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Anand Ghosh

Anand is a Data Governance strategist with Perficient. He has helped several Top Tier Financial Institutions in USA and Europe in digital transformation, and align with risk and regulatory requirements. His deep experience in regulatory data and financial services and business processes allows him to provide valuable insights and guidance for companies looking to improve their data governance strategies. Anand’s current focus is on Data Privacy in the age of AI and staying at the forefront of emerging technologies and trends. Anand is a globe trotter and loves to enjoy going concerts to fulfill his leisure’s. If you’re looking for a knowledgeable and experienced Data Governance strategist with a passion for Banking, AI and data privacy, look no further than Anand. His expertise and dedication make him an invaluable asset to any organization.

Blogs from this Author

Ethical AI

Ethical AI in Video Interviews: Compliance and Data Governance

In recent years, video job interviews have become the norm, offering numerous advantages for both employers and candidates. However, with technological advancements come new responsibilities, especially when it comes to protecting candidates’ rights and privacy. In this blog post, we’ll explore the growing trend of video interviews, take a closer look at Illinois’ Artificial Intelligence […]

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Revamping Data Integration for CRA Compliance: A Necessity in the New Normal

The Community Reinvestment Act (CRA) is a federal law in the US that promotes the interest of financial service firms to serve their communities’ credit needs, including low- and moderate-income neighborhoods. Federal banking agencies use the bank’s contribution metrics as a parameter when they apply for mergers, acquisitions, and new branch openings. CRA remains essential […]

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Don’t Let Poor Data Quality Derail Your AI Dreams

AI is reliant upon data to acquire knowledge and drive decision-making processes. Therefore, the data quality utilized for training AI models is vital in influencing their accuracy and dependability. Data noise in machine learning refers to the occurrence of errors, outliers, or inconsistencies within the data, which can degrade the quality and reliability of AI […]