Credit risk management is the practice of mitigating financial losses by understanding the adequacy of an organization’s capital and loan loss reserves at any given time. It involves building the risk profile for a customer based on their current financial health, past payment history, and market conditions.
Developing a customer’s risk profile is challenging because of the many factors that need to be considered. Data across various parameters and periods must be aggregated and analyzed to get a holistic view.
So, how do financial institutions and companies create customer risk profiles for managing credit risk?
Sorting customers into groups based on parameters such as past payment behavior is one of the first steps used by businesses to build customers’ risk profiles. If a customer demonstrates poor payment practices, they are more likely to present a higher risk. Similarly, you might find indications of financial stress in a specific geography or sector, leading to higher bad debt from customers in these categories.
This exercise also helps identify your clientele’s spread based on their credit risk. If a high proportion of your revenue is attributed to a small number of customers, the risks to your cash flow are higher, especially if the credit profile of this customer group is not strong. Alternatively, customers that represent a small part of the overall receivables may not make a significant impact even if they pay a few days late.
Segment customers based on geography, trade sectors, product categories, payment guarantees, percentage of overall receivables the customer represents, and more to get a picture of the risk from different angles.
Identify and list what security arrangements and guarantees the business uses. Analyzing security arrangements and instruments help shed light on what percentage of your AR portfolio is protected.
Following are some examples of security arrangements and guarantees:
Monitor previous unpaid invoices from different customer segments to identify reasons for late-payments and track the actions taken.
DSO is the average time (in days) it takes a company to get paid after a sale has been made. In short, it is the average number of days between invoicing and payment collection. DSO is a good indicator of the efficiency of your receivables management process. A lower DSO indicates reduced risk and an efficient AR process.
Tracking DSO regularly helps to understand:
An aging report is a document that records accounts receivables according to how long an invoice is outstanding. It assesses the customers’ financial health, reviews the business’s cash position, and explores whether your sales team is extending the right credit terms.
Aging reports help companies improve their cash flow and optimize credit policies to reduce bad debt. Utilize a ready-to-use aging report template to gain 360° visibility on past-due receivables’ health.
Future sales prospects and market developments should be considered when assessing debtors’ creditworthiness. Aspects to be taken into account include:
Too many clauses in your payment terms can result in variations leading to delayed payments. The number of clauses in your payment terms should be limited and to the point. Payment incentives such as bonuses, discounts, extended periods of pay, and more help drive early payments. The company’s payment terms must be assessed and adjusted accordingly.
An efficient team of credit risk managers helps protect the company’s cash flow by monitoring accounts receivables through daily and weekly reviews. To enhance the overall performance of the credit management team, you must:
Volatile business environments, increased competition, and cyber security risks are just some of the market pressures that raise the need for credit teams to work faster and smarter.
There has been a growing demand for risk management teams to improve operating procedures and decision-making efficiency. The rapidly changing environment and a surge of bankruptcies accelerated these efforts. Many businesses began to look for alternative data, new analytical approaches, and more dynamic reporting to stay on top of deteriorating credit conditions.
Credit and risk professionals are challenged by not having essential information at their fingertips, especially when assessing mid-sized businesses. This spurred firms to consider a range of new approaches to credit risk management.
Businesses are looking to combine out-of-the-box approaches with traditional data to estimate clients’ resilience to crises. Many businesses use data mining, artificial intelligence, and machine learning techniques to extract new and deeper insights about customers’ financial health and invoice payment capacity.
Business leaders increasingly realize the importance of having automated, dynamic reports for decision-making.
Organizations need to adopt more efficient credit assessment models that can parse larger volumes of data in shorter timelines and dynamically alter risk profiles with real-time data.
Artificial Intelligence (AI) and Machine Learning (ML) based technologies learn from complex datasets and become more accurate over time. Human data science expertise and analysts’ efforts are minimized as AI and ML models provide the desired insights for smarter decision-making.
Machine learning algorithms adapt and intuitively learn. The machine learning model is continually fed data. It then draws predictive insights on new datasets and gets more accurate with every round of credit analysis.
There are several drawbacks to the manual approach to credit risk management, including:
So, any solution that aims to enhance the process of managing credit risk must include:
Automation technologies, particularly AI and data mining techniques, offer a unique opportunity to incorporate these features into the solution and generate dynamic insights. Automation helps mitigate losses more efficiently and swiftly and is less cost-intensive.
After the surge in non-performing assets during the pandemic, many credit risk managers are working on improving their models and early-warning systems to identify high-risk clients and prospects.
According to a recent survey on credit risk management 50% of respondents wanted to update models to better forecast default probability and recoveries. Furthermore, 50% want to use back-testing exercises and internal ratings-based models to monitor portfolios at a more granular level, and 37% want to use early-warning systems based on advanced analytics.
The primary objective of the credit risk management practice is to reduce revenue losses. Monitoring credit risk allows the management team to understand which potential clients are above the predefined risk tolerance level. Correctly identifying and managing credit risk is a strategic opportunity for businesses to improve overall performance and secure a competitive advantage. Automation helps achieve this goal and improves risk assessment at a lower cost in a lesser time.
AI and ML-based solutions that help modernize credit risk assessment are already available in the market. It’s safe to say, the future of credit risk analysis will be hugely guided by automation. Check out the RadiusOne Credit Risk App for exclusive features and benefits of automation.
HighRadius Credit Software automates the credit management process, enabling credit managers to make highly-accurate credit decisions 2X faster and enable faster customer onboarding with 4 primary components: configurable online credit application, customizable credit scoring engines, credit agency data aggregation engine, and collaborative credit management workflow. Along with that, there are a lot of key features that should definitely be explored some of which are online credit application, credit information aggregation, automated credit scoring & risk assessment, credit management workflows, approval workflows, and automated bank & trade reference checks. The result is faster customer onboarding, better internal collaboration, higher customer satisfaction, more targeted periodic reviews, and lower credit risk across the company’s customer portfolio.