Generative AI in the financial industry | Key use cases and limitations

October 7, 2023

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Generative AI is playing an increasingly important role in the financial industry.

Generative AI in the financial industry | Key use cases and limitations

In this blog, we take a closer look at how this tool is applied and what the challenges are with it. Through this article, you can gain an objective understanding of the impact of generative AI in the financial industry.

①Main usage examples
1. chatbot
2. Investment plan proposal
3. compliance management
4. Financial analysis and forecasting
5. personalized financial guidance
6. AI-based fraud detection
7. Loan score management
8. Automation of office operations
9. Preparation of financial reports
10. Legacy software maintenance
②Limitations of generative AI
1. Data security
2. Accuracy of data analysis
3. cost
4. Limited interaction with people
5. technology dependent
in conclusion

①Main usage examples

chatbot

Many banks are already using chatbots to process customer requests.

Generative AI allows bankers to use large language models that speak like humans. Instead of navigating through many options, customers can use phrases like “What’s my balance?” or “Update my billing address” to quickly get the help they need.

Investment plan proposal

Generative AI can help banks provide more detailed advice when proposing investment plans to customers.

First, deep learning models learn from a lot of economic data. Bankers can then use AI to predict what will happen in the future using various financial factors such as currency rates, inflation, and politics.

In this way, they can create the correct investment plan. And the best part is that customers don’t have to share all their financial information.

compliance management

Compliance management is a continuous process of monitoring and evaluating systems to ensure compliance with industry and security standards, corporate and regulatory policies, and requirements.

Banks exist in a highly regulated industry and are required to adhere to strict rules.

This includes monitoring transaction activity, compiling relevant information, and submitting it to the designated department on time.

If a bank uses an AI system that has learned how these processes work, it will help the bank handle all the rules. For example, bankers can use generative AI to review customer data to ensure compliance with Know Your Customer (KYC) rules before approving an account.

Financial analysis and forecasting

Like any other business, banks need to strategize and maintain a strong position in evolving market conditions. Generative AI allows banks to simulate scenarios, predict economic trends, and change strategies accordingly.

For example, banks can use AI to predict how inflation will change shortly and adjust interest rates accordingly.

personalized financial guidance

Generative AI allows banks to provide equal and personalized engagement to every customer. Deep learning models examine customers’ historical data, spending behavior, and risk preferences. We will then suggest products that are suitable for them. This not only improves sign-up rates but also helps retain existing customers.

AI-based fraud detection

Due to frequent data breaches, banks face regulatory pressure to protect customer interests and prevent fraudulent attempts.

Generative AI can be trained to identify anomalous patterns in large volumes of financial transactions and immediately alert you. This allows banks to stop suspicious transactions and maintain customer trust.

Loan score management

Banks evaluate several criteria before approving or rejecting your loan application. Generative AI can aid in credit scoring by analyzing an applicant’s financial history and current data.

For example, machine learning models are trained to predict the likelihood of default by evaluating an applicant’s salary, age, occupation, residence, and other credit metrics.

Automation of office operations

Banks are making significant investments in the workforce to operate back-office processes such as scanning documents, verifying personnel identities, and securing network infrastructure. Integrating generative AI into workflows reduces the burden on operational staff.

For example, they can use NLP software to scan, process, and classify physical documents into secure cloud storage.

Preparation of financial reports

Generative AI is built from machine learning models that can present well-structured information. This allows banks’ AI systems to automatically generate financial statements on demand.

For example, customers can request customized cash flow or revenue reports, and the AI will compile them into files in seconds.

Legacy software maintenance

Some banks still use software developed from outdated programming languages. Instead of rewriting software from scratch, developers use generative AI and underlying large-scale language models to generate code.

This improves coding efficiency and reduces human error when migrating software to new programming frameworks.

②Limitations of generative AI

While generative AI may be useful for the banking industry, there are some drawbacks that banks should be aware of.

Data security

Generative AI creators take enormous measures to ensure the security and privacy of their users. However, banks must take measures to protect the security of their users.

This is seen as a major concern for banks as they are dealing with sensitive borrower information and material non-public information (MNPI) which can make the process less efficient.

Accuracy of data analysis

One area that requires major testing is the accuracy of data analysis of basic financial ratios. ChatGPT says it is capable of analyzing financial and related information, but early testing suggests inaccuracies.

Therefore, this requires further testing, and any errors need to be addressed.

cost

Implementing generative AI in banking systems can be quite costly. Banks need to consider how much they will spend on integration, training, deployment costs, ongoing operational costs, and regulatory costs. Generative AI is a great tool, but it may be out of reach for small banks with tight budgets.

Limited interaction with people

Automation certainly has its benefits, but it reduces the need for human labor. For people who prefer to talk to people, going through the many steps of artificial intelligence can be frustrating until the system allows them to pass.

Furthermore, the implementation of AI means the need for humans will be reduced, and the job market will become smaller accordingly.

technology dependent

Technology is an incredible tool, but there is no perfect replacement for humans.

Relying on generative AI may lead to dependencies that can lead to oversight and blind decision-making, leading to bank mistakes.

in conclusion

As mentioned above, generative AI improves efficiency and decision-making at work. However, it is extremely important to take into account its shortcomings and consider it objectively before introducing it.

In short, while generative AI is shaping the new future of finance, reaping its full benefits requires using it wisely.

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Generative AI in the financial industry | Key use cases and limitations

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