Firstsource: How Automation Helps Invoice Finance Lenders Detect and Stop Fraud Faster
At the start of a new year, we often see an increase in invoice finance fraud – when customers seek to borrow money by providing lenders with inaccurate information. (Not to be confused with invoice fraud which sees fraudsters send out fake, real-looking invoices to extract payment from businesses.)
The mechanisms of fraud differ for invoice factoring and invoice discounting. But at the heart of it are false, duplicated, or re-arranged invoices, fake credit memos, misappropriated receipts, and general ledger reconciliation challenges.
Invoice financing fraud becomes harder to spot during peak sales periods. Because the rise in trade legitimizes an increase in borrowing. And as lenders’ resources are strained due to high volumes of loan applications, fraud can creep in.
To spot and stop invoice finance fraud quickly, lenders must have the right tools. This can be done by using intelligent automation across the entire process, from lending to account management and checks.
Improve review processes
For invoice factoring, a manual review process means that a level of human error is unavoidable. The result is that red flags are missed while heavy due diligence processes take longer.
Automating exception handling can improve the review process. This allows automations (or bots) to perform background checks on an invoice to detect irregularities in the invoice number, size, or assigned credit scores. If anything unusual is detected, the relevant teams are notified and asked to investigate further. Here, human action only occurs when needed, saving employees time while reducing the margin for error.
Another challenge for invoice factoring companies comes from misappropriated receipts – when payments collected by the customer are not transferred to the lender. This may be missing until the customer’s debt extends the agreed terms or an old and unpaid invoice is reported.
Automation can be used to quickly recognize this fraud. Bots can monitor the customer’s banking history for anomalies. For example, a bot can identify when payments from named debtors are not returned to the lender on time.
Verification of invoices, new borrowers and their creditors is crucial for invoice factoring and discounting. A simple step such as verifying receipt of goods or services can become a bottleneck when manually searching for proof of delivery.
Automation makes verification quick and easy. Here, bots can automatically send emails to request documents, search for customers, and process shared data by extracting relevant information. They can flag deviations and unusual activity for the team to review. So people don’t need to do low-value activities like chasing and can focus on tasks that require detailed examinations.
Bots can also verify new borrowers more quickly and accurately. The appointment of the same administrators to the boards of borrowers and creditors is a telltale sign of fraud. Bots can analyze online data sources to spot any correlation between borrower and creditor administrators. It’s a smart way to stop rogue apps in their tracks.
Speeding up the general ledger reconciliation process
Effective general ledger reconciliation is essential for lenders who discount invoices – unfortunately, it is often slow and error-prone. First, it takes two weeks for clients to submit their books, then another ten days for lenders to manually reconcile them to their records. This means that any mismatches and potential fraud are detected almost a month later.
Automation can help reconciliation in two ways.
First, a workflow can be configured to automatically pull the customer ledger at the beginning of each month. Second, bots can examine entries in customer and lender ledgers for discrepancies and animalities. These are then given to people to investigate further. This ensures that fraud is spotted earlier in the month and action taken immediately.
Harness cutting-edge technology
Automation is a great springboard for doing more with data – it improves data quality. This data can then be used to gain deeper insights by deploying analytics or machine learning.
For example, analysis may reveal more patterns that signal suspicious activity. While machine learning can examine external and contextual data sources to determine tricky fraudulent transactions that go undetected due to their more elusive nature.
The best thing about automation is that it relies on the lenders existing IT infrastructure with minimal disruption to existing systems. There is no need to extract and replace applications or learn to use a new process. The bots are calibrated to work with lenders’ systems and processes, requiring minimal IT intervention.
Automation is not only a more efficient but also a non-intrusive way to spot and stop fraudulent activity. With this solution in place, lenders can keep fraud at bay even during the busiest times without straining resources by increasing expenses.
This article is written by Venugopala Dumpala, Head of Banking and Financial Services Practice at Firstsource in Review of Global Banking and Financial Activities.