Data analytics tools and techniques are increasingly being used in forensic accounting and internal auditing to uncover fraud and errors. Descriptive, diagnostic, predictive, and prescriptive analytics help auditors analyze large amounts of financial data. Techniques like Benford's Law, cluster analysis, and decision trees can help identify anomalies that traditional sampling may miss. AI and machine learning are also being applied to tasks like contract analysis, image recognition, and identifying outliers in big data sets.

1. Data Analytics
Data analytics in Forensic Accounting

2. Data Analytics
 Data analytics is the science of analyzing raw data to make conclusions about that
information. Many of the techniques and processes of data analytics have been
automated into mechanical processes and algorithms that work over raw data for
human consumption.
 For example, manufacturing companies often record the runtime, downtime, and
work queue for various machines and then analyze the data to better plan the
workloads so the machines operate closer to peak capacity.
 Gaming companies use data analytics to set reward schedules for players that
keep the majority of players active in the game. Content companies use many of
the same data analytics to keep you clicking, watching, or re-organizing content to
get another view or another click.

3. Data Analytics process

4. Data Analytics process
 Data Collection
The first stage of the data pipeline is ingestion. During this stage, data is collected from
sources and moved into a system where it can be stored.
 Data Processing
The next stage of the data pipeline prepares the data for use and stores information in a
system accessible by users and applications. To maximize data quality, it must be cleaned
and transformed into information that can be easily accessed and queried.
 Data Modeling
In the next stage of the data pipeline, stored data is analyzed, and modeling algorithms are
created. Data may be analyzed by an end-to-end analytics platform like SAP, Oracle, or
SAS—or processed at scale by tools like Apache Spark*
 Decision-Making
After data has been ingested, prepared, and analyzed, it’s ready to be acted upon. Data
visualization and reporting help communicate the results of analytics.

5. Types of
Data Analytics
Data analytics is broken down into four basic types.
1. Descriptive analytics: This describes what has happened over a given period of time. Have
the number of views gone up? Are sales stronger this month than last?
2. Diagnostic analytics: This focuses more on why something happened. This involves more
diverse data inputs and a bit of hypothesizing. Did the weather affect icecream sales? Did
that latest marketing campaign impact sales?
3. Predictive analytics: This moves to what is likely going to happen in the near term. What
happened to sales the last time we had a hot summer? How many weather models predict a
hot summer this year?
4. Prescriptive analytics: This suggests a course of action. If the likelihood of a hot summer is
measured as an average of these five weather models is above 58%, we should add an
evening shift to the brewery and rent an additional tank to increase output.

6. Data Analytics Tools
 R programming – This tool is the leading analytics tool used for statistics and data modeling. R compiles and runs on various platforms such as UNIX,
Windows, and Mac OS. It also provides tools to automatically install all packages as per user-requirement.
 Python – Python is an open-source, object-oriented programming language that is easy to read, write, and maintain. It provides various machine
learning and visualization libraries such as Scikit-learn, TensorFlow, Matplotlib, Pandas, Keras, etc. It also can be assembled on any platform like SQL
server, a MongoDB database or JSON
 Tableau Public/Power BI– This is a free software that connects to any data source such as Excel, corporate Data Warehouse, etc. It then creates
visualizations, maps, dashboards etc with real-time updates on the web.
 SAS – A programming language and environment for data manipulation and analytics, this tool is easily accessible and can analyze data from
different sources.
 Microsoft Excel – This tool is one of the most widely used tools for data analytics. Mostly used for clients’ internal data, this tool analyzes the tasks
that summarize the data with a preview of pivot tables.
 RapidMiner – A powerful, integrated platform that can integrate with any data source types such as Access, Excel, Microsoft SQL, Tera data, Oracle,
Sybase etc. This tool is mostly used for predictive analytics, such as data mining, text analytics, machine learning.
 KNIME – Konstanz Information Miner (KNIME) is an open-source data analytics platform, which allows you to analyze and model data. With the
benefit of visual programming, KNIME provides a platform for reporting and integration through its modular data pipeline concept.
 Apache Spark – One of the largest large-scale data processing engine, this tool executes applications in Hadoop clusters 100 times faster in memory
and 10 times faster on disk. This tool is also popular for data pipelines and machine learning model development.

7. Data Analytics Methods

8. Data Analytics Methods
 Cluster analysis
The action of grouping a set of data elements in a way that said elements are more similar (in a particular
sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable
when clustering, the method is often used to find hidden patterns in the data. The approach is also used to
provide additional context to a trend or dataset.
 Regression analysis
Regression uses historical data to understand how a dependent variable's value is affected when one
(linear regression) or more independent variables (multiple regression) change or stay the same. By
understanding each variable's relationship and how they developed in the past, you can anticipate possible
outcomes and make better decisions in the future. i.e. weather forecasting, crop yield prediction etc.
 Data mining
A method of data analysis that is the umbrella term for engineering metrics and insights for additional
value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify
dependencies, relations, patterns, and trends to generate advanced knowledge. When considering how to
analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth
exploring in greater detail.

9. Data Analytics Methods
 Neural networks
The neural network forms the basis for the intelligent algorithms of machine learning.
It is a form of analytics that attempts, with minimal intervention, to understand how the
human brain would generate insights and predict values. Neural networks learn from each
and every data transaction, meaning that they evolve and advance over time.
 Text analysis
Text analysis, also known in the industry as text mining, works by taking large sets of
textual data and arranging it in a way that makes it easier to manage. By working through
this cleansing process in stringent detail, you will be able to extract the data that is truly
relevant to your organization and use it to develop actionable insights that will propel you
forward.

10. Data Analytics Methods
 Time series analysis
As its name suggests, the time series analysis is used to analyze a set of data points
collected over a specified period of time. Although analysts use this method to monitor the
data points in a specific interval of time rather than just monitoring them intermittently, the
time series analysis is not uniquely used with the purpose of collecting data over time.
Instead, it allows researchers to understand if variables changed during the duration of the
study, how the different variables are dependent, and how did it reach the end result.
 Decision Trees
The decision tree analysis aims to act as a support tool to make smart and strategic
decisions. By visually displaying potential outcomes, consequences, and costs in a tree-
like model, researchers and business users can easily evaluate all factors involved and
choose the best course of action. Decision trees are helpful to analyze quantitative data
and they allow for an improved decision-making process by helping you spot improvement
opportunities, reduce costs, enhance operational efficiency and production.

11. Data Analytics Methods

12. Types of data visualization charts

13. AI in Accounting
AI possesses the potential to take the strength of human knowledge (skills and rules)
and apply these insights to gigantic datasets without the human weaknesses of
inattention, bias, and fatigue. AI use in many industries has proliferated due to the
availability of big data and the power of quantum computing.
 Accounting firms are heavily investing in the development of AI systems, ranging
from automation of processes (e.g. Robotic Process Automation, or RPA), to
contract analysis, to image recognition (using drones).
 Deloitte and EY have used Natural Language Processing (NLP) in their tax
services to expedite their sifting through thousands of legal documents.
 The use of machine learning algorithms to identify outliers and fraudulent records
have been among the accounting firms’ favorite AI applications.

14. Data analytics in Internal Audit
 A key benefit of data analytics is that it offers an alternative to sampling.
Previously, internal auditors relied on analyzing a few sample transactions – out of
millions – to identify instances of non-compliance, revenue leak, potentially
fraudulent activity, and other problems.
 For instance, when using the sampling method for internal auditing, it’s easy to
miss the fact that an unusually large number of transactions were entered on a
weekend, although the entity being audited is only open for business during
weekdays.
 Such mistakes can occur because audit sampling does not examine 100 percent
of the items within a class of transactions.

15. Benford’s Law application in uncovering
frauds
 A great example of accountants leveraging data analytics to uncover fraud took place in 2014 when
Caseware Analytics client KPMG audited a call center. In this organization, hundreds of call center
operators could issue—without need for their manager’s approval—refunds of up to USD $50.
Within the span of several years, each operator issued more than 10,000 refunds. This presented
an ideal opportunity for theft, so KPMG used data analytics—Benford’s Law, specifically—to verify
the validity of the refunds. Benford’s Law expects that 30.1% of numbers in a list of financial
transactions will begin with ‘1’, 18% with ‘2’, and so on, with each successive digit predicted to
represent a progressively smaller proportion. When digits fall outside the expected pattern, it may
indicate fraud.
 Using the Benford’s Law functionality in their data analysis software, KPMG found that there was a
large spike in fours—the refunds did not follow Benford’s Law. As the accountants soon discovered,
several operators had been issuing refunds just below the $50 threshold to friends, families and
even themselves. Hundreds of thousands of dollars in fraudulent refunds had been processed and
may have gone undetected had a Benford’s analysis not been conducted on the refund data.

16. Big data
Big data refers to data sets that are too large or complex to be
dealt with by traditional data-processing software.
Data with many fields (rows) offer greater statistical power,
while data with higher complexity (more attributes or columns)
may lead to a higher false discovery rate.

17. Five V’s of Big data
Here are five V’s of big data
• Volume refers to the increasing size of the datasets that the financial industry must
process and analyze, which now measure in the petabytes (one petabyte equals 1 million
• Variety relates to the many different data sources that big data applications tap to create
analyses that more accurately represent a business’s financial operations today and in the
• Velocity refers to the high speed at which data is created, which requires distributed
processing techniques to collect and curate information in many different formats and
• Veracity describes the quality of the data being analyzed, especially whether the data is
consistent and certain. It also relates to the data’s ready availability and controllability.
• Value means that the data contributes in a meaningful way to the analysis rather than