Merging forensics w data analytics

Merging Forensics with Data Analytics Monday, September 28, 2015

Time

Josh Shilts, CPA/CFF/CGMA, CFE

Presentation will be available at:

www.misti.com/downloadDownload password is available in your Show Guide

MIS Training Institute Holdings, Inc. Slide 3©

Big Data Phenomenon and its’ Effect on Organizations & Audit Departments

Data Analytic Program Landscape Forensics + Data Analytics = Mitigated Risk Continuous Auditing Evolution Data Analytics Future: Organization and Audit

Department Affects

Key Points


The BIG Data Phenomenon

“Information is the oil of the 21st century, and analytics is the combustion engine.”

- Peter Sondergaard, Gartner Research



“Ninety percent of the world’s data has been created in the last two years… The ultimate question is really what insight and value can we draw from that data.”

- George Lee, Chief Information Officer, Investment Banking Division, Goldman Sachs, Sept. 2014



“Big data is not about the data.”

- Gary King, Harvard University, making the point that while data is plentiful and easy to collect, the real value is in the analytics

“Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”

- Atul Butte, Stanford School of Medicine



“Figures often beguile me, particularly when I have the arranging of them myself…

…there are three kinds of lies: lies, damned lies, and statistics.”

- Mark Twain, 1906


Employee & Customer Behavior Supply Chain Efficiency Faster Decision Making Push to understand Data Structure and Advanced

Analytical Tools Identify Data Analytics Talent Embedding Big Data

This creates a lot of unanswered questions…How is big data going to improve our performance as a business? What will the company focus on? etc…

Effect on Organizations


“Trickle Down” to Audit Departments Employee & Customer Behavior Supply Chain Efficiency Faster Decision Making Push to understand Data Structure

and Advanced Analytical Tools Identify Data Analytics Talent Embedding Big Data

This creates a lot of unanswered questions…How is big data going to improve our performance or the audit plan? What will the audit plan focus on? etc…


Use of “Scripts” defined by software companies, large internal audit consulting shops and well known professional associations (i.e. IIA, ACFE)

Excel, ACL and IDEA used to identify duplicates, quantitative outliers (e.g. aberrations, deviations)

No defined method for script implementation No use of existing resources Audit Management “forcing” use of analytics with no

other reason to do so than meet professional association guidance

Current Data Analytic Landscapes


“If you want to give yourself a fair chance to succeed, never expect too much too soon”

– Po Bronson

Data Analytic Program Landscape


- Don’t put the cart before the horse…


To succeed you must utilize existing knowledge as well as existing tools to build the “base” of your program…

Magnitude

Major >$50M 5

Substantial

>$25M 4

Moderate >$ 10M 3

Minor >$1M 2

Insignificant <$1M 1

Define how Financial Impact is measured (i.e. Net Income, Revenues, etc.)

1 2 3 4 5

Remote Unlikely Possible Likely Almost Certain

Likelihood

12

11

3

10

4

6

5

14

13

15

9

8

1

7


Before even the discussion of a analytic tool consider using existing information…

Employee Turnover %

Income Statements

SEC Enforcement Actions

Hotline Statistics

User Access Reports

Customer Surveys

Inventory Ratios


Forensics + Data Analytics

Employee Turnover %Income Statements

SEC Enforcement Actions

Hotline Statistics

User Access Reports

Customer Surveys

Inventory Ratios

Magnitude

Major >$50M 5

Substantial

>$25M 4

Moderate >$ 10M 3

Minor >$1M 2

Insignificant <$1M 1

Define how Financial Impact is measured (i.e. Net Income, Revenues, etc.)

1 2 3 4 5Remote Unlikely Possible Likely Almost

Certain

Likelihood

9


Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, or otherwise support a claim of financial fraud. The main steps in forensic analytics are (a) data collection, (b) data preparation, (c) data analysis, and (d) reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.

Wikipedia

How Does Forensics Relate


Equals Mitigated RiskContinuous audit, much like the disciplines currently employed within forensic audit, focuses on the prevalence of a risk and/or the effectiveness of a control…

…with automated, frequent analyses of data, you will be able to perform control and risk assessments in real time or near real time. Thus, giving you the ability to analyze key systems for anomalies at the transaction level.


Representative Examples

1. Invalid Tax ID – generate a report to identify all expense payments sorted by Tax ID. Investigate all payments wherein there is no Tax ID, Tax ID is all 9s, or contains alpha characters.

2. Duplicate Payments – Captures payments either same $ paid to the same payee and/or check #

3. PO Box Payments – Captures all payments made on an insurance claims that included a PO box as the address.

The majority of “canned” scripts focus on cash misuse and/or larceny fraud schemes…


Representative ExamplesHowever; focus should be on “real” fraud risks and not utilizing resources to complete “common” tests…

Scenario Fraud Risk Potential TestCompany has a significant # of employees who travel.

Abuse of company credit card.

Identify how many charges per employee are incurred in their hometown.

Technology company incurs high percentage of employee turnover below the manager level.

Inappropriate access with non-existent employee IDs

Run a report to compare employee leaves vs. change management activity.

Insurance company who prides itself on making timely payments to customers.

Payments are made without appropriate approvals or support.

Run a report to identify claims made within 5 days of endorsement to identify if payment is made on an excluded loss.


Forensics has long been a discipline that focuses on a specific risk or situation and utilizes tools and knowledge to pinpoint its cause…

My Point

…data analytics builds on the “Forensic Objective” by allowing professionals to decipher mounds of data and information and focus on more than just fraud within a set of data.


Continuous Audit Process Implementation Graph

Continuous Audit Process

Implementation Steps

1Establish Priority Areas

6Report Results

5Manage

Results & Follow Up

4Configure

Parameters & Execute

3Determine Process

Frequency

2Identify Audit

Rules

7Assess

Emerging Risk & Add to Register


Continuous Auditing Evolution


CONTINUOUS AUDIT RISK/CONTROL

ASSESSMENT

FACT FIND

DATA ANALYTIC INITIATION

CONTINUOUS AUDIT

ACTIVITY PLAN

DATA ANALYTIC STORAGE

REPORTING

Qn REVIEW & ASSESSMENT

IntakeAssess

Emerging

Plan Changes

Resource Allocation

Process & System Reviews

Other Activity

Integration

1st & 2nd Lines

Monitoring

Ratios / Trends

Script Initiation

Evaluation

Periodic

Process Mngt.

Tools

Quarterly

Annual

Continuous Audit Process

Implementation Steps

1Establish Priority Areas

6Report Results

5Manage

Results & Follow Up

4Configure

Parameters & Execute

3Determine Process

Frequency

2Identify Audit

Rules

7Assess

Emerging Risk & Add to RegisterTh

e Com

plex F

uture


The Future

Improve Existing Products and Services Improve Internal Processes Building New Products or Services Transforming Business Models

The business of Forensics by improving the determination of priorities and picking the right angle to review.


Big Data is in fact a phenomenon that has a lot of theory behind it. Problem is most of it is theory…

Some problems the theorists forgot to think about is: Data Integrity Core Value and Missions

Continuous Control Monitoring is complex and requires the appropriate number of resources and allocated time.

You need very creative individuals to implement.

The Realities


Don’t let the Big Data phenomenon distract you from your core values and mission

Focus on “easy” wins by using ‘canned’ scripts

Build slowly: know what works for you and don’t get caught up in an “arms race”

Audit the Data!!!

Summary

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Merging forensics w data analytics