Retail Fraud & Loss Prevention in Data, Brick-and-Mortar and Beyond – with Chris Nelson of Gap Inc.@2x-min

Retail Fraud & Loss Prevention in Data, Brick-and-Mortar and Beyond – with Chris Nelson of Gap Inc.

Retail fraud and loss prevention have always been significant business concerns impacting profitability and customer trust. However, with the emergence of AI technologies, there is a newfound potential to combat these challenges more effectively. 

Fighting Retail Fraud with Personalization and Classification AI Tools@2x-min (2)

Fighting Retail Fraud with Personalization and Classification AI Tools – with Experts from Instacart, Etsy, and Gap Inc.

This article is sponsored by Riskified, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.

The Future of Healthcare in Large Language Models@2x-min

The Future of Healthcare in Large Language Models – with Ylan Kazi of Blue Cross

In just the last year, rapid advancements in AI technologies, particularly in natural language processing (NLP), have dramatically impacted virtually every industry. Most recently, large language models (LLMs) have become front and center – driven by the overwhelming popularity of OpenAI's ChatGPT. 

OECD-Approved AI Tools and Resources for Financial Services@2x

OECD-Approved AI Tools and Resources for Financial Services

The OECD.AI Policy Observatory is an inclusive platform that brings together resources and expertise from the OECD and its partners to facilitate dialogue and provide evidence-based policy analysis on the impact of AI. It is built upon the foundation of the OECD AI Principles, the first intergovernmental standard on AI adopted in 2019, endorsed by OECD countries and partner economies.

002 – What Responsible AI Means for Financial Services – with Scott Zoldi

What Responsible AI Means for Financial Services – with Scott Zoldi 

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. 

Strategic Adoption of Large Language Models and Generative AI – with Asif Hasan of Quantiph@2x

Strategic Adoption of Large Language Models and Generative AI – with Asif Hasan of Quantiphi

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.

Overcoming Obstacles 
in Reaching ROI
for AI projects@2x

Overcoming Obstacles in Reaching ROI for AI Projects – with Fallon Gorman of NLP Logix

AI initiatives that cannot find ROI have no use in modern business. However, the path to ROI from enterprise-wide digital transformations is never a straight and narrow road. A pre-pandemic survey from MIT Sloan Management Review and Boston Consulting Group found that 70% of companies among those surveyed reported no value from their AI investments. 

001 – Finding _Ground Truth_ In Accounting Workflows – with Michael Hitchcock of Intuit

Finding “Ground Truth” In Accounting Workflows – with Michael Hitchcock of Intuit

While accounting as a discipline has roots going back to the 13th century, current-day accounting software is still based on manual record-keeping just as it was in the 1990s, only with digital checks in a digital checkbook register, allowing users to keep track of their finances on a computer.

002 – AI at HSBC

Artificial Intelligence at HSBC – Two Use Cases

HSBC Bank is a British multinational banking and financial services company. The bank has 130 branches and serves over 40 million customers. By total assets, HSBC is one of the ten largest banks globally.

How Culture Holds the Key to Building Exceptional AI Teams@2x

How Culture Holds the Key to Building Exceptional AI Teams – Advice from Leaders at Google, PwC, and Quantcast

In today's rapidly evolving world, pursuing AI excellence has become a top priority for organizations across industries. However, the path to building exceptional AI teams is not solely paved with technical expertise and cutting-edge algorithms. The true key to unlocking the full potential of AI lies in ensuring that the teams leading the charge in enterprise digital transformations are working cohesively.