Banking

Algorithms have supported financial services for decades. They have helped companies grow nest eggs and enhance products for customers since before the computer was invented. Today’s financial institutions are doubly focused on customers; but it’s their use of data science, automated trading, machine learning (ML), and artificial intelligence (AI) that is driving customer experiences and the bottom line today.

“Banks can use AI to transform the customer experience by enabling frictionless, 247 customer interactions — but AI in banking applications isn’t just limited to retail banking services. The back and middle offices of investment banking and all other financial services for that matter could also benefit from AI.” -Business Insider, “AI in Banking report 2019”

Fortunately, banks of all kinds can find opportunities to improve existing data science workflows. By incorporating data lineage, teams can develop unbiased models, provide regulators with critical information, and speed up the certification process. Banks and other institutions can optimize how they manage massive amounts of varied data in both legacy and greenfield systems. Automated end-to-end pipelines can scale to solve unique problems, allowing data scientists to spend less time with ad hoc workflows and complex queries and more time developing new and innovative models.

How Pachyderm can help

We know that data science teams become more effective and efficient when workflows are explainable, repeatable, and scalable. Our work with RBC, digital reasoning, and our enterprise-grade platform have proven results helping teams in the banking industry succeed. Designed for any relevant use case, Pachyderm is your go-to solution for financial data success.

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