AI Articles and Analysis about Knowledge management

Implementing responsible AI in the financial sector is crucial for ethical practices, fairness, and transparency. Financial institutions must prioritize data privacy, address biases, ensure explainability, and practice ongoing monitoring. By doing so, they build trust, mitigate risks, and foster sustainable growth. 

As nearly every American household has realized over the last year, large language models combined with generative AI abilities pose tremendous challenges and opportunities for enterprises of every shape and size. Just ask anyone who has heard of ChatGPT.

In the era of big data, companies need help navigating through an overwhelming volume of unstructured data to uncover meaningful insights. The topic search process presents unique challenges in deciphering data signals and identifying critical information before problems escalate.

As a business practice, model development aims to create a dataset, tailored through machine learning, that can accurately predict outcomes or classify data based on input variables. By following a structured approach, developers can ensure that the model development process is efficient, effective, and reproducible.

Market surveillance refers to activity authorities conduct to ensure that products available to consumers adhere to applicable laws and regulations. Additionally, market surveillance in banking and finance takes a somewhat specific form.

It is exceptionally difficult to get started, much less succeed, in AI adoption if one does not have at least a foundational education, particularly in those “modules” pertaining to AI capabilities, requirements, and organizational readiness. A business wanting to implement AI must understand the current state of adoption and how this current state matches up against AI readiness requirements.

I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't.