In the business lending industry, ‘technology’ is all about information and using it for better decision making. Such as faster loan approvals, reduced likelihood of bad debt and enhanced return on capital employed.
Risk assessment processes – as well as many others – are deeply embedded into lenders’ operations. They may not have changed, but the data available certainly has.
Risk assessments that draw heavily on historical indicators, such as previous trading performance, have become less useful since the start of the pandemic: some businesses have traded throughout, some were put on ice but are now bouncing back, and some continued trading thanks to government-backed loans but won’t return to their former glory.
To manage risk effectively in the post pandemic era, lenders need to consider different sources of data and the insight they get from it – particularly near-term and forward-looking indicators such as real-time cashflow positions, which show what companies are doing right now.
This is why products like Asset Based Loans, Invoice Finance and Receivables Finance have become even more popular recently.
The good news?
Far more data is available, and much of it is real-time. This means risk assessments can be vastly more sophisticated, enabling lending in a much wider set of circumstances- quickly.
Below is just a snapshot of some of the data lenders have access to:
Business lending operations of the future will be built on composable ecosystems connected via APIs. This leads to seamless data sharing and straight-through processing.
Technology isn’t going to replace risk managers anytime soon. By automating the collection of newer, more context-rich data, technology can deliver decisions where there is an obvious ‘Yes’ or ‘No’ answer. This frees a risk manager to focus their time on more nuanced deals or to think and support the businesses they serve in a more strategic matter.
Risk models within a modern lending platform are built for the digital age, based on new datasets and a richer context around the borrower’s situation.
Risk managers can now process a much wider set of circumstances, and make the shift from “Who am I lending to?” to “What am I lending against?”.
Here’s an example of what this process could look like:
In addition to this, the automation that is now available to lenders through lending orchestration platforms, such as Trade Ledger, means the most up-to-date information is being used to underwrite and process loans. Massively reducing the number of manual errors.
Lenders who have partnered with Trade Ledger have seen a 90% reduction in errors, on average.
The added bonus of a data-driven business model is that all decisions, events and webhooks are automatically tracked - a level of due diligence that’ll reassure even the strictest regulators.
Redesigning internal processes around the capabilities of lending technology is the cornerstone of a powerful change that brings risk models up to speed with what both lenders and borrowers need - all the while keeping regulators happy.
If the majority of lending applications are still being rejected, lenders need to try harder. The data exists for lenders to understand the nuance of each business, and the type of working capital they need. Lenders who do will win loyalty from business owners.
James Binns, Global Head of Trade and Working Capital at Barclays Bank, says that the big banks are now willing to put their transaction engines and platforms onto externally hosted cloud lending platforms, with API gateways off them:
Once you’ve got the connectivity, then you can start adding different data feeds over a period of time, layering on the data you need to make more decisions.