Detecting fraud by numbers
Is your business inadvertently handing over the keys to its safe to unscrupulous employees or even total strangers? The Association of Certified Fraud Examiners (ACFE) estimates that 5 per cent of all revenue is lost to occupational fraud every year, while research by the Accounts Payable Network reports that 44 percent of organisations have experienced some level of fraud in the last three years.
A trinity of factors known as the “fraud triangle” can align to push a usually law-abiding citizen to do something unethical (in fact, it’s often a seemingly model employee in collusion with a supplier). All it takes is pressure, such as personal debt, opportunity, and the rationalisation that the fraudulent act is somehow justifiable or “no big deal” in the grand scheme of things.
Fraud isn’t 100 percent preventable – deception and opportunism are part of human nature. But there are steps you can take to deter and detect fraudulent attempts through continuous monitoring and surveillance. In fact, the ACFE found that companies could halve time to detection and reduce losses by two-thirds by adopting proactive fraud prevention measures rather than a reactive posture, such as an internal audit, accurate vendor master data or simply waiting for a tip-off.
Looking for clues
Fraudsters will always leave forensic clues behind, so P2P organisations are increasingly turning to data analysis to aid their detective work. For example, fraudsters often create fake invoices in rounded amounts (with no pennies or cents), companies can therefore rank vendors by those with a high percentage of rounded-value invoices. It’s also worth applying “fuzzy” matching to identify similar invoices as well as exact duplicates, such as those with values within a 5 percent range, those where one value is exactly double the other, or those with amounts that start with the same four digits.
Another tell-tale clue is invoice values that are just conveniently below management approval thresholds, so businesses can run searches to identify invoices that are <3 percent under the approval amount and flag them for investigation. Monitoring vendor invoice volumes can also alert companies to abnormal behaviour: a sharp uptick in invoices may be attributable to a legitimate increase in business, or an indication that a fraudster is getting more confident (or complacent) at stealing money.
Using fraud analytics to uncover suspicious activity
It can be labour-intensive to proactively carry out manual, spreadsheet-based analyses - that’s why organisations are adopting analytics solutions that use complex algorithms to do the heavy lifting of unearthing suspicious activity. For example, using a scoring system, it’s possible to identify abnormally large invoices for a given supplier by identifying the average and standard deviation of their values.
Another useful principle is Benford’s Law, also known as the ‘first-digit phenomenon’. This observes that certain numbers, such as 1 and 2, naturally occur more frequently as leading digits in data sets (about 30 percent and 17 percent of the time respectively) than those beginning with 8 and 9 (around 5 percent ). If you’re too young to remember logarithmic tables or skipped stats class, it basically means that the first digits in data sets like invoice values aren’t distributed uniformly, as you might imagine, but along a curve. So if the first digit of invoices is say, eight, 50 percent of the time rather than 5 percent , something may be amiss. While any anomalies aren’t conclusive proof of fraudulent activities – it is possible to produce a false positive – it can suggest that deeper digging is called for.
By using multiple variables as check points and more sophisticated analysis, it’s possible to score suppliers, either individually or on aggregate for risk indicators, in much the same way as the now-familiar practice of credit-scoring quantifies lending risk.
An ounce of prevention is worth a pound of cure
While all of this detective work after the fact can be highly effective, it’s worth remembering that automation can have a dramatic impact on internal and external fraud prevention. Invoice fraud thrives in an environment with poor visibility into the invoice process and weak payment controls. Adopting purchase-to-pay analytics can provide the visibility to help everyone in an organisation to manage spend, remove waste, cut costs and optimise use of working capital all whilst preventing fraud.
Matt Ingman is UKI Marketing Manager, Basware
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5 Minutes With: Jim Bureau, CEO Jaggaer
What is data analytics, and why is it important for organisations to utilise?
Data analytics is the process of collecting, cleansing, transforming and analysing an organisation’s information to identify trends and extract meaningful insights to solve problems.
The main benefit for procurement teams that adopt analytics is that they’re equipped to make faster, more proactive and effective decisions. Spend analysis and other advanced statistical analyses eliminate the guesswork and reactivity common with spreadsheets and other manual approaches and drive greater efficiency and value.
As procurement continues to play a central role in organisational success, adopting analytics is critical for improving operations, meeting and achieving key performance indicators, reducing staff burnout, gaining valuable market intelligence and protecting the bottom line.
How can organisations use procurement analytics to benefit their operations?
Teams can leverage data analytics to tangibly improve performance across all procurement activities - identifying new savings opportunities, getting a consolidated view of spend, understanding the right time for contract re-negotiations, and which suppliers to tap when prioritising and segmenting suppliers, assessing and addressing supply chain risk and more.
Procurement can ultimately create a more comprehensive sourcing process that invites more suppliers to the table and gets even more granular about cost drivers and other criteria.
"The main benefit for procurement teams that adopt analytics is that they’re equipped to make faster, more proactive and effective decisions"
Procurement analytics can provide critical insight for spend management, category management, supplier contracts and negotiations, strategic sourcing, spend forecasting and more. Unilever, for example, used actionable insight from spend analysis to optimise spending, sourcing, and contract negotiations for an especially unpredictable industry such as transport and logistics.
Whether a team needs to figure out ways to retain cash, further diversify its supply base, or deliver value on sustainability, innovation or diversity initiatives, analytics can help procurement deliver on organisational needs.
How is data analytics used in supply chain and procurement?
Data analytics encompasses descriptive, diagnostic, predictive and prescriptive data.
Descriptive shows what’s happened in the past, while diagnostic analytics surface answers to ‘why’ those previous events happened.
This clear view into procurement operations and trends lays the groundwork for predictive analytics, which forecasts future events, and prescriptive analytics, which recommends the best actions for teams to take based on those predictions.
Teams can leverage all four types of analytics to gain visibility across the supply chain and identify optimisation and value generating opportunities.
Take on-time delivery (OTD) as an example. Predictive analytics are identifying the probability of whether an order will be delivered on time even before its placed, based on previous events. Combined with recommendation engines that suggest improvement actions, the analytics enable teams to proactively mitigate risk of late deliveries, such as through spreading an order over a second or third source of supply.
Advanced analytics is a research and development focus for JAGGAER, and we expect procurement’s ability to leverage AI to become even stronger and more impactful.