Technology Category
- Analytics & Modeling - Machine Learning
- Sensors - Liquid Detection Sensors
Applicable Industries
- Education
- Finance & Insurance
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Chatbots
- Machine Translation
Services
- Data Science Services
- Training
About The Customer
The customer is a global consulting firm, part of the 'big four', with a history of over 170 years. The firm has built its reputation on rigorous analysis and the ability to anticipate change. It strives to provide its diverse team of experts with the most current and relevant accounting, auditing, and industry information. The firm has over 300,000 experts spread across 150 countries who spend hours each day manually reviewing various accounting, auditing, and industry information. The firm's data science team was tasked with leveraging AI/ML to reduce this costly effort and to innovate by helping to identify which of their clients are most likely to be audited.
The Challenge
A globally renowned consulting firm, with a history spanning over a century, was seeking to enhance its auditing capabilities by leveraging artificial intelligence. The firm's reputation hinged on its ability to conduct thorough audits, irrespective of their size, complexity, or location. The firm's experts were spending significant time manually reviewing various accounting, auditing, and industry information, a process that was both time-consuming and costly. The firm estimated that each auditor search lasted 10 minutes and cost $50-60 on average. The firm's data science team was tasked with streamlining news monitoring to anticipate changes in capital markets, regulatory trends, or technological innovation. They aimed to use custom NLP models to automatically analyze, categorize, and extract key client information from various sources. However, they faced challenges in labeling training data for the machine learning algorithms. It took three experts a week to label 500 training data points, and they found it nearly impossible to adapt to changes in data or business goals on the fly.
The Solution
The firm partnered with Snorkel AI to build an NLP application that could classify whether news articles were relevant to the original search term. Snorkel Flow's programmatic labeling capabilities allowed the team to label over 10,000 news articles in just a few hours, a task that would have taken a human audit team nearly a year. The data labeled using Snorkel Flow was 32% more accurate than their previous hand-labeling methods. The team also used Snorkel Flow's guided error analysis to rapidly improve model quality and drive fine-grained corrections, bringing their F1 score up from 70 to 85 in just a matter of days. Snorkel Flow also allowed the team to quickly react to changing market conditions and business needs by adjusting labeling functions rather than relabeling manually. The collaboration between the SMEs and data scientists was significantly improved using Snorkel Flow's Annotator Suite.
Operational Impact
Quantitative Benefit
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