Exploring Generative AI’s Leap From Content Creation to Financial Services
From its simple beginnings of data crunching to now generating autonomously produced content, AI is continuing to transform the digital space. Its newest version, generative AI, has piqued consumer interest with its ability to generate human-like content, images, and audio - setting a precedent for what's technically achievable. This technology opens a new door of possibilities for B2B startups; ones that are creating the algorithms (such as OpenAI) and ones that are implementing them into real-world applications.
Needless to say, AI is already embedded in businesses across multiple industries. However, generative AI introduces a heightened level of personalization, enabling enhanced accuracy and relevancy for output information. For example, I asked my cell phone provider’s chatbot “What’s the difference between iPhone 12 and 14”? The chatbot needed to direct me to an agent to give me a response. With generative AI, the chatbot would’ve been able to ingest all of the relevant information from the site, summarize it, and provide an accurate human-like response - saving time and resources for the company. It's no wonder why major players like Google and Microsoft are working hard to capitalize on generative AI’s potential. Accordingly, Google has released its chatbot Bard while Microsoft is reigniting its search engine, Bing.
We are still in the early days of expanding generative AI to enterprise use cases, mainly because the margin of error, albeit low, is still considered risky for businesses. However, I think there’s interesting potential for its application to financial services, particularly the areas where vast data input is foundational: wealth management, fraud detection, and lending.
Wealth Management
B2B wealth management tools enable advisors to create tailored portfolios that cater to each client's unique risk tolerances and goals. Despite advancements in automated solutions, experienced advisors still provide invaluable expertise and nuance to the decision-making process. Yet their wisdom is limited by the data at hand. Generative AI can empower advisors to go beyond the average market analysis and tap into a wider range of data points for greater accuracy in predicting future performance. Personalized advice can be tailored to each client by mining deeper than merely relying on their self-reported information.
Fraud Detection
Financial criminals have become more innovative in their techniques, increasing the need for fraud detection solutions. These solutions identify potential issues by monitoring customer transactions for suspicious behaviors. Generative AI can revolutionize fraud detection with its forward-thinking ability to forecast potential scenarios. Its capacity for synthetic simulation gives it heightened vigilance and precision when uncovering fraudulent behavior.
Lending
The emergence of alternative data has enhanced credit models, allowing small businesses to access lending products that may have previously been out of reach. Generative AI can take the underwriting process a step further by leveraging non-traditional historical and real-time data points to better assess a company's financial health - providing unprecedented levels of loan eligibility determination speed and accuracy.
Generative AI is on the horizon and poised to make a major impact on business operations, from customer service to back-office functions. With regulated businesses such as financial institutions having to grapple with the challenges that come with AI - compliance, risk management, and data privacy- new generative AI technology solutions would need to incorporate proprietary frameworks that offset potential risks. As we continue to explore the possibilities that generative AI offers, I look forward to seeing the use cases that are uncovered. If you are building or interested in the space, it would be great to hear your thoughts!
- Kiswana