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Go beyond rules-based data analytics for audit with machine learning techniques. Machine learning can take any dataset and make predictions as to whether the data is accurate or anomalous.
In this session, we’ll dive deep into the practical applications of machine learning to detect, prevent, and mitigate fraud.
We’ll show you:
- How machine learning can transform audit and forensics functions
- Why professional judgement is key for effective analytics, and why artificial intelligence isn’t a “silver bullet”
- How to apply supervised and unsupervised learning algorithms to detect new anomalous data patterns
- How to identify use cases for supervised machine learning in audit, risk, and compliance
Phil Lim
Senior Product Manager, Analytics at Galvanize
Phil has over 11 years of experience advising audit, risk, compliance, and finance teams of Fortune 500 and government organizations. Phil has deep experience implementing programs aimed at monitoring anti-bribery, data-privacy, and fraud, waste, and abuse risk.
Angie Contarbio
Technical Product Manager, Product at Galvanize
Angie has more than 6 years of experience delivering high-quality software implementations for clients, with a focus on the plan, design and execution of data analytics and automation solutions. She is well-versed in engagements involving Internal Audit, IT Audit, SOX Audit, and Enterprise Risk Management.
