A post by Sophie Hundertmark (Sophie on Linkedin: https://www.linkedin.com/in/sophie-hundertmark/)
Artificial intelligence is revolutionizing numerous industries – and its influence is growing rapidly in the financial sector in particular. More and more banks, insurance companies and asset managers are using AI-supported systems to automate processes, improve customer experiences and meet regulatory requirements more efficiently. According to our studies at the Institute of Financial Services Zug (IFZ) at Lucerne University of Applied Sciences and Arts, AI has become indispensable for financial companies and other industries. And the usage figures are still rising.
ChatGPT plays a central role here as a specialized language assistant: With its ability to analyze complex data volumes and translate them into natural language, ChatGPT opens up new possibilities – from the rapid creation of reports and the analysis of market trends to direct interaction with customers. At the same time, the handling of sensitive financial data and regulatory requirements place high demands on the responsible implementation of this technology.
In this article, I will show in a compact and practical way how ChatGPT can change the financial sector in the long term – and which specific areas of application can already be successfully established today.
Applications of ChatGPT in the financial sector
1. generate reports
Significance: Finance departments must produce structured reports on a regular basis.
Implementation: ChatGPT can automatically formulate and summarize reports from existing raw data.
Example: Creation of a quarterly report based on sales and expenditure data.
2. analysis of text data and messages
Meaning: Financial information from news or reports must be evaluated quickly.
Implementation: ChatGPT analyzes large volumes of text data and filters out relevant financial information.
Example: Summary of the most important findings from daily financial news.
3. ask questions about specific reports
Significance: Detailed questions about extensive financial reports are often time-consuming.
Implementation: With targeted prompts, ChatGPT can quickly search reports and provide precise answers.
Example: In response to a request, ChatGPT explains deviations in the budget report.
4. data analysis with visualizations
Significance: Data analyses often have to be prepared in an understandable way.
Implementation: ChatGPT can interpret Excel data and suggest or create suitable visualizations.
Example: Creation of a diagram to illustrate sales trends.
5. have an investment overview created
Significance: Investment portfolios must be presented clearly and up to date.
Implementation: ChatGPT structures existing data into a clear investment summary.
Example: Overview of all current investments with yield development.
6. answer customer inquiries
Meaning: Financial service providers receive many inquiries about accounts, transactions or products.
Implementation: ChatGPT can answer customer questions quickly, correctly and individually.
Example: Quick response to a customer inquiry about account management or credit terms.
7. prepare texts for specific target groups
Meaning: Financial texts must be formulated differently depending on the target group.
Implementation: ChatGPT adapts the tonality and complexity of financial texts to different reader groups.
Example: Preparation of a report for both professionals and private customers.
8. education and training
Significance: Employees in finance require ongoing training on new tools and processes.
Implementation: ChatGPT supports the creation of training material and learning content.
Example: Creating a learning module on new compliance guidelines.
ChatGPT in finance – ATTENTION
There are a few important points to bear in mind when using ChatGPT in connection with financial data. Remember the following points and do not act irresponsibly with sensitive data:
Do not enter any data worth protecting in ChatGPT.
Use secure alternatives, for example SwissGPT or OnPremise.
Responses from ChatGPT may be incorrect
Every result checked by humans.
ChatGPT must be able to process and understand data
All data must be cleanly prepared.
Steps for effective data cleansing
The right and well-structured data is the basis for successful AI support in the financial sector. Here are some tips on how you can prepare your data for an LLM such as ChatGPT.
1. thoroughly examine your data before you start the cleanup process
Meaning: Understanding your data set before cleansing helps to identify redundant fields or inconsistent formats.
Implementation: Use data profiling tools to analyze distributions, missing values and unusual patterns.
Example: Check in a customer database whether all entries contain an e-mail address or telephone number.
2. standardize formats for uniform data
Meaning: Recurring fields such as dates, telephone numbers or currencies must be formatted consistently.
Implementation: Create a standardized template with Excel functions or Python scripts.
Example: Convert all dates to the format YYYY-MM-DD.
3. automate data cleansing tasks to save time
Meaning: Data cleansing often involves repetitive tasks. Automation speeds up the process and reduces errors.
Implementation: Use macros in Excel or libraries such as pandas in Python.
Example: Write a script that automatically marks and removes duplicate entries.
4. edit missing values to improve data quality
Significance: Missing data can distort analyses. You have to decide whether to fill, estimate or remove them. Imputation can help with predictable patterns.
Implementation: Use statistical models or machine learning algorithms for imputation.
Example: Add missing sales figures based on the average of previous entries.
5. validate cleansed data against the original data set
Meaning: After cleansing, you should check the results against the original data to ensure that the quality has actually been improved.
Implementation: Compare the adjusted data set with the original on a random basis.
Example: Check whether duplicate entries have been removed without losing unique data records.
Common challenges in data cleansing
My experience shows that most companies face the following challenges when it comes to finding suitable data for an LLM.
1. data volume – splitting large data sets
Problem: Large volumes of data from different sources make cleansing more difficult and increase the susceptibility to errors.
Solution: Divide large data sets into smaller, manageable parts and use scalable processing tools such as Hadoop or Apache Spark.
2. data inconsistencies – standardize different sources
Problem: Different formats, values and structures make it difficult to merge and analyze the data.
Solution: Create a uniform data dictionary and use transformation tools to standardize formats automatically.
3. unclear quality standards – define clear benchmarks
Problem: Without fixed criteria, it is difficult to judge when data has been sufficiently cleansed.
Solution: Define specific, measurable quality standards such as error tolerances or completeness quotas.
4. high costs and time expenditure – use automation
Problem: Manual data cleansing costs a lot of time and resources.
Solution: Invest in automated tools that take over routine tasks and increase efficiency.
5. maintain clean data – carry out regular updates
Problem: Data quickly becomes outdated and can become unreliable without maintenance.
Solution: Establish fixed cleaning cycles and use real-time validation to keep data continuously up-to-date and correct.
How ChatGPT helps you work with data in Excel
When it comes to the use of ChatGPT in the financial environment, Excel quickly comes into play. Here are a few additional tips for using ChatGPT in conjunction with Excel.
1. have data summarized in Excel
How it works: Upload your data (e.g. customer feedback or sales figures) to Excel and use ChatGPT to create summaries, for example with prompts such as “Summarize the three most common issues in column B” or “Identify the most important themes in customer feedback”.
Practical example: Scenario: Analyzing customer feedback from an online store. Result: ChatGPT recognizes that most complaints are about delivery delays and makes suggestions for improvement.
2. create Excel formulas with ChatGPT
How it works: Describe your calculation needs, formulate a request like “Create a formula to calculate the profit margin with column B (sales) and column C (costs)”, and ChatGPT will provide you with the appropriate formula, e.g. =(B2-C2)/B2.
Practical example: Scenario: A small company wants to calculate profit margins, but is not very familiar with Excel. Result: ChatGPT creates the formulas quickly and without errors.
3. clean up data in Excel with ChatGPT
How it works: Load your data set into Excel and describe your task, e.g. “Remove duplicates in column A” or “Standardize dates in column B to the format MM/DD/YYYY”.
Practical example: Scenario: Cleaning up a customer list with irregular email formats and duplicate entries. Result: ChatGPT ensures standardized e-mail formats and removes duplicate addresses.
4. categorize data in Excel
How it works: Prepare your data set and use prompts like “Categorize comments in column A as Positive, Neutral or Negative” or “Assign products in column B to the categories Electronics, Clothing or Housewares”.
Practical example: Scenario: An online store wants to organize product reviews by mood. Result: ChatGPT tags the comments efficiently and saves hours of manual work.
5. data analysis and forecasts in Excel with ChatGPT
How it works: Load historical sales or performance data into Excel and ask analysis or forecasting questions such as “How are sales in column B and C performing?” or “Predict sales for the next quarter.”
Practical example: Scenario: A retail chain wants to plan its quarterly sales. Result: ChatGPT forecasts growth of 15% and thus supports warehouse planning.
6. create automatic reports in Excel
How it works: Load your raw data into Excel and give ChatGPT tasks such as “Create a summary report of Q1 2024 sales performance” or “Highlight key insights from columns A to D”.
Practical example: Scenario: A marketing manager needs a weekly performance report. Result: ChatGPT quickly creates a finished report that can be used directly.
Future outlook: How ChatGPT could further transform finance
The use of ChatGPT and comparable AI technologies in the financial sector is still in its infancy. The influence of these systems is likely to increase significantly in the coming years.
AI-supported advice is increasingly becoming the standard: robo-advisors, which create individual financial plans and communicate with customers in natural language, could supplement or even replace personal bank advisors. ChatGPT will also play a more important role in the area of fraud prevention by recognizing and evaluating unusual transactions more quickly.
At the same time, companies are developing customized AI models that are specifically tailored to regulatory requirements and industry-specific processes – so-called “FinTech LLMs”.
In the long term, we could see a stronger fusion of AI and traditional financial services, with ChatGPT not only taking on assistance tasks but also actively preparing strategic decisions.
In order to benefit from this development, financial companies should start building up expertise in AI management, data protection and the ethical use of AI today.
Conclusion: ChatGPT in finance – recognizing opportunities, mastering challenges
- ChatGPT offers great potential for increasing efficiency, automation and better customer interaction in the financial sector.
- The practical benefits are particularly evident when analyzing text data, creating reports and communicating with customers.
- However, regulatory and data protection requirements place high demands on responsible implementation.
- The use of ChatGPT will continue to increase over the next few years – companies should consider the opportunities and risks at an early stage.
- Those who build up targeted skills in dealing with AI today will secure decisive competitive advantages for the future.
Any further questions?
Do you have any questions? I am happy to support you, act as a sparring partner and answer your questions. I am always happy to receive your messages, preferably by WhatsApp message or e-mail.
This article is also available as a podcast episode
Attention! The podcast was created entirely by my AI-Assistant based on my contribution – no guarantee for incorrect content.
