In volatile times of rising inflation and interest rates it is particularly important for financial institutions to understand fully the quality of their underlying credit assets to reduce risk. Enhancing and improving on traditional credit scoring methods, risk management systems and statistical modelling to assess creditworthiness, monitor portfolios and predict the likelihood of default is a key factor in ensuring strong loan book performance, good outcomes and maintaining a competitive edge.

Advanced loan book vulnerability analysis can be achieved with the application of Finexos proprietary technology and cutting-edge data science and behavioural analytics. The unique Financial Capability Scoring (FCS)® metric coupled with AI-powered cash flow-based credit-decisioning using the FIOLA® Risk Engine enables real-time vulnerability analysis, is highly accurate and proven greatly to outperform traditional credit scoring methods.

By combining Open Banking, Finance and Accounting data with the FCS® Metric and historical loan performance data, the predictive accuracy of the probability of default of credit-based assets is optimised. This dramatically improves the detection of bad loans and incorporates early warning systems that alert to the prospect of potential default through real-time monitoring.

Bespoke risk engines can be tailored to suit individual lenders’ portfolios, with the data-agnostic machine learning SaaS-based platform delivering sophisticated scenario-based risk modelling in the form of MLaaS. Finexos combines up to eighteen times more data points than traditional metrics with advanced AI and behavioural analytics to deliver next-generation insights into the health of lender portfolios to dramatically improve early detection of bad loans that could adversely affect balance sheet positions.