Data Science in Banking & Finance
The banking and finance sector offers one of the most powerful applications of Responsible Data Science. Through partnerships with organizations like First National Bank (FNB), PNC, and BNY Mellon, RDS@Pitt explores how data can drive transparency, risk management, and customer trust in highly regulated environments. Students engage with real-world financial datasets and scenarios that emphasize ethical modeling, data governance, and accountability—preparing them to lead responsibly in data-driven financial institutions.
Context and Datasets
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News.Bank.001 News.Bank.001
- Data Science in Banking & Finance
The banking industry faces a growing threat from AI-enhanced fraud schemes that bypass traditional detection systems. TrustNet Bank, a mid-sized financial institution, serves both personal and small business custom
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News.Bank.002 News.Bank.002
- Data Science in Banking & Finance
The rise of sophisticated deepfake technology has increased risks of identity fraud in the financial services industry. A leading financial institution is working to strengthen its fraud detection capabilities to safeguard customer assets and maintain trust.
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News.Bank.003 News.Bank.003
- Data Science in Banking & Finance
The banking industry faces rapid transformation driven by economic shifts, regulatory changes, and technological innovation
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FNB.Ban.001 FNB.Ban.001
- Data Science in Banking & Finance
The banking industry is undergoing a significant transformation driven by large investments in artificial intelligence and advanced technologies aimed at improving customer experience and operational efficiency.
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News.Bank.004 News.Bank.004
- Data Science in Banking & Finance
A mysterious “coding issue” in Equifax’s AI-driven credit scoring system led to consumers receiving inaccurate credit scores in early 2022, illustrating risks within the financial services and data infrastructure industry.