Asset quality assessment in the absence of quality data towards optimal credit intermediation

The COVID-19 pandemic has posed a significant challenge for credit managers and risk management of financial institutions and regulators worldwide. The challenge faced arises because the consequences of outbreaks and epidemics are not distributed equally throughout, with some sectors of the economy suffering disproportionately. Businesses within the same sector are not affected the same way. This paper uses high-frequency transaction data and data obtained through web scraping to simulate firm's behaviour and performance during a crisis to estimate the sectoral impact of the pandemic and its pass-through to the portfolio of financial institutions and ultimately on economic growth. This proactive approach is critical due to the rapidly evolving nature of the crisis and delays by customers submission of the books of accounts and the impact of various measures such as lockdown and selected sector shutdowns undertaken by authorities that may have diverse implications for different businesses in various sectors of the economy thereby compromising he ability of risk managers to accurately forecast the performance of their portfolios.