Login Try our product

Generative AI: Unleashing Human Potential Through Automation

Generative AI has come a long way in the past few years. We’ve all been mesmerized by its ability to produce human-like responses and generate visual representation for your bravest and most creative ideas.   


The latest breakthroughs in artificial intelligence have enabled machines to create custom content and generate text from raw data. These developments created a way for companies to generate massive business value with a plethora of use cases: automation, document processing, fraud detection, customer support, etc. 


From DALL-E to GPT-4, the technology behind generative AI becomes more and more sophisticated each day, not only raising eyebrows but also concerns about its safety and possible negative implications for humans. 


So let’s have a look at how generative AI is currently used in the financial sector and what its benefits and challenges are. Also, let's delve into the topic and figure out whether ChatGPT and other generative AI technologies are actually after your job.


Generative AI in the financial sector: Benefits and Challenges


In the past few years and months, you could witness a boom in the tech industry with hundreds of AI-powered tools emerging and flooding the market. From conversational intelligence to CRM automation, financial organizations now have a vast choice of tools to help them increase their performance metrics and supercharge their staff. 


Not only has it become very easy to automate routine processes, but you can also make use of AI-generated insights to make decisions quicker and more accurately. This has enabled client advisors and relationship managers to stay ahead of the competition, save time and resources, and improve their efficiency. 


Additionally, these advancements have allowed financial institutions to more accurately predict trends and create personalized products and services that meet the needs of customers. With these advancements, generative AI has proven to be a game-changer in the financial sector, and its influence is expected to continue to grow in the coming years.


However, the emergence of Artificial Intelligence technology has raised concerns about its potential to replace professionals and take away jobs. 


Automation has become increasingly popular in the financial industry, with AI-driven tools used for tasks such as financial analysis, portfolio management, and data analysis. This has caused some to worry that AI may soon be able to take over roles traditionally held by humans, such as financial advisors, analysts, and accountants. In addition, AI can also be used to automate processes such as trading and investment decisions, which, some fear, could eliminate the need for human intervention. 


AI and Job Replacement Concerns: Separating Fact from Fiction


While there is no denying that AI can make certain tasks more efficient and cost-effective, there are also concerns about the potential for it to reduce the number of jobs available in the financial industry.


However, in the financial industry it's too early to talk about any replacements. Despite the fact that we're all blown away by responses produced by AI, the technology is still in the development stage and the outputs are far from perfection and 100% accuracy. 


Additionally, OpenAI executives stress the fact that AI will indeed transform the way current jobs look, but in a more general way, it plays a role of a co-pilot to supercharge and enable professionals across various industries. 


Let’s have a look at the most impactful use cases for the generative AI in the financial industry:


1. Automation. With the help of AI-driven tools (e.g. Unique) relationship managers and client advisors can record client conversations and generate custom GPT-generated insights based on prompts, and use them to create follow-up emails, high-level overviews or executive summaries, which would be automatically sent to their CRM system.

2. Lead Generation: AI-driven solutions and tools are capable of detecting certain keywords during a client conversation and create an overview of pain points mentioned so that RMs and client advisors could create a better marketing strategy and spot upselling and cross-selling opportunities. 


3. Risk Management: Generative AI can be used to develop models that help financial institutions manage risk. By analyzing data from the past, AI-driven platforms can generate new models that can help identify potential risks and develop strategies to minimize them.

All these examples represent the actual impact of generative AI on productivity of the staff. From insurance advisors to relationship managers, their jobs rely heavily on building long-term trusting relationships with their clients. Therefore, not even the most sophisticated AI chatbots can replace this workforce. 


Whenever clients have issues related to their insurance coverage or investment strategy, they will always seek expert opinion from a human adviser. Advisors, in their turn, will be able to provide better, more informed advice and consultation with the help of generative AI and AI-powered tools. Not only will these tools save advisors plenty of time that they usually spend on routine repetitive tasks, but they will also provide them with valuable assistance that will increase client satisfaction and loyalty. 


Imagine a client advisor equipped with the latest technology that significantly facilitates their day-to-day work, how much time this will free up for them to focus on serving clients with utmost attention to detail.


AI will not steal jobs, but instead will supercharge professionals in the future by automating mundane and repetitive tasks, allowing for faster and more accurate decision making, and allowing for increased efficiency. 


Written by

Hanna Karbowski