Insights

One of our financial services customers, a leading union-owned bank, needed to prioritize data governance and auditability as the pre-requisite before starting data modernization, advanced analytics and AI implementations.

Testing generative AI systems is a complex task due to their non-deterministic nature, lack of transparency, resource intensiveness, ethical considerations, and evolving domain. However, it is crucial to ensure their reliability and security. Strategies like benchmarking, red teaming, and societal harm assessment are essential. The blog addresses these challenges and effective testing methodologies so that organizations can maximize the potential of generative AI while mitigating risks.

Artificial Intelligence (AI) has become a transformative force across industries, revolutionizing business processes, decision-making, and customer experiences. Its remarkable ability to analyze vast historical datasets, interpret complex human interactions, and apply these insights to current contexts is not just changing how businesses operate—it is unlocking unprecedented avenues for innovation and growth through technology.

The Banking, Financial Services, and Insurance (BFSI) sector grapples with a multitude of challenges including the demand for round-the-clock customer support, the pressure to reduce operational costs, and the growing expectation for personalized customer experiences.

Life Sciences and Consumer Health industry is witnessing a significant shift in recent years. Previously, the focus was primarily on consolidating fragmented data from disparate systems, which hindered seamless access and integration, making it challenging to derive valuable insights.