With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses.

Fintech is one of the industries that is skyrocketing due to the growing number of internet users. To increase the speed, security, and scalability of the financial industry, several technologies function in the background. One of the technologies that have significantly changed the financial industry in 2023 and beyond is artificial intelligence (AI). Financial organizations are focused on leveraging AI, which would be introduced in areas such as mobile banking, customer experience, cyber security, social banking, payments, branch automation, and operational efficiency. Due to its remarkable advantages, such as more effective business operations, superior financial analysis, and more consumer engagement, artificial intelligence (AI) and machine learning (ML) are increasingly being used in the finance industry. Artificial intelligence is not going out of trend anytime soon. But, what are the best use cases of AI in the fintech industry, how does it change the finance industry, and how can you profit from this new technology? This blog will address the technical aspects of bringing AI/ML to the finance industry and outline every aspect of AI in the finance industry. But before proceeding further, please go through the interesting stats.

Jamie Dimon, chief executive officer of JPMorgan Chase & Co., speaks virtually during a webcast ... [ ] event on a mobile phone. If you want to know which technologies will be hot in the banking industry in 2023, heed the advice of Deep Throat. In the movie All The President's Men, Woodward and Bernstein's informant--whom they refer to as Deep Throat--tells them: "Follow the money." A new study from Cornerstone Advisors, What's Going On in Banking 2023, follows the money and reveals where banks and credit unions will place their technology bets in this uncertain year. Uncertain not just because of the economic conditions, but because of the vagaries of the organizational and technological environments in which banks operate.

With the growing competition in banking industry, banks are required to follow customer retention strategies while they are trying to increase their market share by acquiring new customers. This study compares the performance of six supervised classification techniques to suggest an efficient model to predict customer churn in banking industry, given 10 demographic and personal attributes from 10000 customers of European banks. The effect of feature selection, class imbalance, and outliers will be discussed for ANN and random forest as the two competing models. As shown, unlike random forest, ANN does not reveal any serious concern regarding overfitting and is also robust to noise. Therefore, ANN structure with five nodes in a single hidden layer is recognized as the best performing classifier.

The topic of how artificial intelligence can transform banking continues to get a massive amount of attention. With data in abundance, and the need to improve efficiency and create better customer experiences, every new evolution of AI creates opportunities, while also raising questions around privacy, biases, the impact on the human workforce, and changes in existing business models. One of the most talked about advances in the deployment of AI occurred on November 30, when OpenAI released ChatGPT, deemed "the most advanced, user-friendly chatbot to enter the public domain." ChatGPT can create high-level content, respond to customer inquiries, assist with research, and provide perspectives on current trends. OpenAI, a nonprofit company, was founded in 2015 by Sam Altman, Elon Musk and other Silicon Valley investors.

The topic of how artificial intelligence can transform banking continues to get a massive amount of attention. With data in abundance, and the need to improve efficiency and create better customer experiences, every new evolution of AI creates opportunities, while also raising questions around privacy, biases, the impact on the human workforce, and changes in existing business models. One of the most talked about advances in the deployment of AI occurred on November 30, when OpenAI released ChatGPT, deemed "the most advanced, user-friendly chatbot to enter the public domain." ChatGPT can create high-level content, respond to customer inquiries, assist with research, and provide perspectives on current trends. OpenAI, a nonprofit company, was founded in 2015 by Sam Altman, Elon Musk and other Silicon Valley investors.

AI can accomplish more with less, from manufacturing to telecommunications to food service. At the same time, it's a powerful tool for companies to meet or exceed consumer expectations. You may not realize that domestic and offshore banks also employ AI in several ways. Two goals drive the desire to make the most of this resource: lower costs while enhancing profits and providing a level of convenience that motivates customer loyalty. Here are some of the ways that AI is changing the way that many banks do business.

AI can accomplish more with less, from manufacturing to telecommunications to food service. At the same time, it's a powerful tool for companies to meet or exceed consumer expectations. You may not realize that domestic and offshore banks also employ AI in several ways. Two goals drive the desire to make the most of this resource: lower costs while enhancing profits and providing a level of convenience that motivates customer loyalty. Here are some of the ways that AI is changing the way that many banks do business.

Artificial intelligence in the banking sector makes banks efficient, trustworthy, helpful, and more understanding. It is strengthening the competitive edge of modern banks in this digital era. The growing impact of AI in banking sector minimizes operational costs improves customer support and process automation. Besides, AI in banking also helps users to select loan amounts at an attractive interest rate. The AI technology in the banking sector allows banks to update processes automatically and work under existing regulatory compliance. In this blog, we briefly explained a few core use cases of Artificial Intelligence in the banking sector. Let's have a look into What AI can do for the banking sector?

Artificial Intelligence is expected to permanently change the banking industry in profound ways during the coming months and years. Companies want to seek a competitive edge by implementing more technology to achieve improvements in speed, cost, accuracy and efficiency. The key for global corporate enterprise is to benefit from the collective intelligence presented by RPA and cognitive technologies along with human workers. Only by having technology combine with human talent can global corporate enterprise achieve scalable intelligent automation. And only with scalable intelligent automation enterprise resiliency be realized.