Remember Steve Jobs' iPhone announcement in 2007? It was the moment when the phone tool was upgraded as a "friend" with whom you go to bed. We now enjoy YouTube, Netflix, TikTok and Instagram everywhere through our smartphones. Even though wallets and important meeting materials are often left at home, smartphones never fall out of our grasp.

The corporate ecosystem has also changed remarkably. Samsung and LG, which divided the domestic mobile phone market before the release of smartphones, faced different aspects depending on how they handled them. Sensing the fast-changing industrial atmosphere, Samsung has become a global company through its Galaxy series, while LG, which started development just a year later than Samsung, ended its service in 2021 due to persistent business deficits.

In November 2022, companies faced new changes that would once again shake their fortunes. ChatGPT made by OpenAI came to our attention.

ChatGPT is an innovative service in that it is based on generative artificial intelligence (AI) technology. While general AI technology specializes in predicting results by analyzing or classifying data, generative artificial intelligence technology differs from general AI technology in that it can generate or transform complex data such as natural language or images. In other words, generative AI can write, draw and make music by itself.

As we have seen in previous smartphone cases, innovation through new technologies is essential, not a matter of choice for companies. Then, how can commercial banks use generative AI technology for their services?

First, based on generative AI, a new chatbot that acts as an "emotional secretary" can be created. Most of the existing bank chatbots are only available for banking and asset management services. This is due to the lack of skills and data necessary for emotional recognition, analysis, emotional responses and expressions to move from the simple "intelligent secretary" stage to the complex "emotional secretary" stage.

It is already widely known that the bond with the other party in relationship marketing has led to more services and the purchasing of products. Emotional secretary-type chatbots developed through generative AI can provide new services by forming a close bond through emotional exchanges with customers and preemptively responding to various services.

gettyimagesbank

Consider an AI chatbot that responds quickly and accurately to customers' inquiries 24 hours a day, seven days a week, and identifies their gender, age, region, disposition, etc. for personalized conversations. Isn't that amazing?

Fraud detection artificial intelligence models for account holders can also be created using generative AI. Generative AI learns the transaction patterns of account holders and informs them in real time when abnormal transactions occur.

For example, if an account holder trades in a different region or country than usual, trades a larger amount than usual, or trades on a suspicious store or website, he or she may send a push notification to the account holder or ask whether the transaction is approved.

Since the COVID-19 pandemic, the trend of declining cash use has accelerated, and the use of digital payment methods has become commonplace. Financial fraud, especially financial fraud targeting the elderly, has increased rapidly. Fraud detection artificial intelligence models can protect the elderly who are vulnerable to digital finance, while reducing the loss and risk of fraud internally in banks.

gettyimagesbank

Finally, a loan review model can be created using generative AI. Loans are the core business of the bank and play a role in meeting the financial needs of customers and generating profits for the bank. However, the screening and review process is complicated and takes a long time, and personal mistakes and fraud risks coexist.

Therefore, it is possible to increase efficiency and accuracy by creating a loan review model using generative AI. Generative AI can understand and generate natural language, so it can perform tasks such as analyzing, predicting and summarizing various data.

In addition, using generative AI to determine customer creditworthiness and loan availability allows more sophisticated and diverse credit ratings using non-financial data or digital data as well as existing financial data.

Let's apply a loan review model to PF loans that have recently been in question. Generative AI calculates future interest rate changes more reasonably and more accurately calculates construction cost changes due to changes in raw material costs. Non-profit PF loans will be excluded in advance and reviewers will be able to make decisions more reasonably.

Many people are concerned about the speed of AI's development. This is because of the fear that AI might take away our jobs and we cannot fully control AI. No one can completely refute such concerns. This is because it is a distant future that has yet to come.

But rather than worrying about the distant future, let's imagine our near future that will be different through AI. It is a future in which my father and brother, who work hard at the construction site, will not be able to receive money due to the collapse of the company and inform their grandparents of mobile financial transactions with confidence on Chuseok. That probably is the near future that AI and banks will create.

Lee Jun-ho is a student at Kookmin University.