risk assessment recommendation

© 2015 DineEquity, Inc. All rights reserved.

Restaurant Risk AssessmentRecommendation for Audit Team

David KayAugust 7, 2015

© 2015 DineEquity, Inc. All rights reserved.2

Outline

1. Objective2. Recommendation3. Overview4. Methodologies5. Summary6. Going Forward


Objective

• Use quantitative methods to analyze restaurant level data

• Develop a robust fraud risk assessment

• Identify the top 5th percentile of high risk restaurants for further investigation

• Create an interface for detailed analysis


Recommendation

See Excel File

• Scoring Table worksheet highlights high risk restaurants (top 5th percentile)

• Subjective decision regarding weights and value for “N/A” result (1 or 0)


Risk Assessment Overview1. Each restaurant scored from 0 to 1 for

each assessment2. 0 = lowest risk, 1 = highest risk3. Each assessment is assigned a weight4. A final score between 0 and 1 is

calculated5. Top 5% highest scores should be

further investigated


Methodologies

• Food Costs• Sales Trend and Pattern• Operations• Voids and Discounts

– Autocorrelation– Benford’s Law


Food Costs

• Compares average food costs (% of sales) to the system average

• Scoring based on number of standard deviations above average

• Higher food costs as a percentage of sales could indicate underreported sales or significant shrinkage


Food Costs (cont.)Ex: Avg. food cost % = 31%Standard deviation 2%


Food Costs (cont.)

• Outlier restaurants get an extra score to better differentiate them

• Based on the previous example (31% average food cost):

Food Cost Percentage Score Notes

X <= 0.325 0.0 Higher than avg.

0.325 < X <= 0.333 0.5 Much higher than avg.

0.333 < X 1.0 Very high


Food Costs (cont.)• Standard deviation of food cost

percentage used to detect fraudulent data

• A standard deviation of 0 could indicate food cost was “created” using a percentage (i.e. food cost was exactly 25% every quarter, so = 0)

• Scoring based on number of standard deviations below average


Food Costs (cont.)

• YoY trend in food costs (% of sales) is compared to system average

• Scoring based on number of standard deviations above average

• Food costs increasing more (or decreasing less) than system average may indicate underreported sales or significant shrinkage


P&L Sales Growth

• Compares restaurant quarterly sales growth (from franchisee P&L) to system average

• Sales decreasing more (or increasing less) than system average may indicate underreporting or significant shrinkage


P&L Sales Growth (cont.)

Example:• Given a 2% average compounded

growth rate

Sales Trend Score Notes

X <= -2% 1.0 Much worse than avg

-2% < X <= 2% = 0.5 – (X/0.08) Worse than avg

X > 2% 0.0 Above average


Sales Pattern

• Compares each restaurants current year and prior year sales pattern- Sales follow a yearly seasonal pattern- Deviations from this pattern indicate a

higher risk for fraud- Scoring based on correlation between

prior year and current year weekly sales (lower correlation = higher risk)


Operations

• Reflect personnel attitudes toward franchisor and franchisee, and/or franchisee attitude toward franchisor

• Lower scores, higher numbers of guest complaints and health violations may be indicative of greater risk of fraud


Operations (cont.)

• Comparison of restaurant versus system averages for OAR, OE, GLI, GRC and # of health inspection violations

• Scoring based on number of standard deviations below average (OAR, OE, GLI) or above average (health inspection violations, GRC)


Autocorrelation• The correlation of a time series value

with an earlier value (lagged correlation)• Positive autocorrelation = earlier value

and current value tend to both move in same direction

• Negative autocorrelation = earlier value and current value tend to move in opposite directions

• No autocorrelation = random series


Autocorrelation (cont.)System-wide, traffic follows a weekly pattern:


Autocorrelation (cont.)• Lag 7 and Lag 14 show the weekly pattern

(Mondays will be similar to other Mondays, Tuesdays will be similar to other Tuesdays, etc.)

• This same pattern is seen in over 90% of restaurants

• Voids/Discounts are naturally correlated with traffic, and thus follow a similar pattern


Autocorrelation (cont.)• However, voids/discounts should still be

somewhat random• Higher void/discount autocorrelation could

indicate non-random behavior (fraud, error, or unique promotions)

• Scoring is based on number of standard deviations above average

• Top 25% of restaurants get a second score equal to their percentile rank


Benford’s Law

• Frequency of first digit or first two digits of a large sample of numbers will display a counterintuitive distribution

• “Naturally occurring” (i.e. not fabricated or artificially modified) numbers (with no upper limit) should approximate a Benford curve


Benford’s Law (cont.)

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 880%

1%

2%

3%

4%

5%

1st 2 Digit Distribution


Benford’s Law (cont.)Benefits of testing data conformity toBenford’s Law:• Average difference from Benford easily

assesses data conformity• Manipulated numbers (either via fraud

or through non-random behavior such as promotions) will not conform

• Spikes at specific digits allows more precise investigation of transactions


Benford’s Law (cont.)

Using the mean absolute deviation (MAD) from Benford, scoring is based on the number of standard deviations above average


Summary• Robust quantitative assessments• User discretion regarding individual

test’s weight• User discretion regarding value in

place of “N/A”• Final weighted score used to assess

percentile rank• Can be used in conjunction with

existing risk assessment


Going Forward…• Final scores should be compared to those of an earlier study

• If the correlation of scores between the two studies is low, it means that there was virtually no relationship between the past scores and the current scores

• Low correlations could be because of:a) Different predictor weights in the current systemb) Addition of new predictors and/or the deletion of old predictorsc) Changed conditions

• Low correlation suggests the risk-scoring system needs to be regularly updated

risk assessment recommendation

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