AI in banking and finance is among the most promising trends. With the capabilities of artificial intelligence and machine learning, financial institutions can streamline processes, as well as utilize AI solutions to refine a wide range of operations, from customer service and risk management to early fraud detection and compliance with regulatory requirements. Integration of AI is the best approach for more efficient banking practices, providing competitive advantages.  

Read this article to learn more about the impact of AI in banking and the future of the financial services sector. 

The Evolution Of AI In the Banking Industry

The financial industry is one of the most heavily regulated sectors, which makes innovation adoption in the banking sector slower. However, with the need for digital transformation and the development of AI technologies, as well as demand for more personalized services and effective methods to prevent fraud, we can observe growing use of AI in the banking industry. Today, artificial intelligence is applied not only in fintech but also traditional banks are increasingly implementing AI tools to solve various tasks.

Investment banking has previously used natural language processing (NLP) to parse vast amounts of data from internal or third-party sources, which facilitated informed decision-making around key investments and wealth management.

AI capabilities are excellently revealed in the banking sector, providing a range of valuable benefits, especially when we talk about AI in digital banking, as today's customers are focused on more personalized and accessible financial products and services. Additionally, with the emergence of generative AI in banking, customers can better manage their finances and enjoy enhanced user experience thanks to virtual assistants, thus solving many tasks independently without bank employee assistance.

At the same time, artificial intelligence enables automation of routine and repetitive tasks, which is among the most common use cases in banking today. Due to the combination of new technologies (AI and RPA), financial services companies can better parse data analytics, which is especially useful when it comes to validating customer data needed to meet KYC, AML, and CDD restrictions.

AI's potential is being revealed better each time. While previously the main tasks of AI technology in banking were reduced to simple automation and basic data analysis, current AI models excel in risk management, fraud detection, protection against cyber threats, and personalized customer service. Also, generative AI goes even further and allows creating accurate predictions based on massive amounts of data. This aspect of generative AI models is one of the most attractive for institutions, as it can significantly transform banking operations and strategies.

Thus, there is a consistent evolution of AI in banking and financial services. Starting from automation of simple tasks that were previously manual, artificial intelligence technology with machine learning techniques has continuously improved, opening new functions and capabilities that are actively being implemented in the banking sector nowadays. This allows organizations to reduce costs and improve efficiency, as well as enhance customer engagement and make financial services more convenient, faster, and safer for users.

How is AI Used in Banking?

AI in banking and payments is becoming an integral part of the industry, as more institutions transform their operations and implement new technologies. So how can AI be used in banking? Here are some of the major artificial intelligence applications in the industry:

Cybersecurity

As banking customers perform a huge number of actions daily (paying bills, deposits, withdrawals, etc.) through applications or websites, security issues and financial crime prevention are priorities in the sector.

The integration of artificial intelligence and machine learning algorithms facilitates early identification of fraudulent activities and allows effective tracking of loopholes. This makes it possible to minimize risks and enhance online banking security.

AI systems expand monitoring capabilities and ensure quick response to potential attacks. At the same time, AI model-based security solutions reduce employee workload by automating some decisions.

Virtual Assistants

Chatbots and assistants can serve customers 24/7, significantly improving user satisfaction. Additionally, generative AI capabilities allow bots to learn from each interaction with the user, thus providing each customer with a personalized virtual assistant that understands their needs and habits.

Round-the-clock availability and individual client behavior learning by chatbots is a step toward customer-oriented service and personalized financial products and recommendations, which are of importance for modern banking users.

Loan & Credit Decisioning

AI in online banking revolutionizes services by facilitating more efficient document processing, risk management and assessment, as well as smooth customer experiences. AI systems allow banks to make more informed, safe, and profitable decisions regarding loans.

While traditional banks largely rely on customer history and scores, artificial intelligence has limited credit history importance when determining creditworthiness of a client or company. Instead, AI models can thoroughly study habits and behavioral patterns of specific customers, thereby determining their reliability and predicting possible risks.

Trend Identification

Another way regarding how to use AI in banking is market trend forecasting. Since AI processes large volumes of data, this technology helps identify and correctly interpret market sentiments and suggest investment options.

Thanks to predictive capabilities and high data processing capacity, AI tools are particularly attractive for investment research, providing insights regarding possible risks, as well as better timing for selling or buying assets, making AI a popular tool for investment banks and their clients.

Data Collection & Analysis

Financial institutions' systems work with enormous volumes of transactions, customer data, and information. The processes of collecting, recording, and structuring all this data require significant effort and are prone to errors.

AI in banks allows solving these challenges through automation, as well as the technology expands capabilities in fraud identification and decision-making based on collected and analyzed data.

User Experience

Today's customers primarily choose convenience, pleasant user experience, and service accessibility at any time from any location.

The integration of artificial intelligence and machine learning in the banking sector allows institutions to significantly improve customer service by offering personalized experiences. From quick and easy KYC/AML procedures to loan/mortgage applications and receiving individual financial recommendations, AI-based banking applications provide customers with exceptional experience and a unified solution for most tasks.

Risk Management

The financial industry and capital markets largely depend on currency fluctuations, political events, natural disasters, and numerous social & economic factors. Strong volatility as a reaction to events always carries various risks and is accompanied by losses. As a result, financial institutions are increasingly investing in the implementation of generative AI technology, which offers deep analytics and can create realistic forecasts. This helps not only in making quick decisions to reduce risks in case of unforeseen global circumstances but also allows thorough study of bank clients based on their habits, thereby predicting their future behavior, such as a probability of a client failing to repay a loan.

Compliance

The banking sector is one of the most regulated. Companies providing financial services must constantly monitor rule changes, transform workflows to meet new requirements, and create reports. These tasks require special attention, effort, and time from institutions, as non-compliance with rules leads to huge fines and can severely damage company reputation, and in some cases lead to business failure.

AI and ML are effective solutions for compliance. These technologies allow quick access to information regarding rule changes, thus financial institutions can significantly improve their decision-making, respond to new requirements in time, and increase operational efficiency.

Automation

Robotic process automation (RPA) technology is another popular way to use AI. RPA implementation gives banks the ability to automate time-consuming and repetitive tasks, freeing resources for more priority tasks that require human intelligence.

Additionally, RPA's popularity lies in the fact that this technology can review documents and derive data much faster than humans, which increases company efficiency.

Predictive Analytics

Since AI can continuously learn and develop, as well as notice patterns and correlations in data, artificial intelligence is an especially useful tool for banks when it comes to forecasting and predictive analytics, providing valuable insights regarding potential risks, opportunities, and solutions.

How are Banks Using AI in 2024?

Many prominent banks are already actively using artificial intelligence and machine learning technologies to improve service quality, operational efficiency, detect fraud, and provide personalized customer experiences.

For example, JPMorgan Chase has developed an AI and deep learning-based early warning system. This system detects trojans, malware, and phishing campaigns, alerting about potential risks and even preparation for attacks in advance, allowing the security team and bank employees to be prepared for cyber attacks before they occur.

Meanwhile, Capital One uses a virtual assistant called Eno, which provides high-quality personalized customer service. The bank also uses virtual card number technology to prevent credit card fraud.

Another example of AI implementation in financial technology is the European Bank's AI-based solutions. The institution uses artificial intelligence to resolve customer queries in real-time. This generative AI chatbot is integrated into the bank's web and mobile applications and can perform various complex tasks, including handling real-time customer complaints and reporting stolen credit card cases. The chatbot also supports 7 languages and can assist customers worldwide. Moreover, the implementation of the virtual assistant has increased customer retention by 20%.

Lastly, many investment-focused banks are increasingly utilizing Alphasense, an AI-based search engine that uses natural language processing to discover market trends and analyze keyword searches.

Benefits of AI in Banking Sector

Summarizing how AI can be and is already being used by banks, we can identify 10 key benefits of this technology in the financial industry.

So, how does AI help in banking?

Effective Fraud Detection

Artificial intelligence analyzes transactions in real-time, identifying suspicious activities based on behavioral patterns. Such security systems can adapt more quickly to new fraud schemes, allowing banks to save money and prevent customer data breaches, thereby increasing trust.

Personalized Service

AI tools can analyze transaction history, customer behavior, and preferences to create personalized financial recommendations and offers. This approach allows banks to offer relevant products, increase satisfaction, and enhance cross-selling opportunities.

Automated Credit Scoring

Since machine learning algorithms analyze vast amounts of data about potential borrowers, including non-traditional information sources, banks can more accurately assess risks, speed up decision-making processes, and reduce loan default rates.

Chatbots and Virtual Assistants

AI assistants can handle the vast majority of typical customer inquiries and provide instant support 24/7. Virtual assistants understand natural language and learn from previous interactions, significantly reducing staff workload while improving service quality.

Workflow Optimization

AI automates many routine tasks, such as document processing, data reconciliation, and report generation. This reduces operational costs, minimizes human error, and frees up employees for more priority tasks requiring human intelligence.

Predictive Analytics

AI systems can forecast financial trends, future customer behavior, and market risks based on historical data, helping banks make informed strategic decisions and adapt to changes in advance.

Risk Management

AI-based solutions continuously monitor multiple risk factors, including market fluctuations, regulatory changes, and operational threats. These tools can identify potential issues in early stages and suggest preventive measures, significantly improving banking business resilience.

Investment Portfolio Optimization

Machine learning algorithms analyze market data and automatically adjust investment portfolios according to specified risk and return parameters. This enables more efficient asset management and ensures more profitable investment decisions.

Enhanced Security

AI provides multi-level protection for banking systems and applications through biometric authentication, behavioral analysis, and cyber attack detection. Algorithms can detect and block threats in real-time, significantly increasing the security level of banking operations.

Big Data Analysis

AI technology is particularly effective when it comes to processing and analyzing huge volumes of structured and unstructured data, extracting valuable insights. Thus, AI helps banks better understand customer needs, optimize their products, and find new opportunities for business growth.

 

Explore New Approaches & Streamline Operations With Customized AI Solutions In Banking. Bring Your Ideas To Life With The Jappware Team! 

 

Challenges in Adopting AI in Banking

Despite the many advantages of AI technology in banking, there are several challenges and aspects that financial institutions should pay attention to:

Data Security

Since banks and financial companies collect and process huge volumes of data, their security and protection is among the top priorities. It is crucial for institutions to guarantee robust measures to prevent data leaks and violations. This is why companies need to collaborate with AI developers to ensure their solution will be reliably protected and handle customer data appropriately.

Explainability

Another challenge to consider is the potential lack of explainability. The thing is that generative AI doesn't think independently; instead, the technology uses data from previous cases, some of which may be biased or contain too much human judgment. The problem here is that even minor inconsistencies are enough for AI models to quickly escalate these inconsistencies, thus leading to failed decision-making and recommendations. Therefore, it's essential for banks to understand, validate, and explain how models make decisions.

Lack of Quality Data

AI is best trained on structured and quality data during the learning process so that algorithms can be applied to real-life situations. Consequently, institutions should ensure their data has a machine-readable format to avoid unexpected behavior of AI models.

Is AI the Future of Banking?

Even though artificial intelligence plays an important role in transforming the banking sector, it's critical to understand that the technology cannot completely replace traditional approaches. Speaking about the future of AI in banking, it's worth noting that while AI can automate routine operations, improve risk analysis, and personalize services, human intelligence remains crucial. Customers still value personal interaction, especially when it comes to solving complex financial issues. Therefore, we can say that the future of banking will likely be hybrid:

  • AI will handle technical and analytical tasks, while banking specialists will focus on strategic consulting and complex financial decisions.

This symbiosis will allow banks to provide more efficient and personalized services while ensuring high-quality service for complex and priority operations and decisions.

The Importance of AI in Banking

The implementation of artificial intelligence in the banking sector is of tremendous importance for improving the efficiency and competitiveness of financial institutions. Today, AI allows banks to significantly speed up transaction processing, improve fraud detection, and optimize credit risk assessment. Also, due to machine learning, banks can better understand customer needs and offer personalized products.

AI technology is an excellent solution to reduce operational costs through automation of routine processes and improve customer service through the implementation of chatbots and virtual assistants. This leads to increased user satisfaction and strengthening of the bank's market position, making the value of AI for the financial industry difficult to overestimate.

How We Can Help With Your Artificial Intelligence for Banking Implementation

Jappware provides custom software development services for institutions and startups in the financial industry. Taking into account your unique needs, challenges, and goals, our team will find the best solution options and implementation methods to ensure a smooth transformation of your business.

By collaborating with our specialists, you gain access to an experienced team of professionals who will become like an additional department in your company and will go through the entire journey from planning and initial development to product release and continued support.

At Jappware, we aim for long-term cooperation and partnership, offering tailored banking software development solutions. Contact us today to discuss your ideas and bring your vision to life. You can count on our expertise!