Generative AI in FinTech, IT

How Stripe is using Generative AI for Dev accessibility

Payment processing platform Stripe has implemented Generative AI to their extensive developer documentation. This enhancement allows developers to pose natural language queries within Stripe Docs to GPT-4, which will answer by summarizing the relevant parts of the documentation or extracting specific pieces of information. This purports to let developers spend less time reading and more time building.

GPT-4 to Stripe Documentation allowing users to ask Stripe Docs questions like:

• What is test mode?

• What is a Payment Intent?

• How do I test my Stripe integration?

In a press release from Stripe:

“Like the introduction of email, smartphones, or videoconferencing, GPT-4 has the potential to fundamentally rewire—and improve—how businesses run,” said Eugene Mann, product lead for applied machine learning at Stripe. “By integrating GPT-4, Stripe is giving our users the most advanced tools to help them build and grow online.”

In addition, Stripe uses GPT-4 to detect fraud. By examining the structure and content of Stripe operated Discord community posts, GPT-4 identifies suspicious accounts that require further investigation by Stripe's fraud team. This helps ensure that these accounts are not fraudulent actors pretending to be cooperative. Additionally, GPT-4 assists in analyzing incoming communications to uncover instances of coordinated misconduct by malicious actors.

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Generative AI in Data Analytics

AI powered feedback analysis platform Viable is using GPT-4 to conduct in-depth analysis of qualitative data with exceptional accuracy at large scales.

Viable has tackled the challenges of summarization and analysis of data to deliver fast and accurate insights from customer support interactions to recorded transcripts, providing companies with actionable insights to improve their Net Promoter Score, reduce support ticket volumes, and better inform their product roadmaps.

Viable is using GPT for:

• Advanced text analysis that goes deeper into understanding the root causes of customer feedback, even identifying issues hidden in sarcasm or word ambiguity.

• Churn mitigation, by analyzing customer feedback to assign urgency levels based on the associated churn risk.

• Metadata visualization with the help of charts and graphs showcasing the traits of users delivering that specific feedback (e.g. location, customer type, NPS, etc.)

IT Giants identify ways to leverage Generative AI

Information technology companies like Accenture are also increasingly building advanced capabilities based on Generative AI, identifying over 300 use cases across 19 industries where generative AI can deliver significant results.

In a post on the Accenture website, they discuss how companies can make the most of the advancements in AI capabilities. To capitalize on these changes, businesses need to reinvent work processes and find ways to extract value from AI technology. The following key points are highlighted:

1. Dive in with a business-driven mindset: Companies should approach AI experimentation from two angles. Firstly, they should focus on quick wins by utilizing readily available models and applications. Secondly, they should work on customizing models using their own data to drive business transformation. A business-driven mindset is crucial to define and achieve the desired outcomes.

2. Take a people-first approach: It's important to invest in both AI creation and utilization skills. This involves developing technical competencies such as AI engineering and enterprise architecture, as well as training employees throughout the organization to effectively work with AI-infused processes.

3. Prepare proprietary data: High-quality data is essential for training foundation models. Acquiring, refining, safeguarding, and deploying data should be approached strategically and with discipline. Building a modern enterprise data platform on the cloud and creating a trusted set of reusable data products is recommended.

4. Invest in a sustainable tech foundation: Consider infrastructure, architecture, operating models, and governance structures while leveraging generative AI and foundation models. Cost-effectiveness and sustainable energy consumption should be prioritized.

5. Accelerate ecosystem innovation: Access resources and expertise from partners such as big tech players, start-ups, professional services firms, and academic institutions to build and scale AI applications. Leverage industry best practices and insights from these ecosystem partners.

6. Level up responsible AI: Evaluate the robustness of the company's responsible AI governance regime before scaling up generative AI applications. Incorporate controls for risk assessment during the design stage and embed responsible AI principles and approaches throughout the business.

By following these guidelines, companies can effectively embrace AI capabilities and drive competitive advantage through innovation, efficient processes, and responsible practices.

See you next time,
Mukundan

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