Building NLP applications
for financial services using data-centric AI
June 8, 2022 | 11∶00 AM–11∶45 AM Pacific Time
NLP is used across the financial industry, from retail banking to hedge fund investing. NLP techniques help financial institutions make informed decisions by processing and extracting data from unstructured documents such as 10-K reports, legal contracts, credit reports, and many other document types.
10-Ks, in particular, contain a wealth of information such as earnings, risk factors, company strategy, and more. Financial institutions use information from 10-K documents to improve credit decisions, carry out KYC, discover financial health, and more.
However, building NLP techniques to understand 10-Ks is challenging, costly, and labor-intensive. Join us to learn how the world’s largest banks are using Snorkel Flow’s data-centric AI platform to build highly accurate NLP applications in a matter of days.
In this webinar, you will learn
- How Snorkel Flow is used to extract information from 10-Ks
- How data-centric AI and programmatic labeling enables teams to get models into production faster while improving scalability, adaptability, and governability.
- The real-world impact Snorkel Flow is delivering for the world’s largest financial services companies
Presented by
AARTI BAGUL
Machine Learning Engineer
Snorkel AI
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About the presenter
Aarti Bagul
Aarti is a machine learning engineer at Snorkel AI where she enables some of the top organizations across banking, healthcare and government to solve complex ML problems using Snorkel Flow. Before Snorkel AI, she worked closely with Andrew Ng in various capacities—at AI Fund helping build ML companies from scratch internally, as well as investing in ML companies (a hybrid MLE/PM/VC associate role), as a machine learning engineer at his startup Landing AI, as head TA for his deep learning class at Stanford (CS230), and in his research lab at Stanford.
She graduated with a masters in CS from Stanford and with bachelors in CS and computer engineering from NYU with highest honors. During her time at NYU, she worked in David Sontag’s lab on applications of machine learning to clinical medicine, and at Microsoft Research as a research intern for John Langford, where she contributed to Vowpal Wabbit, an open-source project.