RP Sanjiv Goenka Group

How lenders can work smarter to spot invoice factoring and discounting fraud

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Invoice finance and asset-based lending continue to recover following the pandemic. According to UK Finance Business Finance Review, an 11 per cent rise in lending in Q3 of 2021 was followed by further five per cent rise in Q4 – reaching over £17 billion.

Yet lenders need to be cautious – as borrowing increases so does the risk of fraud. For invoice finance companies, fraud becomes more difficult to spot during busy trade periods for two reasons. First, an expected rise in sales legitimises apparent increase in borrowing. Second, reliance on manual processes means fraud can slip through the cracks as resources get stretched.

While invoice factoring and discounting operate differently, both are impacted by the same challenges – high volumes of checks required across numerous documents and disparate systems. Both can be improved with the right solutions. Here, we’ll look at five ways Intelligent Automation can help lenders work smarter (not harder) to spot fraud across the entire process.

1. Addressing the review process

For Client Administrators, working on invoice factoring manual data review is highly time-consuming (and possibly the least enjoyable part of processing). When the data across numerous invoices, credit notes and receipts is reviewed to ensure everything adds up, a degree of human error is inevitable even among the best-trained teams.

Automation is an efficient way to accelerate reviews and improve accuracy across the entire process. Automations (also known as bots) can process vast amounts of data quickly to spot inconsistencies across invoice sizes, numbers and assigned credit notes. Once a mismatch is detected, it is automatically flagged for Client Administrators to investigate, enabling them to spot if a fraud attempt has been made. This saves employee time as they only review flagged inconsistencies, and the potential for error decreases.

2. Better visibility

Diverted receipts present yet another challenge for invoice factoring – this happens  when payments collected by the client are not transferred to the lender. On large, complex accounts diverted receipts can go unnoticed until the client’s debt exceeds agreed terms, or an outstanding invoice is flagged.

Automation helps spot diverted receipts much sooner. Bots can monitor clients’ books and banking history for deviations, automatically detecting when scheduled payments are missed.

3. Improved verification

Verification is most crucial and a highly strenuous part of invoice factoring and discounting. Verifying borrowers, creditors and invoices requires proof. Manually gathering supporting documents and information can become a resource drain and lead to bottlenecks. Automation simplifies and expedites this process.

Automating parts of communications such as email requests for supporting documents and follow up can free up resources. Bots can also process shared documents scanning for key data.

In this scenario cases are flagged for Client Administrators to process only when missing documents are not provided or problems with data are identified. This removes  laborious chasing and frees up employee time for more value-added activities.

Automation can also be used to verify borrowers. Bots can scan public borrower and creditor company data to identify if same names feature on boards of directors – which can be a cause for concern. This is a smart and simple way to halt fraudulent applications.

4. Faster ledger reconciliation

For invoice discounting, monthly ledger reconciliation can be a slow, inefficient and error prone. It can take clients up to two weeks to submit end-of-month ledgers. Then the lender might require up to ten days to manually reconcile this with their ledgers. In this instance if fraud occurred it would go unnoticed for almost a month. Automation speeds up ledger reconciliation.

A workflow can be created to automatically pull a client’s ledger at the start of every month. Bots can then compare entries across clients’ and lender’s ledgers to spot any mismatch. When ledger entries don’t align, this noted automatically and handed over to the relevant teams to investigate. Automation has been shown to reduce ledger reconciliation from 20 to just three days.

5. Technology is key

Automating processes improves data quality as bots record data with higher accuracy. Quality data makes it possible to apply other advanced technologies such as analytics and machine learning. Running analytics across all activities can uncover unique patterns that are not otherwise easily identifiable. While machine learning can look at external and contextual data sources to gain more insights into fraudulent activity.

No disruption to existing IT systems

Automation sits on top of lender’s IT infrastructure without disrupting existing systems. There is no need to replace applications or to extensively train Client Administrators on how to use entirely new systems. Because it is non-disruptive automation and can be implemented without serious involvement from lender’s IT teams.

It is a sad reality that fraud attempts do and will continue to occur, but lenders can be better prepared for these. Adopting Intelligent Automation ensures fraud attempts get spotted earlier, minimizing losses down the line while allowing Client Administrators to focus on value-adding activities that secure greater revenue for the business.

This article is written by Venugopala Dumpala, Practice Head Banking & Financial Services at Firstsource in Finance Derivative.

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