how of execution to merely employing AI capabilities.

Step 2 :Describe a use case-driven procedure

Instead of concentrating on constrained AI solutions, concentrate on business value-driven use cases and invest in a variety of AI capabilities.

Step 3 :Test out prototypes

Provide a basis instead of just a notion, and become ready for strategic alignment.

Step 4 :Build confidently

Transition from a reactive to a proactive approach by prioritizing risks and ethics, exploring new alliances, and striking a balance between convergence is the real key.

Step 5 :Scale for business deployment

Make the AI talent list a “must-have” instead of a “nice-to-have,” and replace inflexible operating models and technologies with adaptable ones that will make the entire company more agile.

Step 6 :Promote long-term results

Go beyond merely putting AI into practice and learn how to improve capabilities and extract more value from deployed apps.

 AI in banking solutions

Top 3 Major Challenges of Implementing AI in Banking

When utilizing AI technology, banks face a number of obstacles, from a lack of reliable and high-quality data to worries about data security. Let’s go through major challenges.

  • Data Security

The use of AI in banking necessitates the gathering and management of massive amounts of data, a significant portion of which comprises sensitive information. Thus, to protect this data, further security measures need to be implemented. It is essential for banks to look for technology partners that can provide a variety of security alternatives in order to guarantee the correct management of consumer data.

  • Lack of High-quality Data

Financial institutions need organized, high-quality data for training and validation before implementing a full AI-based banking solution. Moreover, AI algorithms demand high-quality data to perform well in real-world situations. Furthermore, unanticipated behaviors in the AI models might result from the data not being in a machine-readable format. To reduce privacy and regulatory issues, banks that want to use AI technology must modify their data policies.

  • Explainability Challenges

AI-based systems might unintentionally reinforce biases from previous cases of human mistake, even when they significantly improve decision-making processes by lowering errors and saving time. The reputation and operational integrity of a bank are seriously at risk from even small anomalies in AI systems that have the potential to grow rapidly.

Banks need to make sure that all judgments and suggestions made by AI models have a sufficient degree of explainability in order to prevent future disasters. It becomes essential to comprehend, validate, and articulate these models’ decision-making processes in order to promote confidence and reduce risks.

Bonus Read: AI Applications & Use Cases: Revolutionizing Major Industries

How Blocktunix Can Help You in the AI Banking Journey?

The future of banking is undoubtedly intertwined with the transformative power of AI. As we have explored, AI offers a myriad of opportunities for banks to enhance efficiency, personalize customer experiences, mitigate risks, and stay ahead in an increasingly competitive landscape.

This is where you can take advantage of Blocktunix. As an AI development company, we are at the forefront of this revolution, empowering banks with top-notch solutions that harness the full potential of AI.

Needless to say, together, we can unlock new realms of possibility, redefine the banking experience, and pave the way for a future where innovation knows no limits.

So join us in shaping the future of banking with AI. Let’s embark on this journey together, where technology meets ingenuity, and possibilities are limitless. The future is here, and it’s time to embrace it with Blocktunix as your technology partner.

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