Resources

The 'AI Broadcast' event series

Improving reliability, fairness, and inclusiveness of ML underwriting models

Emerging regulatory framework for AI in the US

Impact of RBI guidelines and components required for AI Governance

Applications, best practices and impact of Indian regulations

ML Explainability and methods that can be used for ‘Fraud Monitoring’ use case

Increase fraud monitoring coverage and efficiency to enable swifter interventions for risk mitigation

Overview of global Policies and regulations around AI usage, their sufficiency and impact on our future

Explore the current AI adoption in financial services, the ‘black box’ problem with AI and how explainability helps resolve the trade-off between accuracy, automation and being compliant

Arya.ai has innovated a state-of-the-art framework, ‘Arya-xAI’ to offer transparency, control and Interpretability on Deep learning models. This whitepaper explores the explainability imperative, tangible business benefits of XAI, overview of current XAI methods and their challenges and details on the functioning of Arya-XAI framework

From the lab to production: explainable, reliable, trustable AI