There is a growing body of evidence to suggest all businesses should be aware of a growing fraud risk as the economy changes under the weight of several converging factors. PwC’s Global Economic Crime and Fraud Survey (2022) suggested that fraud levels have remained constant over the last year but collusion between internal and external actors was up to 26% of reported fraud versus 21% in 2020.

In November, The Economist claimed that stock market booms, of the sort that peaked in January, tend to engender fraud. In addition, they quoted one expert that said there “is an inverse relationship between interest rates and dishonesty”. A decade of ultra-low borrowing costs has encouraged companies to load up on cheap debt and debt can hide a lot of misdeeds. They are uncovered when credit dries up.

We have previously written about the rise of fraud in receivables finance and, as we see this threat increasing, wanted to share our thoughts again on how best to manage this risk.

Why behaviour monitoring is key to keeping receivables programmes secure

The digital age presents those capable of fraud with opportunities even though digital technology helps businesses in many different ways. This creates a conundrum for those organisations that want to take advantage of the benefits of technology and data, but understand that doing so increases risk.

Nowhere is this felt more keenly than finance, where deception is more common than you’d like to think and often hidden within the detail and complexity complex programmes. Fraud is not typically covered by trade finance insurance policies, so counter-parties to these transactions are highly exposed to fraudulent sellers, and it’s funders who bear most of the burden of the ever-present risk.

What can organisations, who provide and manage trade finance programmes, do to protect their investors and their investments?

Data is the answer here. When fraud lurks in the detail, understanding data helps to uncover these misdemeanours. By identifying the signs of potentially fraudulent behaviour, appropriate action can be taken to avoid financial consequences.

Hidden fraud risks

Most of the documented fraud in trade finance is based on common ruses that are well known, yet often hard to stop. Fresh air invoicing is one example: in which the seller sets up false client records and builds up a small trading history. Then, after lulling the funder and/or insurer into a false sense of security, issues larger and more frequent invoices before disappearing with the funds.

The organisations who run programmes are aware of this type of fraud and are using technology and obligor verification in solutions such as ours to take steps to eliminate it. But there is another type of fraud that is becoming increasingly more pervasive throughout trade finance, simply because traditional reporting methods are completely blind to it.

Undetectable changes

This type of fraud involves the subtle manipulation of individual data points to bypass funding eligibility criteria. All funding programmes control risk through setting criteria or eligibility rules: funders or insurers may, for instance, impose certain geographical or currency restrictions, concentration caps or minimum asset performance requirements as a condition for funding.

Once the programme has been set up with all the necessary criteria in place, funders and transaction counter-parties are generally unable to check every detail of every transaction against their original records. This is where the gaps that fraudsters exploit can open up.

In receivables finance for instance, minor details in the data record of invoices presented for funding are altered, ensuring that sellers receive funding for invoices, despite knowing that the invoice in question never met the agreed eligibility criteria.

In many cases, the perpetrators don’t even consider it fraud. In their eyes, since they intend it to be paid in the long run, no one ultimately loses. However, the practice is clearly fraudulent and introduces invisible layers of risk into the programme, unanticipated and uncovered by insurance in the case of invoice default, which is impossible to mitigate by traditional methods.

This is where data and behavioural analysis must be employed to help close gaps down.

Example fraud scenario:

Let’s take an example where a programme will fund invoices from US-based companies, in US dollars, with a maximum tenor of 90 days

On day one, a seller presents an invoice that conforms to these criteria. It passes all the checks and funding is released. Five days later, the seller changes a single data point on the invoice – they extend the tenor from 90 days to 120.

This could be an honest mistake while inputting data details into an accounting system. Indeed, where a small number of sporadic instances occur, this is most likely what it is. However, it could also be systematic manipulation of programme requirements:

  • The seller’s buyer always pays on or around the due date, and the seller believes they will continue to do so.
  • The seller needs a bit more funding, so they change a minor data point on the record, knowing that this will never be flagged in the reporting they provide.
  • As the bank eventually gets its money, they are none the wiser and nobody is the victim – or so it seems.

By manipulating the terms of funding in this way, the seller is passing risk on to their funders and committing fraud in doing so. The seller has obtained their funds illegally, and should the buyer default on payment (with no source of funds to settle the debt) it jeopardises the programme and it may fall to the funder to shoulder the loss. In this instance, these behaviours often happen recurrently with identifiable patterns of data alterations over time.

Protecting programmes with insight

With more sophisticated sellers able to flaunt programme restrictions, the threat of this hidden fraud in trade financing is nearly impossible to identify with the traditional, periodic reporting that still supports the bulk of transactions today. The only way to effectively mitigate the risk of this fraud is a systematic approach to the monitoring of data, using technology to provide you with the right insight and the tools to take fast, decisive action.

Getting away with manipulating data relies on victims being blind to the fraud. Data analysis opens visibility of your programme and raises red flags around suspicious data changes. The right analytical tools expose patterns of behaviour that indicate potential fraud. Historical data gathered during the evaluation phase of the programme and daily feeds of updated information can be analysed to build a picture of buyer and seller behaviours over time. All those ‘harmless’ little tweaks disguising frauds will be flagged as anomalies, allowing you to address them before they become real problems. Insight into behaviours both prior to funding and once the funding is in place helps to protect all parties to the transaction for the life of the programme.

How trade finance technology helps

  • Identify sellers and buyers within a working capital finance transaction against a database of more than 330 million global businesses
  • Gain visibility of (and manage limits on) geographical, sector and group concentrations
  • Systematically apply and monitor eligibility criteria for every new invoice and obligor
  • Monitor, and alert against, anomalous seller and obligor behaviours that may identify potentially fraudulent activity

    A sleuth’s guide to the coming wave of corporate fraud

    PwC’s Global Economic Crime and Fraud Survey 2022.
    Protecting the perimeter: A new frontier of platform fraud

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