How does the engine’s risk profiling work?

FloFunder operates multiple levels of risk validation categories with a scale of ratings within each category.

From these a composite risk analysis is performed and a weighted score is allocated to each Seller, each Debtor, the historical trading relationship between Seller and Debtor which classifies each into a low, medium or high risk profile.

This risk score is dynamic and can go up as well as down based on learned performance.

Also taken into consideration are:

Composite Profiles

In addition to the risk weighting analysis, FloFunder uses historical data from the cloud accounting platform, as well as their accountant’s knowledge of their clients to determine the health of the Seller’s business, the nature of their end customers, the frequency of business traded, average invoice size, the industry sectors, average DSO and business projections.

This approach ensures that risk profiling remains real time, proactive and dynamic as opposed to static information, which often does not reflect a true picture at that moment in time.

Seller’s end clients

The engine will also look at the ultimate Debtor across all transaction as part of the “learned risk assessment”.

This will be used to:

a) continually re-assess the seller based on how accurate the Seller’s assessment of payment by due date is, and

b) to ensure that any potentially single counterparty risk across practices is mitigated.

The above aims to mitigate the underlying risk of non-payment of invoices bought and sold on the platform in a way that no other platform does and thereby aiming to minimise potential risk to each buyer’s money.

Learned process

The FloFunder platform integrates data from multiple sources to ensure that risk profiling is continuously updated in real time.

Our validation checks include full integration into the cloud accounting platform for:

  • complete trading history
  • payment patterns of trade debtors
  • validation against purchase orders and contracts
  • verification of invoice with end debtor
  • predictive analysis of timely settlement
  • integration via APIs to Companies House for active trading information