Business Intelligence In Banks – Artificial intelligence has become a major cause of disruption in almost every industry, including banking and finance. The introduction of AI in banking applications and services has made the industry more customer-centric and technology-focused.
AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information that cannot be understood by humans. In addition, intelligent algorithms can detect fraudulent information in seconds.
Business Intelligence In Banks
A report by Business Insider found that almost 80% of banks are aware of the benefits that AI can bring to their industry. Another report shows that by 2023, banks are expected to save $447 billion using AI software. These numbers show that the banking and finance industry is rapidly moving towards AI to improve efficiency, service, productivity and reduce costs.
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In this article, we will explore the main uses of AI in the finance/banking industry and how this technology is redefining the customer experience to its unique advantage.
Artificial intelligence has become an integral part of the world we live in and banks have begun to integrate this technology into their products and services.
Here are some important uses of AI in the banking industry where you can reap the most benefits of the technology. So let’s dive in!.
Ai In Banking: Can Banks Meet The Challenge?
Every day, a large number of digital transactions take place as users pay bills, withdraw money, deposit checks and more through apps or online accounts. As a result, the banking industry increasingly needs to strengthen its fraud detection and cyber security efforts.
This is where artificial intelligence in banking comes into play. AI can help banks improve online financial security, monitor vulnerabilities in their systems and reduce risk. AI combined with machine learning can easily identify fraudulent activities and alert customers and banks.
For example, Danske Bank, Denmark’s largest bank, has implemented a fraud detection system to replace its old fraud detection system. This deep learning tool increased bank fraud detection by 50% and reduced misinformation by 60%. The system also automates many critical decisions while referring other cases to human analysts for further review.
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AI can also help banks manage cyber threats. In 2019, the financial sector accounted for 29% of all cyber attacks, making it the most targeted sector. With continuous artificial intelligence monitoring in financial services, banks can respond to cyber attacks before they affect employees, customers or internal systems.
Undoubtedly, chatbot is one of the best examples of practical use of artificial intelligence in banking. Once deployed, they can work 24/7, unlike humans who have set work hours.
In addition, they continue to learn about the usage pattern of a particular customer. It helps them understand the user’s needs effectively.
Improving Digital Banking For Business Clients
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 and recommend appropriate financial products and services.
One of the best examples of AI chatbots in banking applications is Erica, Bank of America’s virtual assistant. This AI chatbot can handle tasks like reducing credit card debt and updating card security. Erica managed over 50 million customer requests in 2019.
Banks have started incorporating AI-based systems to make more informed, safer and more profitable lending decisions. Currently, many banks are still very restrictive in their use of credit history, credit scores, and customer references to determine creditworthiness of an individual or company.
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However, one cannot deny that these credit reporting systems are often riddled with errors, lack real-world transaction history, and misclassify creditors.
AI-based credit and lending systems can look at the behavior and patterns of customers with limited credit history to determine their creditworthiness. In addition, the system will send warnings to banks about specific behaviors that may increase the likelihood of default. In short, such technologies play an important role in changing the future of consumer lending.
Artificial intelligence in financial services helps banks process large amounts of data and predict the latest market, currency and stock trends. Advanced machine learning techniques help assess market sentiment and recommend investment options.
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AI for banks also suggests the best time to invest in stocks and warns when there are potential risks. Due to its high data processing capabilities, this emerging technology also helps speed up the decision-making process and make transactions easier for banks and their customers.
Banks and financial institutions record millions of transactions every day. Since the amount of information generated is very large, collecting and registering information becomes a huge task for employees. Creating and recording large amounts of data without any errors becomes impossible.
In such cases, innovative AI-based solutions can help in better data collection and analysis. This, in turn, improves the overall user experience. The information can also be used to detect fraud or make credit decisions.
Artificial Intelligence To Power The Banks Of The Future
Customers are always looking for the best and easiest experience. For example, ATMs are successful because customers can use essential deposit and withdrawal services even when the bank is closed.
This level of simplicity only inspires more innovation. Customers can now open bank accounts from the comfort of their homes using their smartphones.
The integration of artificial intelligence into banking and financial services will further improve the user experience and increase the level of convenience for users. AI technology reduces the time of recording Know Your Customer (KYC) information and eliminates errors. In addition, new products and financial offers may be released over time.
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Eligibility processes like applying for a personal loan or loan are automated by AI, meaning customers can eliminate the hassle of going through the entire process themselves. In addition, AI-based software can reduce approval times for facilities such as loan payments.
AI banking services also help capture customer information accurately to set up accounts without errors, ensuring a smooth experience for customers.
Global external factors such as currency devaluations, natural disasters or political instability have a significant impact on the banking and financial sector. In these volatile times, it’s important to make smarter business decisions. AI-powered analytics can provide a clear picture of what’s to come and help you prepare and make timely decisions.
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AI also helps find risky applications by assessing the likelihood of a customer defaulting on a loan. It predicts this future behavior by analyzing past behavior patterns and smartphone data.
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 avoid serious mistakes.
In many cases, banks maintain an internal compliance team to handle these issues, but these processes take time and require significant investment when done manually. Compliance regulations can also change from time to time and banks need to constantly update their procedures and work processes in accordance with these regulations.
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AI uses deep learning and NLP to study new compliance requirements for financial institutions and improve their decision-making processes. Although AI banks cannot replace the compliance analyst, it can make their operations faster and more efficient.
One of the most common use cases for AI includes the use of general-purpose and native logic languages as well as widely adopted predictive analytics. AI can identify specific patterns and correlations in data, which traditional technology could not detect before.
These trends can point to untapped sales opportunities, cross-selling opportunities, or even metrics related to performance data, resulting in a direct revenue impact.
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Robotic process automation (RPA) algorithms help increase operational efficiency and accuracy, and reduce costs by automating time-consuming, repetitive tasks. This also allows users to focus on more complex processes that require human involvement.
To date, banking institutions have successfully used RPA to speed up transactions and increase efficiency. For example, JPMorgan Chase’s Coin technology looks at documents and pulls data from them faster than humans can.
Banks are rushing to be AI-first, and for good reason. Over the years, the banking industry has struggled to transform itself from a people-centric business to a customer-centric one. These changes have forced banks to take a more holistic approach to meet the needs and expectations of customers.
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Now, considering the customers, banks should start thinking about how to serve them better. Customers now expect to have the bank there for them whenever they need it – that is, available 24 hours a day, 7 days a week – and they expect their bank to do so on a large scale. One way banks can do this is with AI.
To meet these customer expectations, banks must first overcome some of their internal challenges; legacy systems, data repositories, asset quality and limited budgets. Since these are just some of the issues that prevent banks from acting quickly to keep up with customer needs, it’s no wonder that many banks have turned to AI as a catalyst for change.
Now we have seen
Application Of Business Intelligence In The Banking Industry
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