Improving Audit Effectiveness / Efficiency by Leveraging Data Analytics12 May 2016

Arrow Audit DA Journey

Pre-2015•1 staff•10 analytics – AP, T&E

2015• Invested in enhanced skillsets & technology

•260+ analytics•Data visualization•Self service•Financial Close Toolkit

•Manual JEs analytics

2016•Dedicate more finance and accounting resources

•Statistical Modeling

•Behavioral and predictive analytics

Analytics Defined

Data presentation

Statistical models

Subject matter

knowledge

Technical expertise Discovery &

communication of meaningful

patterns

Audit Team

Common ChallengesAccording to a KPMG study, Audit departments are challenged by:• Disparate systems supporting different business models (e.g. T&E)• Establishing the definition of an “exception”, addressing “false positives” and

“false negatives”• Bridging the gap on what the audit population is (e.g. Benford’s)• Relying on intuition rather than data to support audit risk assessment (e.g.

defining a manual JE)

“Data analytics will likely be unsustainable without linkage to, or integration with, an audit work plan and the related audit objectives.”

Our Challenges1.Data acquisition – understanding and processing the data; need to

start with client-provided data as a base and then become more independent as you get comfortable with the data

2.Finding the right resources – BI, Auditor, Business Analyst?3.Bandwidth4.Technology needs5.Over-dependence by auditors – analytics are just the beginning of

the audit dialog

What we can do

• Understanding process is critical to provide valuable analysis• Right sizing the analytics for the size of the organization and

risks being assessed• Continuous improvement on analytics effectiveness

Audit Data Analytics Lifecycle

PlanningBrainstorming

Session

Communicate scope & objectives

Understand business context

Fieldwork

Knowledge sharing

Integrate DA documentation

Reporting

Integrate analytics

Feedback on use of analytics

Program Management

Establish development methodology (e.g. Agile)

Business process driven

Audit Data Analytics Key Elements

Access Data Acquisition

Tools

Understand business processes

Identify data sources

Establish data acquisition approach (direct connection, backup restoration, system canned reports, etc.

Evaluation of development tools

Excel

SQL

ACL

R/Python

Tableau / QlikSense

Understand what data is captured by the source system

Examine the data quality, integrity, and completeness

Design testing approach based on the data obtained

Data Source Project Management

Data Analytics to Start With

Accounting Analytics

When Benford Analysis Is or Is Not Likely UsefulWhen Benford Analysis is Likely Useful Examples

Sets of numbers that result form mathematical combination of numbers

AR (number sold *price), AP (number bought * price)

Transaction-level date – no need to sample Disbursement, sales, expenses

On large data sets – The more observations, the better Full year’s transactions

When Benford Analysis is Not Likely Useful Examples

Data set is comprised of assigned numbers Check numbers, invoice numbers, zip codes

Numbers that are influenced by human thoughts Prices set at psychological thresholds($1.99), ATM withdraws

Accounts with a build in minimum or maximum Set of assets that must meet a threshold to be recorded

Key AnalyticsThe Wharton School has published basic data analytical tests that can assist in re-focusing efforts in planning and executing audits in areas that could indicate incentives for management to manipulate results. • These tests fall into the following areas:

– Dupont Analysis– Revenue & Expense Recognition Management– Discretionary Accruals & Expenditures– Fraud Prediction – Beneish M-Score

DuPont Analysis

Revenue Recognition Red FlagsPotential red flags that identify potential changes in revenue recognition policies:• Unusual seasonally-adjusted quarterly (monthly) trends

• Growth in Revenue• Growth in Accounts Receivable

• Unusual trends in Ratios• Days Receivable and Accounts Receivable/Revenue

Then, we will try to find what happened• Do earnings management incentives exist?• Is there anything unusual in the Revenue Recognition policy

Year-over-Year Growth TrendsDue to seasonality need to compare to same quarter / month of the prior year

• YoY Revenue Growth• YoY Growth in AR

Benchmarks• Time-series: is growth unusual in one specific quarter for the firm?• Cross-sectional: is growth unusual for the industry in a given quarter?

Predictive Analytics

Examples

Fraud Prediction• Fraud prediction models examine companies that have been caught committing fraud to

model how they differ from companies not caught• Uses statistical techniques to chose a small set of ratios

Advantages– Specifically tailored to characteristics of fraud firms– Model parameters are fixed and don’t have to be re-estimated for each company

Disadvantages– Models based on companies that were caught with large frauds

M-Score is based on eight ratios– Higher M-Score means higher likelihood of manipulation– Uses comparisons between current year and prior year