Over the past few years, Generative Artificial Intelligence, or GenAI, has started to play a major role in many industries and has catalysed a wave of dramatic increase in productivity. The finance industry is no exception. Banks have moved on from being
just financial organisations and transformed into technology companies. 

For example, Capital One and JPMorgan Chase are using GenAI to strengthen their fraud and suspicious activity detection systems, Morgan Stanley implemented an AI tool that helps its financial advisors find data, and Goldman Sachs uses GenAI to develop internal
hugbúnaður.

Let’s take a look at five most promising implementations of GenAI in banking and finance. 

1. Virtual expert

This is one of the most popular GenAI use cases in banking these days. A virtual expert tool is an AI-powered financial advisor that can be client-facing and internal. The idea behind it is pretty straightforward: a user can ask a question and receive a
generated answer that’s based on long unstructured documents or large data arrays.

As a client-facing chatbot solution it can provide high-quality customer service by returning quick and accurate answers to customers’ questions and assisting with financial transactions. In many routine cases it works much faster and more efficiently than
a human.  

As an internal service, a virtual expert can provide customised answers based on the bank’s proprietary information and assets. Similar tools can be developed to automatically review transaction data, including the nature, volume, frequency, and counterparties
involved. This can help detect potential red flags, monitor market news and asset prices in real-time, and more. All this can be extremely helpful for making informed risk evaluations.

2. Evaluating risks

Evaluating risks is one of the most promising implementations of GenAI in finance, as it takes the task to another quality level. It can analyse large data sets and detect patterns that can go unnoticed.

Credit risk. GenAI can help automate decision-making on customer applications for credit products. Previously, a loan application would take two to three weeks to process, and the process required attention of many different specialists. When these applications
are reviewed by AI, it takes just a few minutes.Fast, remote and paperless format helps make the credit process faster and therefore more attractive to clients. 

Upon making a decision, GenAI can even generate the credit memo and develop a draft of the  contract. Generative AI tools can also be used to aggregate credit risk reports based on the data. 

Cybersecurity risk. GenAI can analyse cybersecurity vulnerabilities to generate code for detection rules and foster secure code development. It can be useful in simulating attack scenarios for preventive, testing and educational purposes.

GenAI is good at gathering and assessing the security data. Based on that, it can make risk detection more efficient by detecting security events and behavior anomalies and implementing security insights and trends based on that information.

Operational risk. Another area in which GenAI can play an important role. Banks can use it for operational automation of controls, monitoring, and incident detection. It can also automatically draft risk and control self-assessments or evaluate existing
ones for quality.

Climate risk. GenAI tools can automate data collection, perform risk assessments and generate early-warning signals based on trigger events or help visualise the potential climate risks. An artificial intelligence solution can automatically generate reports
on environmental, social, and governance risks and provide a solid base for annual sustainability reports.

3. Spá 

Stock markets are known for their volatility and constant change. Therefore, the main tool banks use to assess the potential gains and risks is forecasting. AI can perform trend studies to assess how overvalued or undervalued a stock is now.

Most of the data and processes in the financial industry is regularly repeated through various combinations. That is why AI can perform high-quality pattern analysis using its  well-developed features of calculating statistics and probabilities. AI can give
faster, more accurate and more efficient predictions of the trends that are most likely to appear.

4. Preventing financial crime

GenAI can analyse transactional data to identify suspicious or unusual patterns. Fraudsters follow similar patterns in 97% of cases, which is why this is an efficient measure against financial fraud. 

Based on that the tool can generate suspicious activity reports based on customer and transaction information. It can also automate the creation and update of customers’ risk ratings based on changes in know-your-customer attributes. By generating and improving
code to detect suspicious activity and analyze transactions, the tech can improve transaction monitoring.

5. Automation of processes 

More than 80% of finance and insurance business operations consist of routine action protocols. AI solutions can enhance information flow, decision-making, and coordination efforts. AI can automate numerous time-consuming processes, including loan application
processing, customer account management, and insurance claim analysis.

Additionally, GenAI can optimize processes that are not directly related to banking, such as the migration of legacy programming languages. This enables the adoption of the latest trends and technologies with greater flexibility. 

It can also automate model performance monitoring and generate alerts if metrics fall outside tolerance levels. Companies are also using AI to draft model documentation and validation reports.

Niðurstaða

The banking sector has traditionally been viewed as very conservative and reluctant to risk trying out new technologies in their processes. However, this reputation is rapidly changing due to the growing popularity of artificial intelligence. All the applications
of genAI mentioned above help financial organizations increase efficiency and reduce costs quickly. 

The growth of any company goes hand in hand with the ability to adapt to conditions and leverage advanced capabilities, including technologies that digitise and automate processes, which is why it is crucial to invest in implementation of artificial intelligence
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• Heimild: https://www.finextra.com/blogposting/26022/top-5-use-cases-for-genai-implementation-in-banks-or-fintech-companies?utm_medium=rssfinextra&utm_source=finextrablogs