FREMONT, CA: In today's digital age, the banking and finance industry is undergoing a substantial transition driven by technology advancements. Generative AI is one example of an innovation that is revolutionizing the sector. This form of artificial intelligence (AI) can transform traditional banking processes and improve consumer experiences like never before.
Generative AI, also known as large language models, has the ability to learn from large datasets and generate independent responses.
Unlike typical AI models, generative AI can evaluate past data, identify patterns, and make informed decisions on its own. This technology, along with Robotic Process Automation (RPA), can potentially enhance various aspects of banking operations, such as fraud detection, risk management, and customer service.
Generative AI use cases in banking services
Fraud detection: AI is essential in the banking industry, particularly in fraud prevention. Traditionally, many banks have huge fraud detection departments, which can be costly to operate and may not always be completely effective.
However, Generative AI may monitor transaction parameters such as location, device, and operating system, reporting any unexpected or aberrant activity that deviates from normal trends. This automation minimizes the need for manual transaction review, which is time-consuming and error-prone.
Credit analysis: Generative AI provides banking personnel with a powerful tool for evaluating trustworthiness by analyzing consumer credit scores and financial histories. Furthermore, it may evaluate the risk associated with loan applications by analyzing data from various sources, including credit reports, income statements, tax returns, and other financial information.
The Generative AI can also monitor borrower behavior, bank statements, and account activity to detect any changes in financial situations that could indicate a risk of default or delinquency. Furthermore, for retail and small-price loans, Generative AI allows for real-time loan decisions, expediting the process and decreasing the time and costs associated with previous approaches.
Data privacy: The use of synthetic data offers a possible answer to the issues posed by data privacy in the banking business. When customer data cannot be shared owing to privacy concerns or data protection rules, synthetic data can be a viable option for developing shareable datasets. Furthermore, synthetic customer data is extremely useful in training machine learning models to assist banks in establishing a customer's eligibility for credit or mortgage loans and calculating the appropriate loan amount.
