Our latest blog discusses how a business’s historical and future transaction data is becoming more and more influential when assessing a business’s creditworthiness for funding.
Securing finance requires a business to provide proof of its creditworthiness: a measure of how likely it will be able to pay for its finance obligations on time. Traditionally their fate has been in the hands of a three-digit credit score derived from a credit report – if the score is above the finance provider’s threshold, the funding is approved; if the score is below, the application is rejected.
Most people assume that this is the best criteria for making such a major decision. But have you ever considered the information that’s evaluated to determine this all-important score – a score that could pose an existential threat to a business if it’s not high enough? Credit reports and the resulting score typically focus on a borrower’s current financial information, meaning finance decisions end up being based on factors like payment history and outstanding finance with other providers – the higher the number of transaction histories, the higher the score.
It would seem logical to also base the likelihood of loan repayments/advances being fully paid on the customer’s suitability to pay in the future. But under this narrow system, it is the number of transactions and payments made in the past that takes precedence. Relying solely on a credit score can also create ethical challenges if a finance provider overlooks its obligation to identify and verify its clients in a bid to prevent financial crime.
The tide is turning amid advances in big data, digital, and analytics that generate new opportunities for finance providers to enhance their credit-decisioning models. This has prompted the rise of transaction-based financing: a shift from decisions based solely on financial statements to deciding on creditworthiness using a more comprehensive approach.
Creditworthiness decisions are now being based on real-time assessment of the current and – crucially – the future business situation, including sales, inventory, and reputation. Financial statements, business plans, and guarantees are being augmented with information that can horizon scan creditworthiness, like purchase and payment data, customer evaluations, and products handled.
Within this brave new world of lending an alternative funding option has become available to small businesses (SMEs) that can drive growth by unlocking access to funds quickly and cost-effectively: revenue-based financing. This allows SMEs to access funding based on their overall business revenue – and the benefits over traditional forms of finance like loans are compelling:
The Liberis revenue-based finance model is driven by an intelligent data engine that allows us to automatically forecast business transaction revenues and make a personalised and preapproved offer. In contrast, a credit reference agency must go through the rigmarole of manually pulling a report on the business and its repayment history, before disseminating that information to the lender who makes decisions using physical data – prolonging the lending process.
Automated credit decisions are powered by risk models that assess variables from credit reports and leverage merchant risk model information, such as trends, stability, and seasonality. By linking a business’s credit risk level with its revenue performance, we can make informed decisions before communicating them early in the merchant’s online journey.
But it’s not just about providing an expeditious service; our commitment to act in a business's best interests by ensuring the right deal at the appropriate cost, clear terms and conditions, and transparent pricing underpin our responsible finance philosophy. This extends to ensuring the process is not a conduit for nefarious activities, such as money laundering and fraud.
To achieve this, we notify them that we are obliged to conduct certain checks during the onboarding process to ensure we are funding legitimate businesses:
In our commitment to provide customers with a seamless onboarding journey and frictionless experience, we use global partner, ComplyAdvantage to ensure a global systematised journey.
Further layers to our AML safeguarding obligations include politically exposed person (PEP), sanctions, and adverse media screening of the business and the applicant, which we conduct at the point of onboarding. If we identify any concerns with the business or the owners of the business, we will either apply enhanced checks or ask for relevant documentation to verify the business or individuals. This is not a one-time only process; at the point of renewal, we also monitor previous performance and conduct checks in case circumstances have changed that might influence their suitability.
The Future of Responsible Funding: A Blend of Old and New
Modern lending models do not aim to scrap the historical payment data that underpin credit scores; they harness this information more quickly and augment it with forward-looking insights into a borrower’s suitability to pay a debt obligation in the future – and present their findings and requirements transparently. Crucially, they also value – and automate – a series of legal obligations that have been around for many years that are designed to prevent financial crime.
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