Over the past few years, Generative Artificial Intelligence (GenAI) has begun to play a significant role in many industries and has fueled rapid increases in productivity. The financial industry is no exception. Banks have transformed from simple financial organizations into technology companies.
For example, Capital One and JPMorgan Chase are using GenAI to strengthen their fraud and suspicious activity detection systems, Morgan Stanley has implemented AI tools to help financial advisors find data, and Goldman Sachs is using GenAI to We are developing our internal software using:
Let’s take a look at five of the most promising GenAI implementations in banking and finance.
1. Virtual Expert
This is one of the most popular GenAI use cases in banking these days. Virtual Expert Tools are AI-powered financial advisors that can be used face-to-face with clients and internally. The idea behind it is very simple. When users ask questions, they can receive answers generated based on long unstructured documents or large arrays of data.
With a customer-facing chatbot solution, you can provide high-quality customer service, including quick and accurate answers to customer questions and financial transaction support. In many everyday cases, they operate much faster and more efficiently than humans.
The internal service allows virtual experts to provide personalized answers based on the bank’s unique information and assets. Similar tools could be developed to automatically review transaction data, including nature, size, frequency, and counterparties involved. This allows you to detect potential red flags, monitor market news and asset prices in real time, and more. All of this can be very helpful in carrying out an informed risk assessment.
2. Risk assessment
Risk assessment is one of the most promising implementations of GenAI in finance because it takes the task to another quality level. You can analyze large data sets and detect invisible patterns.
Credit Risk. GenAI can help automate decisions about customers’ applications for credit products. Previously, it took two to three weeks to process a loan application, and the process required the attention of a variety of specialists. It only takes a few minutes for AI to review these applications. Fast, remote and paperless formats make the credit processing process faster and more attractive to customers.
Once a decision has been made, GenAI can also generate a credit memo and develop a draft contract. You can also use generative AI tools to compile credit risk reports based on your data.
Cybersecurity risks. GenAI can analyze cybersecurity vulnerabilities, generate code for detection rules, and expedite secure code development. It can be useful for simulating attack scenarios for prevention, testing, and training purposes.
GenAI is adept at collecting and evaluating security data. Based on this, risk detection can be performed more efficiently by detecting security events and abnormal behavior and implementing security insights and trends based on that information.
Operational Risk. This is another area where GenAI can play an important role. Banks can use it to automate operations for control, monitoring and incident detection. You can also automatically draft risks and control your own assessments or assess the quality of existing assessments.
Climate risk. GenAI tools can help automate data collection, perform risk assessments, generate early warning signals based on trigger events, or visualize potential climate risks. Artificial intelligence solutions can automatically generate reports on environmental, social and governance risks and provide a solid foundation for annual sustainability reports.
3. Prediction
The stock market is famous for its volatility and constant change. Therefore, forecasting is the main tool banks use to assess potential profits and risks. AI can conduct trend studies to assess how overvalued or undervalued a stock is currently.
Most data and processes in the financial industry are regularly repeated in various combinations. That’s why AI can leverage well-developed statistical and probability calculation capabilities to perform high-quality pattern analysis. AI can provide faster, more accurate, and more efficient predictions about trends that are most likely to emerge.
4. Financial crime prevention
GenAI can analyze transaction data to identify suspicious or unusual patterns. Fraudsters follow similar patterns in 97% of cases, making this an effective measure against financial fraud.
Based on this, the tool can generate suspicious activity reports based on customer and transaction information. You can also automate the creation and updating of customer risk ratings based on changes in customer know-how attributes. Technology can improve transaction monitoring by creating and improving code that detects suspicious activity and analyzes transactions.
5. Process automation
More than 80% of financial and insurance business operations consist of routine action protocols. AI solutions can improve information flow, decision-making, and coordination efforts. AI can automate many time-consuming processes, including processing loan applications, managing customer accounts, and analyzing insurance claims.
GenAI can also optimize processes not directly related to banking, such as migrating legacy programming languages. This allows you to adopt the latest trends and technologies with greater flexibility.
You can also automate model performance monitoring and generate alerts when metrics fall outside acceptable levels. Companies are also using AI to draft model documentation and validation reports.
conclusion
The banking sector has traditionally been viewed as very conservative and reluctant to take the risks of trying new technologies in their processes. However, this reputation is rapidly changing due to the popularization of artificial intelligence. All of genAI’s applications mentioned above help financial institutions increase efficiency and quickly reduce costs.
The growth of any company is closely tied to its ability to adapt to circumstances and leverage advanced capabilities, including technologies to digitize and automate processes, so it is important to invest in implementing artificial intelligence where possible.