Generative AI, often referred to as GenAI, has been the subject of much hype in recent years. However, Mastercard believes that this revolutionary technology is more than just another passing trend. According to their Signals Report, generative AI is on the verge of significant economic disruption due to its clear use cases and rapid development.
Unlike many emerging technologies, generative AI has already demonstrated its value and led to the creation of robust solutions. Ken Moore, Chief Innovation Officer and Head of Foundry at Mastercard, compares the excitement surrounding generative AI to that of other groundbreaking technologies such as the internet and the smartphone. Its impact is not limited to a specific industry or geographic region; instead, it has the potential to be applied cross-functionally.
One of the key factors driving the development of generative AI is its democratization. Previously, only technically competent individuals could harness its power. However, with the launch of Open AI’s Chat GPT interface, the average consumer was exposed to the capabilities of generative AI. Companies like Klarna, Expedia, and Instacart have utilized this technology to enhance consumer experience by providing personalized recommendations and information search. To further democratize the technology, Meta has launched their own open-source generative AI model called LLaMA.
The shift toward open-source platforms empowers institutions to use generative AI safely, without compromising their proprietary data or that of their customers. This accessibility could supercharge adoption, making generative AI accessible for enterprises of all sizes. Mastercard emphasizes that models trained on specific data, such as transaction history, can greatly enhance banking interactions.
Another significant development that complements generative AI’s growth is the acceptance of open banking. With more data becoming available through open banking platforms, generative AI can access broader datasets and create more sophisticated models in specific verticals. This influx of data, coupled with the launch of open-source generative AI, could revolutionize the financial services industry.
While it is still in its early stages, generative AI has already demonstrated its potential in various aspects of the digital ecosystem. Financial institutions are leveraging AI for personalized banking services, fraud detection, and regulatory compliance. However, for now, human oversight remains crucial. Fake information and biased outcomes are challenges that require further development before generative AI can operate without human intervention entirely.
Generative AI holds immense promise, and its potential economic disruption should not be overlooked. As the technology continues to evolve and mature, we can expect to see its widespread adoption across industries, driving innovation and transforming the way we interact with technology.
What is generative AI?
Generative AI, or GenAI, refers to a subset of artificial intelligence that involves the creation of new content, images, or ideas based on patterns and existing data. It utilizes deep learning techniques to generate outputs that mimic human creativity and cognition.
How is generative AI being democratized?
Generative AI is becoming more accessible to individuals and organizations without technical expertise through the development of user-friendly interfaces and open-source platforms. This democratization allows a broader range of people to harness the capabilities of generative AI for various applications.
What role does open banking play in the development of generative AI?
Open banking, which involves the sharing of financial data between different institutions and platforms, provides generative AI with access to a wider network of data. This influx of data fuels the development of more sophisticated models and enhances the capabilities of generative AI in specific verticals, such as personalized banking services and fraud detection.
What are the limitations of generative AI?
While generative AI shows great potential, it still requires human oversight due to challenges such as the generation of fake information and biased outcomes. Human intervention is necessary to ensure the accuracy and ethical use of generative AI’s outputs.