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How does AI-Powered Credit Risk Management Work for Large Enterprises?

What you’ll learn

  • How AI-powered Credit Risk Management can simplify global credit operations for enterprises.

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Credit risk management teams have come a long way from being a back-office function to becoming strategic enablers of cash. Credit teams are the vigilant eyes and ears of an organization, protecting the business against risk

Importance of Effective Credit Risk Management in Large Enterprises

For a global business, credit risk management extends across geographies. Enterprise credit teams have to deal with diverse portfolios in terms of various languages, currencies, and complex parent-child scenarios.

Previously, credit teams used to assess the risk by periodically reviewing these customer portfolios. However, COVID-19 has impacted their modus operandi.

Let’s understand this better by having a look back on the COVID disrupted economy:

With the onset of the pandemic, a lot of organizations filed for bankruptcy. The fluctuating economy led to credit teams emphasizing more on credit data in real-time to mitigate risks. The immediate response by credit teams has been reassessing customers’ creditworthiness frequently.

“We’re seeing a greater emphasis on frequent portfolio analysis. Clients who weren’t doing it before have now started analyzing it on a more frequent basis.”

Vice President, Solution Consulting | Experian

As a result, the number of credit reports pulled by an organization to review a customer’s creditworthiness increased by 89%.

This was the right approach for survival at the time, but in the long run, the need for more frequent credit reviews means an increase in cost and workforce as credit reports need to be pulled from multiple credit agencies.

Also, a lot of time and effort goes into studying each report, identifying the correct data to consider, and then updating credit information for each customer. Credit teams should be spending this time to make actual credit decisions instead of gathering information.

An efficient global credit risk management process should sit on top of various geographies, tracking the risk at a global as well as regional level. This requires enabling automation and digital transformation in day-to-day credit activities to mitigate risks in real-time. The below section provides further insights into the traditional credit risk management system. It is followed by a comparison with AI-powered credit risk management and a look at how automation can shape the way towards excellence for credit teams.

How was Credit Risk Management Traditionally Performed?

Considering daily operations, credit teams usually spend their time handling the following:

  • Onboarding new customers
  • Reviewing existing customer portfolios
  • Releasing blocked orders

Let’s take a look at the significant challenges encountered by global credit teams while they are performing these tasks:

  • Slow, Paper-Based Customer Onboarding Impacting Customer Experience
  • Enterprise credit teams have to onboard new customers across the globe. This means they have to generate credit applications in multiple languages and then translate them back to their preferred language for easy credit analysis. Sounds simple, doesn’t it? However, imagine performing rounds of translation for 100+ customers. It’s not scalable!

    Most of these credit applications are paper-based and customers often miss out on adding important business information. As a result, credit teams have to interact with customers multiple times to capture correct and complete information. Additionally, slow credit reference verifications lead to a delayed customer onboarding process, impacting the overall customer experience.

  • Manual Credit Data Aggregation, Credit Scoring, and Approvals
  • Credit teams need to log into D&B and Experian’s portals and manually download every single credit report. Additionally, they need to pull reports from regional credit bureaus to assess the risk. This is most common in regions such as LATAM or Europe. Pulling credit reports for every portfolio at a global level can be difficult. After downloading the reports, credit analysts have to manually review the credit ratings and financials and calculate the credit score. Credit approvals become slow and erroneous because of the multiple stakeholders involved, therefore increasing the risks involved

  • Lack of Real-Time Visibility into Portfolio Risk Globally
  • With periodic reviews, credit teams struggle to identify at-risk customers. This was working out when the economy was stable but in the current turbulent economy, periodic reviews are no longer a solution. This is partly because credit teams face constant unpredictability while identifying portfolio risk which might fluctuate at any time. With 1000s of customer portfolios, it’s difficult to regularly review and track the frequent changes in their credit profile.

    Moreover, there is no way for the senior management to monitor the global credit risk exposure to suggest necessary course corrections and reduce bad debt.

AI-Powered Credit Risk Management:
How Does it Work?

1. Multi-Language Online Credit Applications for Onboarding Customers Globally

With a configurable Online Credit Application, credit teams can onboard customers faster across the globe. Online Credit Application can be configured and translated in any required language and based on customer segments. With pre-filled credit applications from the sales team, customers don’t need to spend a lot of time filling up the credit application.

2. Consolidated View of Credit Risk Globally

Credit teams can seamlessly integrate across multiple ERPs, business units, and review credit risk in a standard, global currency. The senior management can review the overall credit risk exposure across geographies to develop strategies to reduce bad debt. Additionally, they can drill down to a particular geography and check the credit exposure, even in the local currency.

3. Automatically Extract Credit Data from 40+ Credit Agencies and Bureaus

Automatically extract credit reports, ratings, financials, and credit insurance information from 40+ global and local agencies such as D&B, Experian, CreditSafe, Equifax and Serasa. With HighRadius Credit Cloud, credit teams can access a one-stop repository for all credit information required for global operations.

4. Automated Credit Scoring and Approval Workflows

Credit teams can fast-track their credit decisions with automated credit scoring and collaborative e-workflows. Credit scoring models can also be configured across business units, geographies, or various customer segments.

5. Real-Time Credit Risk Monitoring

In the current economy, Real-Time Credit Risk Monitoring helps credit teams to monitor customer portfolios daily on a real-time basis. Credit analysts can receive real-time alerts related to bankruptcy, dips in credit score, and changes in payment behavior to stay on top of risks and control overall bad debt. This way they can proactively manage the credit risk.

Frequently Asked Questions

1. What Should You Look for In AI Software for Credit Risk Management?

AI-Powered Credit Risk Management software should automate repetitive manual tasks and minimize the time required for putting together data to transform it into information so that credit teams can make informed decisions. The system should work for you instead of just providing you with data. Moreover, the solution should be able to reduce process complexity by leveraging automation. It should eliminate the need for periodic reviews by giving alerts and suggestions on revised credit terms, all in real-time.

2. What Are the Main Benefits of Using AI in Credit Risk Management?

With HighRadius’ AI-Powered Credit Management Software, credit teams can achieve 100% real-time credit risk monitoring to ensure lower bad debt by tracking changes in customer credit risk and payment behavior. AI can be leveraged to predict blocked orders based on past order volumes and payment patterns. Credit teams can make better credit decisions through AI-based order release recommendations. Moreover, automation can increase efficiency with a 67% reduction in customer onboarding time through the highly configurable online credit application.

3. What are Credit Reporting Bureaus?

Credit reporting bureaus are external credit agencies that generate credit reports and scores for customers across the globe. These reports and ratings help trade-credit teams to conduct an objective credit risk analysis of customers. Some credit reporting bureaus include D&B, Experian, Equifax, CreditSafe, and CreditRiskMonitor.

4. How to Determine the Creditworthiness of New Customers?

HighRadius’ Credit Cloud helps organizations to fast-track new customer onboarding with the help of online credit applications. Customers can fill in the online credit application or it can be pre-filled by the sales teams. Credit Cloud auto-extracts credit reports from 40+ global and local agencies and automates credit scoring to evaluate the creditworthiness of new businesses.

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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.