Businesses have access to a variety of working capital financing solutions that enable them to operate efficiently. One of the short-term financing solutions that helps businesses manage their cash flows is invoice financing. This financing method allows businesses to access future cash flows for immediate usage. By using invoices as collateral to secure credit, it releases the capital that lenders have been holding. That is, the business borrows cash against approved, unpaid invoices, and the lenders pay the invoice value after deducting some margin and applicable fees. On receiving payment against these invoices, the borrower repays the advance amount, along with interest and charges, if any.
Leveraging AI in financing enhances the productivity, speed, and accuracy of operations, and significantly streamlines processes.
The role played by Artificial Intelligence (AI) starts at the very core of this process – the invoice. AI eases the task of overseeing the workflow and processing efficiency of invoices. By categorizing and assigning invoices using document classification and workflow management systems, AI assists in automating invoice processing.
AI also helps to improve invoice data quality and accuracy by extracting relevant information from various sources and formats, such as scanned images, PDFs, emails, and web pages. This extraction is done using optical character recognition (OCR) and natural language processing (NLP). AI can also help validate and verify the extracted data against predefined rules, policies, and standards with machine learning and rule-based systems. Additionally, it can correct data errors, such as typos, duplicates, missing fields, and inconsistencies using data cleansing and normalization techniques. AI can also augment the data with additional information such as tax rates, currency conversions, payment terms, and discounts using external sources and APIs.
AI Ascends
The complexity of invoice financing has made the use of AI particularly critical to the entire procedure. This technology enables the complete automation of invoice financing, from handling client billing and balancing client payments, among other associated tasks. By offering precise, timely insights, AI helps businesses improve process efficiency and enables the shortening of consumer payment cycles.
AI can analyze huge and complicated data sets, automate repetitive and manual operations, and provide insights and predictions using a variety of approaches, including machine learning, natural language processing, computer vision, and deep learning. Processing customer data swiftly and precisely is possible with AI-based technologies. Processes that are manual and paper-based may be expensive, time-consuming, and prone to errors.
Understanding the process
Integrating AI with the process of invoice financing begins with understanding the objectives for which this technology is being deployed. These goals could be many, such as faster customer payment cycles and improved customer service, among others. This then enables organizations to select the suitable AI-based software and integrate their existing systems with the new solution.
The new AI-based software can process clients’ data speedily and accurately. To enable this, organizations implement suitable data management strategies, including the setting up of data warehouses, and gathering data from various sources. Lastly, they evaluate the success of the new AI-based solution and identify areas for further improvement.
Risks Involved
Even when credit is secured by collateral like invoice finance, there are always some risks involved, primarily for the lenders. The first of these is the risk associated with credit, as it is the lenders’ job to collect the outstanding balance. These creditors and the associated companies will suffer losses if clients are unable to make the payment on time. So, there is a chance that invoice financing will not be repaid, particularly if it involves clients with bad credit.
Fraud is another risk that lenders in this domain could face. In certain cases, clients may offer invoices from fictitious organizations, or present forged invoices to the lenders as collateral. This could result in the lenders advancing funds against non-existent assets.
There are instances when lenders are unaware of the details and nuances unique to each business. Inadequate understanding of the borrowers’ operations may result in erroneous management of the lender’s receivables. The same risk applies in situations where there is inadequate knowledge of the borrowers’ financial standing.
Lastly, there is concentration risk that arises if lenders focus their business on a few clients. If these borrowers experience financial difficulties of any kind, the impact would affect the cash flow and profitability of the creditors.
MSMEs As a Critical Use Case
There is no argument that AI is very beneficial – necessary, even – for the invoice financing process, as it enables businesses to handle payments from clients in a speedy manner, shortens payment cycles, enhances customer support, and efficiently handles client data. But for businesses to effectively profit from the integration of AI, it is essential to develop an implementation strategy, integrate current systems, correctly manage customer data, and assess the effects of the software.
There is no death of opportunities in this space. In a report on MSME lending, investment banking firm Avendus Capital projected an incredible $530 billion credit shortfall in the MSME sector. According to the report, only 14% of India’s 64 million MSMEs have access to loans. The total demand for debt-based financing was roughly $1,544 billion, even though MSMEs’ overall financing demand was estimated to be around $1,955 billion. However, according to the research, 47% of the loan demand is not addressable since it originates from “enterprises which are not financially viable or prefer financing from informal sources.” As a result, $819 million in debt demand remained, of which only $289 billion was currently being met by official credit lenders such as public, private, and non-banking financial organizations (NBFCs).
Source: Avendus: MSME Lending Report. https://www.avendus.com/crypted_pdf_path/msme-lending-report-formattedvf-img-642a719b97ccc-.pdf
Private banks fulfilled the largest demand of 45%, followed by public banks with 43% and NBFCs with 12%, “even though the share of NBFCs has been increasing over the years. A large arena for lenders in this segment, of which $120 billion demand belongs to the small-ticket loan segment, with a ticket size of less than $1 million,” the report stated, is created by the remaining unmet demand of $530 billion.
The Bottomline
The adoption of Artificial Intelligence (AI) in invoice financing is a game-changer for businesses, particularly in streamlining operations and enhancing decision-making accuracy. By automating intricate processes like data extraction and invoice management, AI not only increases operational efficiency but also significantly reduces the likelihood of errors. This advancement is particularly crucial in the MSME sector, where efficient cash flow management is vital for growth and sustainability.
Despite its advantages, the application of AI in invoice financing comes with inherent risks such as credit, fraud, and operational understanding challenges. It is imperative for businesses to navigate these risks with strategic planning and robust risk management practices. In essence, while AI ushers in a new era of efficiency and accuracy in invoice financing, its successful integration hinges on balancing innovation with cautious risk assessment and management.
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Written by: AArtie Rau
