data mining and forensic audit

DATA MINING&

FORENSIC AUDITBy

Dhruv Seth

[email protected] | www.sethspro.com

CONTENT• Data Mining

• Methods of doing

• Difference with standard auditing

• Benefits and Risks

• Patterns in data

• Utilisation in different audits

• Forensic Audit• What is a fraud

• Profile of a fraudster

• Tools available in excel

• Theorems

A PROBLEM…

•A large retail chain doing substantially well

had

•Dismal diaper sale ; Excellent Beer sale

SOLUTION

Place them together !

WHICH IS SUSPICIOUS ?

• User 1: Login → Click on Product #8473 → Click

on Product #157 → Click on Product #102 →

Complete Purchase

• User 2: Failed Login → Request Password →

Direct Link to Product #821 → Change Shipping

Address →Complete Purchase

Data Mining

Computer Expertise

≠

WHAT IS DATA MINING

Data

Information

IDENTIFYING SUSPICIOUS TRANSACTIONS

Computer Behavioral Smartphone Analytics

Mouse Dynamics Screen Pressure

Typing Speed Angle of usage of phone

Previous Navigation Habits

Movement across screen

Entry & Exit points on website

Heart Rate

DATA MINING - VALUE ADDITION

What was my total revenue in the last five years?

TO

What were sales in UP last March? Drill down to Kanpur

TO

What’s likely to happen to Kanpur sales next month? Why?

DATA MINING - METHODS

•Association

•Sequence or path analysis

•Classification

•Clustering

•Prediction

DATA MINING - TECHNIQUES

•Artificial neural networks

•Decision trees

•The nearest neighbour method

DATA MINING V. REGULAR AUDIT

Labor Verification

Regular Audit Data Mining

1. Contracted rate = Billing rate

1. Contracted rate = Billing rate

2. The billing is relevant tothe audit period.

2. Employee Pay grade wise payment

3. Statutory Compliances 3. Mapping resignation to Last Pay

4. Mapping computer / biometric logins after resignation / termination

5. Overtime Analysis to determinea.) Regular Overtimeb.) Employees who worked 100 hrs

6. Those not availing leaves

DATA MINING - STEPS•Business Understanding

•Data Understanding

•Data Preparation

•Data Modelling

•Evaluation

•Deployment

DATA MINING – WHY INTEGRATE

•Transaction Volume

•Mitigate Inherent Risk

•Value addition to the client

•Cost Effective

DATA MINING – BENEFITS

•Remove Sampling risk – 100% coverage

•Decrease in Audit costs

•Provide Real time audit opinions

•Establish Completeness and accuracy

DATA MINING – SOFTWARE TYPES

•Generalized Software

•Specialized Software

DATA MINING – SOFTWARE TYPESCharacteristics Generalised Specialised

Batch Processing No Yes

Support entire audit procedures No Yes

User friendly Yes No

Require technical skill No Yes

Automated No Yes

Capable of learning No Yes

Cost Lower Higher

DATA MINING – RISKS

•First year costs might be higher

•Strong understanding of operations

•Availability of data in desired format

•Risk of Control totals

DATA MINING – PATTERNS

•Numeric Patterns

•Time Patterns

•Name Patterns

•Geographical Patterns

•Relationship Patterns

•Textual Patterns

• Purchases

• Vendors and accounts payable

• Employees and payroll

• Expense reimbursement

• Credit Card utilisation

• Sales & Debtors

• Inventory

• Commission Payouts

DATA MINING – INTERNAL AUDITS

PURCHASES

•Round number transactions

•Duplicate transactions

• Same, Same, Different Test

•Above average payments

•Transactions exceeding PO quantity

• Sequential Invoice numbers

•Too many invoices beginning with “9”


CREDITORS

•Those with high percentage of returns

•Those with rapid increasing purchases

• Small denomination but quick frequency

• SOD for vendor approver and purchaser


PAYMENT TREND ANALYSIS

By the day of week

By the day of Month


DATA MINING – EXPENSES

VENDORS MASTER

• Analysis of Vendors master for creation date

• Identifying regular prompt vendor payment

• Cross reference vendors to employees

• Same, Same and Different test


EMPLOYEES AND PAYROLL

• Regularly working overtime

• Not taking leaves

• Satisfied with unjustified salary deduction

• Segregating employees with salary in cash

• Biometric analysis – First to enter / last to leave


TRAVEL EXPENSES

• Identify weekend or holiday travel

• Search for same or similar claims

• Identify costs outside of policy or costly late bookings

• Identify conveyance claim made for the same time periodas car rental or other transportation

• Compare mileage claims to distances reported

• Instances where employee has refunded a first classticket for an economy, but not reimbursed the balanceback to the company.


SALES & DEBTORS

• Comparing Invoice to Shipping

• Conversely comparing Shipping to Invoice

• Preference in sale to a particular customer

• Same, Same, Different test to sale price

• Debtors

• Lapping

• Old outstanding invoices


INVENTORY

• Determining slow moving inventory

• Determining quick moving inventory

• Purchasing frequency of a particular product

• Mapping stock valuation to last sale price


• Transactions a customer does before shifting? (toprevent attrition)

• Profile of an ATM customer and what type ofproducts is he likely to buy? (to cross sell)

• Patterns in credit transactions lead to fraud? (todetect and deter fraud)

• Traits of a high-risk borrower? (to preventdefaults, bad loans, and improve screening)

DATA MINING – BANKS

• Duplicate Customer id

• DP Limit = Limit = Outstanding

• Comparing Unsecured and secured within scheme

• Rate of Interest being applied

• Last Credit amount and Date

• Same PAN – Different Customer id

• Last Stock statement summary

DATA MINING – BANKS

•Rubbing Nose

•Frequent blinking

•Moving or Tapping feet

•Crossing Arms

•Clearing throat

•Pinched eyebrows

•Smirk

DATA MINING – BEHAVIOR

FORENSIC AUDIT

REPORT TO THE NATION

• Each organization loses 5% of their REVENUE to fraud

• Asset Misappropriation is the biggest factor

• Fraud are generally NOT discovered for 18 months

• Higher the fraud perpetrator BIGGER the fraud

• 58% organizations NEVER recovered anything

FRAUD DETECTION

BANK FRAUDS – 9 MONTHS FY 2014-15

Name Number of Cases Amount

PNB 123 2036,00,00,000

CBI 174 1736,00,00,000

SBI 474 1327,00,00,000

Syndicate 114 749,00,00,000

OBC 86 719,00,00,000

BOB --- 597,00,00,000

IDBI --- 507,00,00,000

UCO --- 424,00,00,000

United Bank --- 376,00,00,000

TOTAL 7542,00,00,000

• A false representation of a matter of fact

• whether by words or by conduct,

• by false or misleading allegations, or

• By concealment of what should have beendisclosed

• that deceives and is intended to deceive another

• so that the individual will act upon it to her or hislegal injury.

WHAT IS FRAUD ?

FRAUD TRIANGLE

WHAT IS FORENSIC AUDIT

•The use of accounting skills;

•To investigate frauds / embezzlement and

•To analyze financial information

•For use in legal proceedings

FORENSIC VIS-À-VIS STATUTORY

Forensic StatutoryVery focused and micro approach Macro approach with wide coverage

Examines Reliability of documentation Relies on Documentary evidences

Not compulsory Regulatory compliance

Establishing existence of fraud Ensuring True and fair view

Determining the quantum of loss Verifying correct representations

Gathering evidences Evaluating Internal Controls

GHOST EMPLOYEES

NEED FOR LEARNING THE TRAITS

Why frauds go unnoticed during stat audit -

• extremely intelligent

• Conversant with internal systems

• Technology savvy

• Aware of stale audit procedures

FRAUDSTERS PROFILE

• Flamboyant lifestyle

• Very aggressive in his approach / targets

• Over protectiveness of data / documents

• Being the first one in and last one out

• Unusual close association with vendor / customers

FRAUDSTERS PROFILE

FORENSIC AUDITOR

Forensic

Accountant

Law

Accounting

Criminology

Investigative

Auditing

Computer

Science

TRAITS OF A FORENSIC AUDITOR

•Think out of the box

•Distrust the obvious

•Develop cognitive dissonance

•Test of absurdity

TEST OF ABSURDITY

Think of events which may be possible but

not probable.

TOOLS AVAILABLE IN EXCEL

•Analyze round number transactions

•Duplicate detection

• Same, Same and different tests

•Above average payments to vendors

TOOLS AVAILABLE IN EXCEL

•Gap detection

•Automated sampling

•MATCH function

•Employee – Vendor match

SPECIAL MENTION – TIME & SPACE

•Establish transactions in quick successions

which take a substantial time in happening

• Storage in excess of the possible space

SPECIAL MENTION – RSF

•Ratio of Largest number to the second

largest number in the set

RSF = Largest Number / 2nd Largest

•RSF greater than 10 highlights probability of

fraud / error

SPECIAL MENTION – RSF

•Types of errors / frauds it can unearth

• Data Entry mistakes

• Fat Finger errors

• Wrong coding with masters

• Capital Asset written off in expense

• Excess payments in payroll

SPECIAL MENTION – BENFORD’SLAW

•Formulated by Simon Newcomb in 1881 ;

further researched by Frank Benford in 1938

•U.S. accepts Benford’s law as an evidence

• Statistical tool which can be applied to

normal audits also to automate samples

SPECIAL MENTION – BENFORD’SLAW

SPECIAL MENTION – M-SCORE

•Theory propounded by Prof. Beneish

• Stipulates the accuracy of financialstatements based on certain ratios

•Ratios such as

• Sales to receivables and Sales Growth Index

• Gross margin Index

• Asset Quality Index

• Depreciation Index

SPECIAL MENTION – M-SCORE

•Financial statements score >-2.22 is

considered as fudging

• Statistically proven to have 76% accuracy

•Model being adopted by Income Tax

Department for CASS

EXCEL LIMITATIONS

•Absence of Log

•Not admissible in court

• Involves slight complexity in applying

•Data size limitation / Instability

•Risk of Hidden data

THANK YOU !

By

Dhruv Seth

[email protected] | www.sethspro.com

data mining and forensic audit

Data & Analytics