Applications of artificial intelligence in banking

Since the advent of artificial intelligence, the transformative effect of this technology has been profound and has changed the way of working and providing services to customers in various fields. In previous articles, we talked about the impact of artificial intelligence in the medical industry and learned about the interesting changes in this industry. In this article from the artificial intelligence company Avir, we are going to learn about the applications of artificial intelligence in banking. The introduction of artificial intelligence in banking applications and services has made the sector more customer-centric and technologically relevant.

AI-based systems are now helping banks reduce costs by increasing productivity and making decisions based on information incomprehensible to humans. Also, smart algorithms can identify fake information within seconds.

The report of the Business Insider website indicates that nearly 80% of banks are aware of the potential benefits of artificial intelligence in banking. Another report published by McKinsey, the most respected global management consulting firm, shows that the potential of artificial intelligence in banking and finance will increase to $1 trillion in the next few years.

These numbers show that the banking and financial sector is rapidly moving towards artificial intelligence to improve efficiency, service and productivity and reduce costs.

In this blog, we will explore the key applications of AI in banking and finance, and how this technology is redefining the customer experience and its exceptional benefits.

Applications of artificial intelligence in the banking and financial industry

Artificial intelligence has become an integral part of our world and banks are already starting to integrate this technology into their products and services. Here are some of the main applications of artificial intelligence in the banking industry:

1. Cyber ​​security and fraud detection

During the day, hundreds of thousands of digital transactions are made by people; Users pay bills, withdraw money, deposit checks and more through apps or online accounts. Therefore, there is a growing need for the banking sector to increase its efforts to detect fraud and forgery.

This is where the issue of the importance of artificial intelligence in banking is raised. Artificial intelligence and machine learning help banks detect fraudulent activities, track weaknesses in their systems, minimize risks, and improve the overall security of the online financial system.

One example of a bank that uses artificial intelligence to detect fraud is Danske Bank, Denmark’s largest bank, which implements a fraud detection algorithm in its business. The deep learning tool at this Danish bank has increased the detection of bank fraud by 50% and reduced false positives by 60%. An AI-based fraud detection system also automates many critical decisions while directing some cases to human analysts for further inspection.

Artificial intelligence can also help banks manage cyber threats. In 2019, the financial sector accounted for 29% of all cyber attacks and was the main target of cybercriminals. With AI’s continuous monitoring capabilities in financial services, banks can respond to potential cyber attacks before they impact employees, customers or internal systems.

2. Chatbots

Chatbots are one of the best examples of practical application of artificial intelligence in banking. Unlike humans who have fixed working hours, these robots work 24 hours a day, 7 days a week.

Additionally, chatbots learn about specific usage patterns. This learning helps them understand the needs of a user effectively.

By integrating chatbots into banking applications, banks can ensure that they are available to their customers around the clock. Additionally, by understanding customer behavior, chatbots can provide personalized customer support, reduce the workload of emails and other channels, and recommend appropriate financial products and services.

One of the best examples of AI chatbots for banking applications is Erica, Bank of America’s virtual assistant. This AI chatbot efficiently handles credit card debt reduction and card security updates; Erica has handled more than 50 million customer requests in 2019.

3. Loan and credit decisions

Banks have recently started incorporating AI-based systems to make more informed, safer and more profitable lending and credit decisions. Currently, many banks are still limited to using credit history, credit scores and customer references to determine the creditworthiness of an individual or company.

However, it cannot be denied that these credit reporting systems are often riddled with errors, missing real transaction dates and misclassifying creditors.

The artificial intelligence-based loan and credit system can examine the behavior and patterns of customers with limited credit history to determine their creditworthiness. Also, the system sends alerts to banks about certain behaviors that may increase the likelihood of default. In short, such technologies play a key role in changing the future of consumer lending.

4. Following market trends

The application of artificial intelligence and machine learning in financial services helps banks to process large amounts of data and predict the latest market trends. Advanced machine learning techniques help assess market sentiment and suggest investment options.

AI solutions for banking also suggest the best time to invest in stocks and warn if there is a potential risk. Due to its high data processing capacity, this emerging technology makes it easier for banks and their customers to speed up decision-making and trade.

5. Information collection and processing

Banking and financial institutions record millions of transactions daily. Since the amount of information generated is huge, it becomes overwhelming for employees to collect and record it. It becomes impossible to structure and record such a huge amount of data without any errors.

Innovative AI solutions in banking help in efficient data collection and analysis in such scenarios. This, in turn, improves the overall user experience. This information can also be used to detect fraud or make credit decisions.

6. Customer experience

Customers are constantly looking for better experiences and more convenience in banking. For example, the expansion of ATMs was successful because customers could use the essential services of depositing and withdrawing money even during non-working hours of banks.

This level of convenience only inspires more innovation. Customers can now open bank accounts from their homes using their smartphones.

The integration of artificial intelligence into banking and financial services further enhances the consumer experience and increases the level of convenience for users. Artificial intelligence technology reduces the time required to register “Know Your Customer” (KYC) information and eliminates errors. In addition, new products and financial offers are released on time.

Eligibility for things like applying for a loan or personal credit is automated using artificial intelligence, meaning customers can eliminate the hassle of going through the entire process manually. In addition, the software based on artificial intelligence reduces the approval time of facilities such as loan payments.

Artificial intelligence in banking customer service also helps in accurately collecting customer information to set up accounts without any errors and ensures a good customer experience.

7. Risk management

External factors such as currency fluctuations, natural disasters or political unrest seriously affect the banking and financial industry. In such volatile times, it is important to make prudent business decisions. Generative AI in banking provides analytics that provide a clear picture of what’s to come and help you be prepared and make timely decisions.

AI for banking also helps find high-risk applications by assessing the likelihood of a customer failing to repay a loan. It predicts future behavior by analyzing past behavior patterns and smartphone data.

8. Compliance with regulations

Banking is one of the most regulated sectors of the economy worldwide. Governments use their regulatory powers to ensure that bank customers do not use banks to commit financial crimes and that banks have an acceptable risk profile to prevent large-scale defaults.

Banks usually have an internal compliance team to deal with these issues, but these processes take much more time and require a lot of investment when done manually. Compliance regulations are also subject to frequent change and banks must continuously update their processes and workflows to comply with these regulations.

Machine learning and artificial intelligence in banking use deep learning and natural language processing to read new compliance requirements for financial institutions and improve their decision-making processes. Even if artificial intelligence in banking cannot replace adaptive analysts, it can make their operations faster and more efficient.

9. Predictive analytics

One of the most common uses of artificial intelligence in banking includes semantic applications and general purpose natural language and predictive analytics broadly. Artificial intelligence can identify certain patterns and correlations in data; Traditional technology previously could not detect these patterns.

These patterns can reveal sales opportunities, cross-selling opportunities, or even operational data metrics that have a direct impact on revenue.

10. Automation of banking processes

Robotic process automation (RPA) algorithms increase operational efficiency and accuracy and reduce costs by automating repetitive and time-consuming tasks. The algorithm also allows users to focus on more complex processes that require human involvement.

Today, banking institutions are successfully using RPA to increase transaction speed and increase efficiency. For example, JPMorgan Chase’s Coin technology scans documents and extracts data from them much faster than humans.

Real examples of artificial intelligence in banking

As mentioned above, a few large banks have started using AI technologies to improve service quality, detect fraud and cyber security threats, and enhance customer experience.

Here are two real-life examples of banking institutions using AI to improve their services.

1. JPMorgan Chase

JPMorgan Chase researchers have developed an early warning system using artificial intelligence and deep learning techniques to detect malware, Trojans and phishing campaigns. Researchers say it takes about 101 days for a Trojan to compromise corporate networks. An early warning system provides multiple warnings and alerts before an attack occurs. It also sends alerts to the bank’s cybersecurity team as hackers prepare to send malicious emails to employees to infect the network.

2. Capital One

Eno, the intelligent virtual assistant introduced by Capital One, is the best example of artificial intelligence in personal banking. In addition to Eno, Capital One also uses virtual card numbers to prevent credit card fraud. Meanwhile, the company is working on computational creativity, which trains computers to be creative.

The challenges of using machine learning and artificial intelligence in banking

Widespread use of advanced technology such as artificial intelligence is not without challenges. There are various challenges for banks using AI technologies, from lack of reliable and quality data to security issues. Let’s take a look at the challenges that exist for using artificial intelligence in banking:

1. Data security

The volume of data collected in the banking industry is very large and requires adequate security measures to prevent any breach or violation. Therefore, it is important to find the right technology partner that understands AI and banking well and offers a variety of security options to ensure that your customer data is properly managed.

2. Lack of quality data

Before deploying a full-scale AI-based banking solution, banks need structured, high-quality data for training and validation. Quality data is needed to ensure that the algorithm is applied in real situations.

Also, if the data is not in a machine-readable format, it may lead to unexpected behavior of the AI ​​model. Therefore, banks moving towards AI adoption must change their data policies to mitigate all privacy and compliance risks.

3. Lack of explainability

Artificial intelligence-based systems are widely applicable in decision-making processes because they eliminate errors and save time. However, these systems may follow biases learned from previous instances of poor human judgment. Minor inconsistencies in AI systems do not escalate and cause large-scale problems, jeopardizing a bank’s reputation and performance.

To avoid these problems, banks should provide an appropriate level of explanation for all decisions and recommendations made by AI models. Banks must understand, validate and explain how the model makes decisions.

How does artificial intelligence company Avir help banking?

Artificial intelligence and banking go hand in hand due to the many advantages of this technology. According to McKinsey’s Global AI Survey, 60% of financial services firms have implemented at least one AI capability to streamline business processes.

This shows that the future of artificial intelligence in banking is bright and promising. Artificial intelligence is set to revolutionize the banking landscape with the potential to simplify processes, reduce errors and enhance the customer experience. Therefore, all banking institutions should invest in AI solutions to provide new experiences and excellent customer service.

Avir is a leading AI development company in Iran that provides advanced software development services and works with banks and financial institutions to develop customized AI and machine learning based models to improve revenue, reduce costs and mitigate risks. Help in different banking departments.

Our experts can help you use artificial intelligence to make important changes because of their knowledge of artificial intelligence and their knowledge of the specific problems facing the banking industry. At Avir AI, we help you build AI-powered solutions that improve risk management, automate banking processes, and improve customer experiences.

Contact our experts now to build and implement a long-term artificial intelligence in your banking strategy.

Frequently Asked Questions

How does artificial intelligence help banking?

Artificial intelligence in banking automates tasks, enhances customer service through chatbots, detects fraud, optimizes investments, and predicts market trends. This will increase banks’ productivity, reduce costs and provide personalized services.

How does artificial intelligence help bank risk management?

Data analysis: AI systems analyze large amounts of data to identify trends and anomalies that can be signs of danger.

Real-time monitoring: AI in digital banking tracks account activity and transaction data in real-time to quickly identify and address risks.

Fraud detection: AI algorithms detect fraudulent actions by examining transaction data and customer behavior patterns.

Compliance and regulatory requirements: AI helps banks ensure compliance by automatically tracking transactions and generating reports.

Predictive analytics: AI builds risk models and performs predictive analytics to calculate the likelihood of defaults and market volatility.