Big data, Machine learning and the Auditor
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This document discusses how auditors can use big data, machine learning, and analytics. It defines big data and machine learning, describing techniques like supervised vs. unsupervised learning. It provides examples of how auditors could use these approaches for risk management, fraud detection, process mining, compliance, and more. Specific use cases are outlined, like vendor collusion identification and predictive analytics for bad debts. Statistical methods like logistic regression that could support predictive analytics are also mentioned. The document suggests auditors are well-positioned to help companies implement big data and machine learning for assurance, automation, and controls.
Big data, Machine learning and the Auditor
- 1. Big Data, Machine Learning and the Auditor BHARATH RAO AND ANAND P JANGID
- 2. Data and Big Data - Intro Data includes any piece and bit of information which can be recorded and stored to be put in use in the future Big Data is a scientific field of information analysis wherein the following processes are part. - Data Identification - Data Source Identification - Data Storage - Data Categorization - Data Analytics - Data Reporting - Life of Data
- 3. Machine Learning - Intro Machine learning is the field that gives computers the ability to learn without being explicitly programmed Supervised learning: Maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal.
- 4. Analytics Performed General Analytics of Data Analytics for External Confirmation Predictive Analytics
- 5. Areas for Analytics and Machine Learning Enterprise Risk Management Audit Risk Management Fraud Risk Management Automation Internal Audit New Fraud Patterns Control Assessments Forensics
- 6. Use Cases for Analytics and Machine Learning - Identification of Vendor Collusion - Predictive Analytics for determining the chances of a bad debt - Process Mining and identification of process weakness - Compliance Management - Automation of Internal Controls and it’s enforcement - Travel and Expense Claims frauds - Governance, Risk and Compliance - Identification of gaps and weakness in Material Management - Vendor Validation - Identification of anomalies - Determination of effective point of Revenue Recognition - Expense Analytics and determination of provisioning - Data Mining - Identification of Fraud for promotional items - Performance Evaluation against budgeted funds and time - Three way match and Payment Analytics
- 8. Industries Insurance Banking FMCG Telecommunicati on Aerospace Automobile Oil and Petroleum Manufacturing Steel Real Estate
- 9. Opportunities in Big Data Analytics Big Data Pre Implementation Preparation Big Data Analytics Framework Design Big Data Design of Dashboards, Reports and Visuals Big Data Assurance
- 10. In the words of Gordon Gekko, Wall Street (1987)
- 11. Thinking out of the Box An Auditor being exposed to variety of business, data, knowledge, etc. would be considered the right person to provide solution for a company to implement Big Data and effectively using Machine Learning for Assurance, Automation and Control.
- 12. Thank You CONTACT BHARATH RAO BHARATH.RAO@QUADRISK.COM