Liquidity is an important factor for organizations to consider when evaluating the viability of a receivable. An organization’s ability to cover its short-term debt obligations and increase cash flow depends on the liquidity of its assets. A company can take several steps to evaluate the liquidity of receivables, as well as use data analytics to improve their evaluation process.
The first step in evaluating the liquidity of receivables is by assessing payment histories for similar customers in order to establish an average time it takes for them to pay (Koh, 2018). This information allows companies to anticipate when they may need additional funds and plan accordingly. Companies should also review any credit agreements they have with customers that may affect their payment history or include deadlines, penalties, or incentives (Koh, 2018). By understanding what conditions are attached to payments before invoicing, organizations are better prepared if payments do not come through on time. Additionally, organizations should prioritize accounts with delinquent payments over those who pay regularly so efforts can quickly be shifted towards collecting those monies owed (Koh, 2018).
Examine the steps a company can take to evaluate the liquidity of receivables. Can data analytics improve the evaluation process
Data analytics can also improve a company’s ability to assess the liquidity of receivables because it helps identify patterns and trends within customer behaviors which may indicate potential risks or opportunities (Savic et al., 2017). Analytics allow companies access detailed reports about customers’ unpaid balances including how many times a balance has been past due versus paid on time; this helps managers make informed decisions about each individual customer’s creditworthiness (Savic et al., 2017). Data analytics can also help identify larger trends in regards to overdue payments such as locating areas where collection strategies might be improved or determining if certain products require more stringent payment terms than others (Savic et al., 2017). Furthermore by leveraging predictive algorithms managers gain insights into future customer behavior like whether customers will likely default on payments or become loyal customers based on historical data sets provided by sophisticated analytics tools(Savic et al., 2017) Therefore these assessments provide actionable information that empower businesses in improving their revenue cycles while mitigating risk associated with bad debt.
In conclusion there are many steps that a business can take in order evaluate the liquidity of receivables such as assessing payment histories, reviewing any related credit agreements and prioritizing accounts with delinquent payments . Also data analytics play a part in improving this evaluation process by providing detailed reports about overdue balances ,identifying larger trends regarding collections strategies ,as well as predicting future customer behavior . Therefore both traditional methods along with advanced digital approaches should be employed together in order maximize effectiveness when assessing liquidy of recievables