AI in banking: trends, benefits and challenges

According to several 2025 market reports, about 78% of financial institutions worldwide have deployed AI in banking to optimize operations, speed up transaction processing and improve customer experience. This is no longer an experimental trend but a new competitive standard in digital finance. From loan approval, fraud detection and customer data analysis to 24/7 customer care chatbots, AI is helping banks run faster, more accurately and at significantly lower operating cost.

Meanwhile, fintech companies keep accelerating by putting AI to heavy use in financial products and services. This puts strong pressure on traditional banks to change if they don't want to gradually lose their competitive edge. In this article, MONA Media will walk you through the trends, benefits and challenges of applying AI in banking today.

The current state of AI in banking

Applying artificial intelligence in banking is becoming an important trend in today's digital transformation. From automating operations and improving customer experience to supporting risk control and fraud detection, artificial intelligence helps banks operate more effectively and compete better in an ever-evolving digital finance environment.

How fast AI adoption is growing in banking

Banking is one of the fastest adopters of AI worldwide. According to several market reports, the share of financial institutions deploying AI has grown from about 55% in 2023 to nearly 78% in 2025. This shows AI is no longer an experimental technology but a practical deployment trend in modern banking operations. For a more detailed picture, see the article AI for customer care for a more detailed picture.

In addition, McKinsey estimates that comprehensive AI adoption could add 200 to 350 billion USD in value to global banking each year. Counting the growth of Generative AI, that figure could approach 1 trillion USD. Beyond improving business results, AI also helps many banks cut operating costs by 15 to 30% through process automation and optimized data handling.

≫ See also: Top 7 free AI website design tools SEO-friendly

The picture of AI in Vietnamese banking

In Vietnam, large banks such as Vietcombank, VietinBank, ACB and MB Bank are investing heavily in AI to improve customer experience and optimize system operations.

Some common AI applications today include:

• Customer care chatbots running 24/7
• Real-time transaction fraud detection systems
• AI-based credit scoring models
• Internal process automation combining RPA and AI
• Customer data analysis to personalize financial services

Applying AI doesn't just help banks optimize costs, it also creates a clear competitive advantage as digital banking and fintech grow rapidly.

Standout AI applications in banking 

AI in banking isn't a one-size-fits-all solution for every function. Depending on the area, such as lending, risk management, customer care or fraud detection, banks deploy different AI models to fit their data, operating goals and the results they actually need to achieve.

Data analysis and financial forecasting

One of the AI applications in banking delivering clear results today is data analysis and financial forecasting. Instead of relying only on traditional data such as transaction history or financial statements, AI can also process other sources like user behavior, e-invoices, statements or online data to build a fuller financial picture of each customer.

Thanks to large-scale data processing and real-time analysis, AI helps banks assess risk more accurately, optimize lending processes and make fast financial decisions in a volatile environment. Beyond credit, AI is also used to forecast market trends, manage investment portfolios and optimize banks' operating strategy.

Highlights of AI in lending:

• Bad-debt risk prediction is about 10-20% more accurate than traditional methods
• Loan approval time cut from several days to just a few minutes
• Lower bad-debt ratios thanks to real-time screening and risk monitoring
• More comprehensive credit assessment by analyzing many different data sources
• Faster digital lending and a better experience for customers transacting online

24/7 customer care with chatbots and AI assistants

Today, artificial intelligence in banking is changing how banks care for and interact with customers. Where chatbots once answered only simple questions, AI can now understand conversation context, recommend suitable products and handle many requests right in the bank's app or website.

In Vietnam, VietinBank's internal AI assistant Genie is a standout example, having handled hundreds of thousands of employee questions shortly after launch, significantly shortening support time and optimizing internal operations. Many AI systems in banking can also automatically handle 60 to 80% of common customer requests, improving the experience and keeping support running 24/7.

Requests that are commonly chatbot AI supported include:

• Checking balances and looking up transaction history
• Help with opening cards and activating digital banking services
• Recommending financial products that fit the customer's needs
• Answering questions about loans, interest rates or promotions
• Help with locking cards or handling basic emergency requests

Applying AI doesn't just lighten the load on customer care teams. It also speeds up response, personalizes the experience and strengthens competitiveness in the digital banking era.

Risk management and real-time fraud prevention

One of the most effective AI applications in banking today is risk management and real-time fraud detection. Instead of relying only on fixed rules like traditional systems, AI uses machine learning to continuously analyze transaction behavior, spot unusual signs and raise alerts almost instantly when it detects risk.

Thanks to the ability to process large volumes of data and learn from earlier fraud patterns, artificial intelligence in banking helps financial institutions move from a reactive model to proactively stopping fraud before damage occurs.

Compared with traditional systems, AI helps banks achieve:

• About 1.5 to 3 times better detection of unusual transactions
• 40 to 60% fewer false positive alerts
• Lets AML teams focus on genuinely risky cases
• 15 to 30% lower financial fraud rates in card and online transfer transactions
• Stronger real-time transaction monitoring across multiple channels

Many banks now see AI as an intelligent security layer that strengthens risk control, protects customers and optimizes operations as digital transactions surge.

Internal process automation with RPA and AI

AI in banking is helping financial institutions strongly automate internal processes that used to consume a lot of time and labor. Combining RPA (Robotic Process Automation) with AI, banks can handle batches of repetitive tasks faster and more accurately, and greatly reduce employees' manual workload.

Beyond boosting operating efficiency, AI helps banks optimize resources so staff can focus on advisory work, customer care and business development instead of handling paperwork or data entry as before.

Many banks have automated dozens of processes such as:

• Account opening and updating customer KYC records
• Handling complaints and transaction inquiries
• Automatically printing periodic reports
• Sending notifications based on events or customer behavior
• Automatic data entry and data syncing between systems
• Managing internal document approval workflows

According to several practical reports, applying artificial intelligence in banking can save more than 80% of the manual workload in repetitive processes. Some banks even report savings of tens of thousands of work hours per year, which optimizes operating costs and improves efficiency across the whole system.

≫ See more: What is AI automation? AI Automation applications in business

Smart accounting, payments and personalized financial marketing

AI in banking doesn't only support internal operations. It also helps financial institutions run accounting, payments and personalized customer experiences more effectively. Thanks to real-time data analysis, AI helps banks automatically handle many financial tasks, reduce reconciliation errors and speed up transactions on digital platforms.

In addition, artificial intelligence in banking helps financial institutions understand each customer's behavior, needs and spending habits to deliver better-fitting marketing programs and product suggestions. This raises conversion, improves user experience and boosts customer retention in today's digital competition.

A personalized AI product recommendation system can:

• Suggest credit cards that fit a customer's spending habits
• Recommend loans based on transaction history and financial capacity
• Automatically personalize offers, vouchers or cashback programs
• Analyze behavior to suggest suitable savings or investment products
• Send notifications and marketing campaigns at the moment a customer needs them
• Help optimize the payment experience in digital banking apps

Applying AI helps banks not only optimize financial operations but also build deeper personalized experiences, which raises satisfaction and creates a lasting competitive advantage.

Core challenges of applying AI in banking

These positive results from AI in banking don't mean deployment is easy. In practice, AI in banking still faces many specific barriers involving data, security and complex operating systems. That is also why applying artificial intelligence in banking calls for a long-term strategy and a careful approach rather than rushing to follow the trend.

Information security and customer data

As AI in banking is applied ever more deeply, data security becomes one of the most important challenges. Financial data is highly sensitive, and when it is fed into AI systems and continuously connected across multiple platforms, the risk of leaks or unauthorized exploitation rises significantly. Beyond external attacks, systems can also be affected by techniques such as training-data poisoning or model manipulation, which skew analysis results. This is a major risk of applying AI in banking if it isn't tightly controlled. The article AI for customer care is recommended further reading.

To reduce these risks, many financial institutions are deploying several advanced security measures at once:

• End-to-end encryption for all sensitive data in transit and at rest
• Federated learning to train AI models without centralizing raw data
• A zero-trust model to control every access within the system
• Regular security audits to catch vulnerabilities and hidden risks early
• Strict compliance with international security standards and the banking sector's internal regulations

Balancing the power of technology with data safety is the key factor that determines the long-term success of AI in banking.

Algorithmic bias in financial decisions

In AI in banking, one notable risk is algorithmic bias. Because AI learns from historical data, if that data already contains skew or unfairness, the model can unintentionally repeat and amplify those distortions in financial decisions. This often shows up in credit approval, where some customer groups may be rated lower not because of real risk, but because past data was unfair.

This risk not only affects customers directly but also exposes banks to legal risk and long-term reputational damage as AI in banking becomes more widespread.

To limit algorithmic bias, banks commonly put in place:

• Regular, independent model auditing
• Explainable AI to make the reasons behind decisions transparent
• Diverse review teams to reduce biased viewpoints
• Review and cleaning of input data before training models
• Continuous monitoring of model performance and behavior after real-world deployment

Controlling bias is not just a technical requirement. It is the foundation for fairness, transparency and sustainability in applying AI in banking.

≫ 9 Applying AI in business optimize the process

Difficulty integrating with legacy systems

In artificial intelligence in banking, a major challenge is integrating with legacy systems. Many banks still run technology infrastructure from years ago that doesn't handle real-time data well or connect flexibly to modern AI systems. This makes deployment complex, costly and prone to operating errors.

How to solve it:

• Adopt a “two-speed” strategy: keep the legacy system for core operations while deploying AI on a new system layer
• Build a middleware layer to connect data between legacy systems and AI applications
• Prioritize integration through APIs instead of replacing the whole system at once
• Deploy in phases along a roadmap to reduce the risk of operational disruption
• Standardize data before feeding it into AI systems to ensure compatibility

This approach helps banks modernize their infrastructure step by step while keeping operations stable, especially as AI in banking keeps expanding.

Legal compliance and accountability 

In AI in banking, a major challenge lies in legal compliance and accountability. The banking sector is under close supervision, so when AI takes part in decisions such as granting credit or processing transactions, banks must ensure every result can be clearly explained and meets regulations. However, not every AI model can “explain” itself in a way that people and regulators can verify, which makes compliance more complicated.

How to solve it:

• Build a dedicated AI governance team to control the entire model lifecycle
• Regularly track legal requirements from the State Bank of Vietnam and international standards
• Validate, document and keep full records of every AI model before it goes into operation
• Prioritize explainable AI models to ensure transparency in financial decisions

Future trends of AI in banking

AI in banking is only at the start of a major transformation. In the coming years, this technology won't stop at supporting operations or customer care. It will become the central foundation for how banks design products, make decisions and personalize the financial experience for each user.

Future trends of AI in banking:

• Generative AI in financial services: generating personalized reports, investment advice and financial documents in just seconds
• Hyper-personalized banking: each customer has a “dynamic financial profile” with interest rates, limits and products adjusted to real behavior
• Open Banking combined with AI: analyzing multi-source data to offer a fuller financial ecosystem, such as automatic saving, investing and spending management
• AI Agent in banking operations: it doesn't just suggest but can execute transactions itself, process loans and coordinate end-to-end operations
• In-depth financial assistants based on LLMsfor customers and bank staff, with analysis and advice close to a true financial expert

AI in banking is no longer a trend of the future. It is already happening, with clear figures showing a growing gap between pioneering banks and those following behind. From credit scoring, fraud detection and customer care to operational optimization, AI is becoming a clear source of competitive advantage in finance.

That said, deploying AI effectively is not just a technology problem. What matters is data strategy, system architecture, risk management capability and the right team. Banks that build this foundation early will have a sustainable edge in the long run.

If your business is looking for a partner to build a solid digital foundation, from an SEO-ready website to an AI-integrated digital marketing strategy, you can contact MONA Media's team of experts via HOTLINE 1900 636 648 for advice on a solution that fits your business goals.