Using data visualisation for effective credit risk management

Data visualisation plays a vital role in today’s information driven world. Transforming complex datasets into intuitive, visual formats helps convey a story in an easy-to-interpret way. This enables quicker insights and informed decisions. There are many uses and benefits for effective credit management and accounts receivable, across credit risk assessment, management and analysis.

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What is data visualisation

Like a picture tells a thousand words, data visualisation transforms raw data into a graphical story with a purpose.

It is a quick and effective way to communicate data-driven insights for instant understanding, and can be used to:

• Identify patterns and trends
• Provide actionable insights
• Identify outliers in datasets  
• Enable interactivity to get more information  

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The benefits of data visualisation

When data visualisation is used to present credit risk data, actionable insights can speed up processes and improve outcomes. Everyone in the team is on the same page, regardless of their credit expertise. Benefits include:

• Faster response times
• Improved collection rate by prioritising collection efforts by risk  
• Making credit decisions quickly and easily  

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Data visualisation in credit risk assessment, management and analysis

Manual, inefficient processes are the bane of credit and accounts receivable teams. When credit data is fragmented across disjointed legacy systems and scattered across multiple processes and repositories, it’s hard to keep track of changes to customers. The risk of bad debts and insolvent customers coming as a surprise is high.  

Being able to present customer data visually has enormous applications for credit risk assessment and management. It helps businesses see, interact with, and better understand credit risk data. With so many different channels of information, having a centralised place for information that is instantly accessible means that credit teams can absorb information quickly, understand insights and act faster.  

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How the Access Intell products use data visualisation

Access Intell is not a credit bureau. Instead, we aggregate data from diverse global sources and transform it into customised credit risk management intelligence. We’re focused on presenting this information in the most efficient way possible, to deliver instant and actionable insights.

Data visualisation plays a large role in how we present these complex datasets to our customers. We convert the data we receive into common ratings to deliver uniform risk categorisation. These common ratings are colour-coded to instantly communicate risk, with the ability to click in to understand the why behind the rating.  

One example is our interactive Access Monitor dashboard, which places a client’s customers into eight colour-coded categories of overall risk. This snapshot overview instantly shows you where your risk lies, including the number of unique entities and the amount outstanding.

We use a similar approach for the payment risk and insolvency risk ratings provided for each customer. Colour coding provides the visual representation of risk, and customers can be filtered by these ratings.  

We also use these ratings to enable instant decision making when assessing a new customer via Access Approve, our online credit application product. Upon receiving a new application, or adding a potential customer for assessment, our summary tab presents an instant snapshot of the applicant and their risk profile. Colour coded scores for insolvency risk, payment risk and failure risk give an instant, visual representation of risk. This can enable a fast, easy decision-making process.  

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Key Takeaway

Data visualisation is a key tool for efficient, effective credit risk management.  At Access Intell, we provide a well-rounded view of risk. Intuitive data visualisation provides an instant overview of credit risk data, highlighting risk that is easy to understand. While our customers records give you an understanding of the 'why' behind the risk rating.