It’s a constant challenge for credit managers to balance the risk appetite of their companies. Market conditions are constantly changing, impacting valuations and risk exposure. While most aggregated credit risk scores appear favorable, the current economy remains fragile, as reflected in Dun & Bradstreet’s US Economic Health Tracker.
Credit managers face pressure to understand and reduce risk and automate their processes. Teams are also increasingly becoming measured on growth, which brings yet another lens and challenge to their portfolio risk management remit. This means credit managers need reliable access to as complete a view of their customers as possible.
Set the Stage with a Portfolio Assessment
Before taking any portfolio risk management actions, we recommend taking stock of the portfolio’s performance. What is the risk profile? How does that compare to the industry standards? What else can you learn about these customers? These questions can be addressed via some basic analyses. Common approaches include segmentation analyses in our complementary D&B IQ Report, as well as a sandbox environment such as the D&B Analytics Studio.
By reviewing the findings in the context of the overall business performance and goals, credit teams will be able to prioritize several data-driven strategies to balance risk and growth. This background will also help set filters and thresholds for the various metrics and triggers leveraged in these strategies.
A Two-Pronged Approach to Balance Broad Coverage and Focused Action
We find that most credit managers benefit from implementing a combination of two complementary data-driven approaches. The first is to apply ongoing management initiatives to the full portfolio; we’ll refer to these as “activity-based” strategies. The second approach consists of monitoring the highest-risk accounts through alert triggers. Since this approach relies on monitoring signals that a potentially actionable change has occurred, we’ll refer to these as “event-based” strategies. Let’s explore specific strategies under each approach, along with data and tools that were designed to support them.
Ongoing, Activity-Based Strategies of the Full Portfolio
The following activities should be part of any credit manager’s tasks. Note, the right data and models built on these datasets can help automate these steps.
Credit Line Increase - For accounts whose payment behaviors exceed expectations or when their scores (whether from internal or external models) predict desirable behavior, credit can promote business growth by increasing credit limits. This is particularly appealing if off-self credit utilization (e.g., with competitors) is stronger than on-self utilization. For clients who qualify, the anonymized credit limit and utilization available to members of the Small Business Financial Exchange (SBFE) and/or users of Small Business Risk Insight (SBRI) for a limited set of use cases can yield additional insights when considering a limit increase.
To avoid increasing portfolio risk beyond acceptable levels, we recommend tactics such as setting a maximum size of increase and/or a maximum new limit. Likewise, this approach should be used with limited frequency in order to allow time to assess changes in behaviors after each increase.
Credit Line Decrease - Conversely, when behavioral data and/or scores deteriorate to a level that indicates potential future charge-offs, it can be wise to reduce risk by decreasing credit limits. In such cases, it’s common to decrease credit lines to the current balances. If the limit decrease is expected to be delayed, one option is to remove open-to-buy by setting it to zero.
Off-self behavioral data, including non-financial credit, can be particularly insightful, as many businesses choose to prioritize paying their financial obligations over smaller trade credit accounts in order to protect their cash flow.
Collections - While many enterprises keep credit and collections separate, the two departments do work together to weigh their portfolio risk management decisions to extend credit to customers that are most likely to pay in a delinquent manner. When a customer fails to make payments, your specific activities vary by stage of delinquency:
Early Stage (Typically 30 or 60 Days Past Due)
- Early-stage delinquencies are typically handled internally via letters, calls, emails, and text messages, with the more labor-intensive contacts (letters and calls) managed via a collections queue. Learn more strategies for effective collections here.
- The queue is prioritized based on the risk of reaching late-stage delinquency, which is known as a Probability of Default model. Since the low-risk population (typically the 25% lowest risk) may have paid late as a result of an oversight, these tend to self-cure, so collection efforts on these accounts can be delayed.
- At this stage, some credit managers may opt to apply a credit hold.
- For the remaining delinquent accounts, the type and cadence of contacts can be set based on user preferences and behaviors, along with historical success rates.
Late Stage (Beyond 60+ Days Past Due)
- After 60 days past due, the queue is prioritized based on the balance and past-due age, along with the dollars likely to be collected, or a Loss Given Default collection model.
- If they did already do it earlier in the process, most enterprises will minimize risk by placing a credit hold on the account.
- In many cases, enterprises may opt to work with a third-party collection agency, which may handle all or just the highest-impact accounts, based on balance and/or dollars likely to be collected. In either case, the finance team must weigh the cost of collecting to decide whether to continue collection efforts, offer to settle with a lowered balance, or consider legal action.
When a delinquent account ages beyond 120 days, the likelihood of getting paid decreases significantly (possibly down to 10% or less based on Dun & Bradstreet’s research), so enterprises should again consider their options before writing off as bad debt. They may again enlist outside help from a third-party collection agency. Companies may also choose to limit losses by selling the debt to a debt buyer, which will then initiate its own collection efforts.
Data-Centric Best Practices
All the approaches listed in this portfolio risk management guide rely on accurate, complete, and up-to-date data and analytics, and so it follows that the next hygiene steps are critical to performance.
Full File Refresh
Most enterprises know the value of third-party data and that in order to be useful, these attributes and scores must be as up to date as possible. To ensure the most current data is considered, many are opting for API-driven solutions like D&B Direct for Finance that continuously update values as they’re needed. In the absence of that, for companies that still use flat files in their systems, we recommend a monthly or quarterly refresh schedule for the full files. Likewise, since business identity attributes (name, location, ownership) also change, we recommend periodically rematching the portfolio; quarterly is ideal, but many clients find an annual rematch cadence adequate.
Beyond systematic updates as outlined above, data must be assessed periodically to ensure accuracy. As with many data stewardship efforts, we recommend focusing on the highest-impact records. In practice, this translates to focusing validation sweeps by data stewards on the top accounts by exposure risk. Common approaches include selecting the top 20% by risk and/or establishing a minimum threshold for the exposed dollars, either in terms of balance or credit line.
Trigger/Event-Based Strategies on High-Risk Segment
The second part of our recommended approach to portfolio management focuses on event-based triggers. Since these strategies are costly in terms of labor, specialized services, and computing power, they are applied to only the highest-exposure accounts. This exposure assessment combines risk scores, activity, and balance. To be clear, these accounts are still included in the full-portfolio risk management activities covered in the previous section; what event-based triggers bring is the ability to receive alerts on a frequent – even continuous – basis so that the portfolio manager can react in or near real time to minimize loss.
Utilize External Data Sources To ‘Push’ Alert Triggers
Monitoring services can provide push alerts on a set portfolio. These alerts can be configured to be delivered on whatever frequency suits the business’s needs and its ability to ingest and act upon them; this can be monthly, weekly, daily, or even intra-daily.
Another key part of the configuration is the criteria based on key variables – typically those used on the risk models discussed in the “Full Portfolio” section. Criteria can be either absolute (i.e., if a score rises above or dips below a set critical value) or relative (i.e., a point or percentage difference). In both cases, these thresholds need to indicate a significant negative change in the business’s underlying state. For example, a 1-point drop in PAYDEX (whose raw score ranges from 0 to 850) likely isn’t worth initiating mitigation workflows. A 20% drop – or a bankruptcy filing or lawsuit – however, is more likely to reflect a true change in the company’s ability to meet its financial obligations.
Focus on the Highest-Risk Accounts
Since most portfolio risk management teams have limited resources, they must aim to generate actionable alerts, which in turn requires focusing trigger efforts on the highest-risk 2%-5% of accounts. The initial selection of accounts to monitor will therefore take into account exposure (line and balance dollars) and risk assessments (from the full-portfolio risk models).
Once that high-risk segment is identified, triggers can be generated based on a number of events, such as:
- External delinquency or charge-off with another company
- High-dollar suits, liens, judgments
- Excessive new credit/exposure opened
- Rising revolving credit utilization
- Reduced payment-to-balance ratio
Strive for High Actionability
Alerts are useful if they enable finance teams to prevent losses through concrete actions such as credit limit decreases, credit holds, and accelerated collections. We have found that the right balance between capturing predicted delinquencies and minimizing manual reviews is achieved when roughly 20% of alerts result in an adverse action. If the action rate is significantly different, we recommend revising criteria and/or the review process.
Further, we also recommend revising the criteria used to identify the highest-risk 2%-5% of accounts for proper portfolio risk management. While this is typically covered during model validation and recalibration efforts as noted above, a finding that adverse actions are markedly different from expected may indicate a model tune-up might be needed.
The information provided in articles are suggestions only and based on best practices. Dun & Bradstreet is not liable for the outcome or results of specific programs or tactics. Please contact an attorney or tax professional if you are in need of legal or tax advice.