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The Impact of AI on the Future of Digital Banking

(11 min)

The introduction of artificial intelligence (AI) has transformed many industries, including banking. Traditional AI/ML solutions have been widely adopted in the banking industry since the late 2000s. Yet, with the recent AI advancements, such as ChatGPT and generative AI, which have been made available for public use, financial organizations are at the doorstep of a fundamental transition. 

Automation of complex processes, robust cybersecurity, and hyper-personal customer experiences – these are only a few of the many benefits AI gives to modern banking. According to the survey of WifiTalents, the gains from AI integration are tangible and versatile, including: 

  • Loan default rate reduction by up to 4%. 
  • Credit scoring accuracy increased by 20%.
  • Money laundering scheme detection up to 45%. 
  • Customer retention improvements by 10%+.

From 60% to 80% of senior executives in banks recognize AI’s transformative potential in customer service improvement, service personalization, and regulatory compliance risk reduction. That’s why the financial industry players invested over $35 billion in AI only in 2023, with the share of banks exceeding $21 billion. 


The global AI in the banking market is expected to reach $130 billion by 2027, which also confirms the acceleration of AI investments. The most active banks in terms of AI adoption are Capital One, JP Morgan Chase, and the Royal Bank of Canada, while dozens of other banks are also following the lead and transforming their processes with AI innovations. 

How Is Artificial Intelligence Disrupting the Banking Industry? 

AI offers a lot of disruptive innovations for the banking industry, from personalized customer service to efficient and secure operations. The most notable changes you may expect from AI include: 

  • Automation. AI enables next-gen automation by streamlining major routine tasks within banks and other financial organizations. These innovations remove the need for human engagement in many everyday operations and free human resources for more advanced and strategic tasks. 
  • Efficiency and speed. With automation come efficiency and speed enhancements in the banking industry. Automated AI-enabled operations bring down the risk of human error to zero, thus making banking operations more precise. The decision-making cycle becomes quicker with many automated steps. 
  • Customer service disruptions. The degree of personalization and access has reached unprecedented heights in customer service. Thus, modern users of banking services enjoy quick, timely, and hyper-individualized support. 
  • Security advancements. Cybersecurity has also reached a new level of accuracy and timeliness with AI advancements. KYC automation and fraud detection enhancements make financial operations in the online space much safer than before. 
  • Customization of financial advice. AI algorithms continually collect and analyze tons of user data, which enables financial institutions to understand their customers better. Based on those insights, banks can issue personalized offers that their clients really need. 

Applications of AI in Banking and Financial Services 

To date, AI technology has found a broad range of applications in the digital banking, FinTech, and DeFi niches due to the benefits it offers. The most popular use cases for AI are: 

  • Process automation – AI can embrace many routine tasks that humans used to perform, doing it faster and without human error.  
  • Voice and facial recognition – chatbots that understand voice commands and advanced biometric authentication methods improve customer experience and enhance security. 
  • Data collection and analysis – AI systems can analyze big data much faster and more accurately than usual algorithms do, thus allowing financial institutions to make data-driven decisions. 
  • Regulatory compliance monitoring – well-trained AI algorithms can effectively scan tons of compliance documents in real-time to ensure regulatory compliance and bring down compliance risks to a minimum.  
  • Chatbots – AI bots use advanced NLP technology to interact with customers in a human-like manner and give assistance and support at a human level.  
  • CX advancements – customer conduct analysis and advanced segmentation allow banks to reach out to clients with individually tailored offerings, which is always positive for the customer experience. 
  • Cybersecurity and fraud prevention – AI can spot previously untraced suspicious activities and transactional patterns to allow more sensitive threat and fraud identification and prevention.  
  • Market trend analytics. 
  • Predictive analytics – AI/ML applications allow precise forecasting and prediction in the banking sector, from demand forecasting to market sentiment analysis to stock price and revenue predictions.  
  • Risk management – AI enables more sensitive credit risk management by adding many fuzzy factors to the applicants’ creditworthiness analysis. 

Leveraging Generative AI in Digital Banking 

Generative AI has become a new step in the development of artificial intelligence technology. It has transformed the banking sector in many aspects, including: 

  • Modeling analytics 
  • Automating manual tasks 
  • Synthesizing unstructured data 
  • Risk management 
  • Regulatory compliance 
  • Virtual expert assistance to clients 
  • Better coordination of departments 

Generative AI applications in banking mainly relate to automated 24/7 customer service with improved response times and personalized financial planning and recommendations. AI innovations can also inform sentiment analysis for better decision-making, algorithmic trading, and personal investment advice and strategy development.

These changes mean that anyone can take advantage of generative AI for their digital banking business by working with reliable banking software provider companies and developing efficient AI-powered software solutions. 

Artificial Intelligence in Digital Wallets and Payments 

The future of AI in banking is also connected with the integration of AI technology in payment solutions, such as digital wallets and payment gateways. The main role of AI is attributed to fraud detection, user authentication, and KYC automation. AI-powered wallets offer better customer service, robust security protections, and individualized customer support that usual payment solutions may still lack. 

The RNDpoint team of experts can also develop customized digital wallet solutions for your organization, thus boosting your digital transition and making the process of AI adoption smoother. 


Key Benefits of Using AI Solutions in Digital Banking 

There is a wide range of beneficial AI solutions for the banking industry’s participants that can improve productivity, enhance automation, and boost revenue. The banking service aspects expected to experience the greatest AI impact are as follows. 

  • Customer experience personalization. The banking industry is highly competitive, with customers continually looking for better experiences. Thus, banks compete for loyal clients with diverse services, quicker customer support, personalized offers, and 24/7 availability of competent assistance. AI has boosted many of these features for banks by enabling personalized offers based on customer preference analysis, quicker KYC verification, and error-free, quick service. 
  • Reducing operational costs. AI is great at service automation, which is a great contribution to operational cost reduction. With AI technology completing routine tasks faster than humans and doing them many times quicker, banks have received a unique productivity booster. 
  • Security advancements. AI has improved cybersecurity immensely by adding real-time analysis of customer transactions and anomaly detection features, which enable early fraud detection and prevention. Besides, AI-powered tools for user authentication and document authorization boost and automate the full spectrum of security operations. 
  • Smart credit risk scoring. A traditional balanced scorecard system is no longer relevant, and AI has added flexibility and predictive analytics to credit scoring. Advanced credit risk management software is becoming increasingly commonplace in the present-day banking sector. With these tools, banking organizations can make better credit decisions based on the anticipated client income instead of focusing solely on credit history. AI-powered credit risk analysis also reduces the ratio of bad credit.  
  • Chatbots. Another aspect of customer service improvement and personalization is the use of smart AI chatbots for service recommendations, troubleshooting, and advice. These chatbots are available 24/7 and can sustain high workloads even during peak hours of customer inquiries, so they have transformed customer service and have contributed to more personalized assistance.  

Real-World Examples of Successful AI Implementation in Banking 

Dozens of frontline banks in different regions are currently exploring AI for operational improvements. Some of the best-known examples are: 

  • JP Morgan Chase. The bank was one of the first to embark on active AI integration. It applies AI for new product development, fraud detection, customer engagement enhancements, and risk management. The bank’s investment department also uses automated decision-making with AI analytical tools. According to the bank’s CEO, Jamie Dimon, AI introduction can reduce the staff’s workload for regular tasks to 3.5 days a week. 
  • Bank of America. This institution was one of the early adopters of the pre-ChatGPT AI technology. The bank announced a $3.8 billion investment in AI for 2024, as its AI virtual assistant Erica, launched in 2018, exhibited healthy gains in user adoption. Besides Erica, which surpassed 1.5 billion user sessions last July, BofA offers an AI-powered CashPro chatbot for corporate and commercial clients.   
  • Citigroup. This bank has implemented an AI-enabled credit scoring system to improve the lifecycle of loan origination and disbursement. As a result of this innovation, the organization has quickly achieved faster and more accurate credit decision-making. It has also entrusted 40,000 programmers with AI technology testing to develop an effective algorithm for ensuring real-time regulatory compliance. Citi also uses AI to detect payment outliers, optimize user time online with real-time feature recommendations and navigation assistance, and improve STR rates. 

Challenges and Risks of AI Adoption in Digital Banking 

Along with the immense promise of AI for the banking industry, this technology also comes with specific challenges and risks worth noting. They may potentially hinder AI adoption in the banking sector or create unprecedented operational bottlenecks for the financial organizations that have already invested in AI integrations. 

Data Security 

Banks and financial institutions collect large amounts of data from their customers and open sources every day. Yet, even such data requires proper security protections from breaches and misuse. Therefore, it is essential to work with trusted AI algorithm providers to avoid compliance risks. 

Data Quality 

Well-functioning AI algorithms need to be trained on structured, high-quality datasets that have been adjusted to the machine-friendly format. Besides, data needs to be homogenized for efficient training, which requires many human resources. Otherwise, models trained with poor-quality data are unreliable and unpredictable. 

Privacy and Data Access 

AI algorithms need vast datasets for training, and these datasets are commonly limited to the banking institution’s own data. Big data sets collected online from a heterogeneous user sample are often ethically and legally challenging to use in AI training. Therefore, financial organizations struggle with AI model fine-tuning because of data access limitations. 


No matter how well-trained an AI algorithm is, it is still prone to decision-making biases and a lack of transparency in the decision-making processes it applies. All AI algorithms are trained on historical data, which may also contain poor human judgments, so they are vulnerable to the same mistakes. 

Implementation Costs 

AI is still an innovative technology, so its implementation is a cost-intensive project for any banking institution. Only large-scale banks with extensive budgets for innovation can afford to develop, train, and integrate top-quality AI algorithms, while many financial businesses may still find these integrations unaffordable. 

Compliance and Ethical Hazards 

Data privacy concerns discussed above point to another challenge for AI algorithm implementation – GDPR and financial industry-specific privacy guidelines. The ethics of AI usage primarily relate to the risk of bias and discrimination to which AI models trained on biased data are also vulnerable. These risks can be overcome only with a consistent policy for AI implementation based on the principles of fairness and transparency. 

Why Should Banks Consider Becoming AI-First? 

Banks have always been innovation-oriented, which is the foundation for staying competitive and relevant to clients. After ATM introduction and the adoption of 24/7 online banking and mobile banking solutions, AI is offering a new turn in banking innovation. 

Given the gains and operational improvements that AI integration promises to any banking institution, becoming AI-first is seen as imperative in the coming years. The AI-first mindset can give banks a vital competitive advantage by enabling: 

  • New value propositions based on the in-depth analysis of customer preferences. 
  • Delivery of distinctive customer experiences.
  • Cost reduction across multiple banking operations. 
  • Improved access and connectivity. 
  • Improved decision-making. 
  • Better risk control. 
  • Improved speed and accuracy of banking processes. 

Besides, it’s important to keep in mind the extensive revenue-boosting potential of AI adoption, which comes from cost reduction, automation, and higher client retention rates, all leading to revenue growth. Overall, AI usage in banking unlocks the immense innovation potential for all banking industry players, with a wide range of AI opportunities still unlocked and unrealized. 

How RNDpoint Can Help You Transform Your Customer Experience 

As soon as you realize the immense potential of AI integrations for your financial business, it’s time to find a reliable partner to implement this technology and supercharge your enterprise. By coming to RNDpoint, you can embrace the rich experience of our large team and get state-of-the-art AI solutions tailored to the financial sector. 

RNDpoint specializes in AI for retail, FMCG, e-commerce, and banking. We know everything about the recent payment FinTech trends and can provide an in-depth consultation on all issues of AI integration in your financial business. Our team is well-versed in using AI for your revenue growth management, demand forecasting, customer analytics, fraud prevention, and compliance improvements, among other objectives. Feel free to apply our AI solutions to solve a number of commercial issues and explore the untapped potential of AI-powered business functionalities. 


What is the role of AI in digital banking? 
How do big banks adopt artificial intelligence? 
What are the main benefits of AI in digital banking? 
How can banks use AI to personalize customer service? 
How does AI help banks with risk management? 
What are the trends of AI in banking? 


Andrew Klesov
Andrew Klesov
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