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.

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

7. Statistical
Theories
used for
Predictive
Analytics
Logistic
Regression
Linear
Regression
Skewness
Kurtosis
Testing of
Hypothesis
Correlation
Statistical
Dispersion

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