Procurement and Fraud Detection Quiz

Procurement and Fraud Detection Quiz

Professional Development

9 Qs

quiz-placeholder

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Procurement and Fraud Detection Quiz

Procurement and Fraud Detection Quiz

Assessment

Quiz

Business

Professional Development

Hard

Created by

Gatot Soepriyanto

FREE Resource

9 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You analyze 9 months of procurement and see a spike of invoices at Rp9,900,000—just under a Rp10,000,000 approval limit—concentrated in two requesters and one vendor. What is the most effective next step?

Run Benford’s Law on all invoice amounts

Pull approval logs and test for split-purchases within 7 days by the same requester/vendor, then escalate exceptions for document review

Interview the vendor first

Recalculate budget variances by cost center

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You plan a Benford test on 12,000 invoice amounts across three vendors. Two vendors use rate cards with fixed prices; one is market-priced. Which is the best approach?

Benford on all 12,000 amounts

Benford only on the market-priced vendor; exclude fixed-price vendors

Benford on invoice IDs to increase sample size

Skip Benford and do round-number testing only

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Your AP data shows many payments of Rp4,995,000 and Rp5,005,000 to the same vendor within 48 hours. Which analytic best surfaces the issue?

Linear regression of amount vs. date

Fuzzy matching with vendor+date window and ±Rp10,000 tolerance on amounts

Topic modeling on invoice descriptions

Chi-square on last-digit distribution

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Payroll lists 2,412 active employees; building access logs show only ~2,280 unique badge IDs in the last 60 days. HR says 150 are field-based with no badges. What should you test first?

Benford on net pay

One-to-many bank account mapping to detect shared accounts across “employees”

Interview supervisors of all non-badged staff

Compare PTO balances to payroll amounts

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Quarter-end shows a surge in “Equipment Improvements” (CapEx). Vouching finds routine repairs capitalized. Which statement is most accurate?

Risk is understatement of assets; test for impairment

Risk is overstatement of assets and income; test additions against capitalization criteria and useful life

No issue if items exceed the monetary threshold

Focus on revenue cut-off, not expenses

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A D2C firm books revenue on shipment; returns window is 10 days. Quarter-end spikes in shipments; post-quarter returns also spike. Best targeted test?

First-digit Benford on invoice amounts

Cut-off testing: shipments near quarter-end vs. proof of delivery + adequacy of returns reserve

Vendor master file review

Duplicate invoice check

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

AP receives a WhatsApp voice note sounding like the CFO ordering a Rp12B urgent transfer to a new beneficiary. Most appropriate immediate action?

Process the payment but keep the receipt as evidence

Reply to the same chat asking for written confirmation

Halt the payment, perform out-of-band callback using a whitelisted number, and preserve the message for IR

Inform the vendor to expect a delay

8.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

EDR alerts show encryption on three servers and outbound traffic to an unknown IP over the last 36 hours. A threat group claims to have “1.5 TB of customer data.” What should incident response prioritize first?

Public statement assuring no data was stolen

Network containment (isolate affected hosts/segments), preserve volatile/log data, then start scoping exfiltration

Immediate full system wipe and rebuild

Notify all customers before understanding scope

9.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You have 1,000 procurement emails + 3,500 invoice descriptions. You suspect collusion (e.g., “split”, “urgent approval”, “use template 3”). Best low-lift analytic combo?

Benford on word counts + PCA

Red-flag dictionary → term frequencies with Z/MAD outlier ranking + co-occurrence network of terms/senders

Forecasting email volume with ARIMA

Sentiment analysis only