Successful AI implementation doesn’t depend on good technology; it requires strong data strategies and judgment to know when ...

Launching and scaling a SaaS business comes with a unique set of financial challenges. Without a clear, structured financial model, it’s nearly impossible to make strategic decisions or raise capital ...

If you are a Global 20,000 company and you want to build a large language model that is specifically tuned to your business, the first thing you need is a corpus of your own textual data on which to ...

Transfer learning addresses data scarcity and resource limitations by allowing a model trained on one task (e.g., stock market prediction) to apply its learned knowledge to another task, such as ...

Learn to understand and apply the most cutting-edge technological developments and analytical tools in FinTech. As part of the Rutgers Stackable Business Innovation Program (rSBI), the Financial Data ...

JPMorgan’s argument is that banks have spent millions of dollars to build APIs, or application programming interfaces, that ...

Facing tighter budgets and lower yields, many colleges and universities are investing in predictive modeling to target scholarships, forecast enrollment and adapt quickly to policy changes.

Many of the 100 banks in the BankTech Ventures consortium have expressed interest in using the data-sharing network.

Imagine being able to ask a single system to code an application, analyse financial data, explain a complex medical concept, or draft a detailed report in seconds. That is the promise of GPT-5, the ...

In this context, a robust digital finance architecture becomes essential. Modern cloud ERP solutions, such as SAP S/4HANA Cloud (a product of my company), provide the foundation to transform ...

Additionally, the new data standards could impact the format in which financial information is submitted under current MSRB rules, such as Rule 15c2-12, Rule G-32 or Rule G-34.