For consumer lenders raising money on the wholesale market, the objective is to secure the lowest loan capital rate possible. Accessing funds at the lowest achievable cost helps improve margins and the bottom line for lenders and enables them to filter savings into more competitive loan rates to consumers.

Healthy loan portfolios signal lower risk and help lenders borrow at lower rates. Analysing portfolios using Finexos’ advanced machine learning SaaS-based risk platform delivers a highly accurate new level of insight into probability of default. In addition, by employing the proprietary Financial Capability Scoring FCS®, a new method of assessing financial capability and enhanced loan book vulnerability analysis, a unique level of insight can be gained. Lenders can better demonstrate the credit quality of loan portfolios to increase confidence and enable more competitive loan capital rates to be negotiated.

Cash flow-based credit analysis is proved to be a superior indicator of creditworthiness than using traditional static credit scoring models alone. The Finexos FIOLA® Risk Engine, a proprietary AI-powered cash flow-based credit-decisioning engine, combines multi-source Open Banking and Finance data, advanced behavioural analytics and cutting-edge data science to analyse internal historical cash flow data of a borrower, and combines it with external credit, banking and loan performance data from other multiple open banking points.

Risk is reduced with the identification of increased performance and behavioural indicators to detect a far greater number of potentially bad loans, improving loan book performance and a reduction in expected defaults. This also generates significant savings of lending capital from better credit decisions and results in improved capital utilisation. Automation of the credit-decisioning process also speeds up decisions and drives down the cost of lending.

The unique FCS® metric and algorithms coupled with the data-agnostic nature of FIOLA® enables Finexos to use up to eighteen times more decisioning factors when compared to traditional credit scoring methods, with a range of algorithms supporting a number of lending verticals for specific use cases. The scale of data aggregation capabilities coupled with automated machine learning and cutting-edge data science techniques allows credit providers to calculate highly accurate suitability and affordability analysis. This improves loan book resilience through enhanced performance and reductions in default rates, making a more compelling case to balance sheet providers to secure lower capital rates in the wholesale market.