A few months ago we launched Merchant Real Industry, our GPT-4-powered classification model that automatically determines a business’s Merchant Category Code (MCC) and NAICS code with >90% accuracy. We’ve witnessed significant adoption of this feature, and have received many requests to continue automating the merchant risk assessment process for SaaS companies and PayFacs.
Today, we’re excited to announce IndustryMark. Now, customers can automatically receive 2 risk ratings: an industry-consensus risk rating for merchants based on their MCC, and a benchmark non-delivery exposure (NDX) time frame. Customers can also set up custom MCC risk ratings and NDX time frames based on their own internal understanding of merchant risk.
Absence of industry standard for MCC risk tiers
Accurate merchant industry classification is critical for risk teams: it enables them to identify prohibited, restricted or high-risk merchants, pre-qualify low-risk merchants, and monitor for fraudulent activities on an ongoing basis. MCCs are pivotal in these risk management processes, but they are hard to assign accurately and at scale. We built Merchant Real Industry to solve this problem.
Once MCCs are identified, SaaS and PayFac teams still need to assess the level of risk associated with each MCC. Since there are no industry standards or sources of truth to define these risk tiers, many risk teams have resorted to the following manual methods:
Find data sources for relevant risk parameters such as chargeback risk, NDX, fraud risk, regulatory risk, etc.
Calculate benchmark risk ratings and NDX time frames for over 500 MCCs using this data, often in spreadsheets
Use these data to calculate risk exposure for each merchant, which is usually saved on another platform
This process can slow down risk decisioning and cause inaccuracies over time. Many teams don’t have the resources to update benchmark data with new information, meaning risk exposure calculations don’t always reflect the up-to-date risk associated with a merchant.
Introducing MCC-level risk metadata
IndustryMark takes the manual effort and guesswork out of MCC analysis, accelerating risk decisioning and enabling risk teams to more accurately assess merchant risk exposure.
How does it work? IndustryMark consolidates data from multiple sources - shipping time frames from merchant websites, chargeback risk, regulatory risks, etc. - and uses these data points to designate a risk tier to each MCC: high-risk, medium-risk, or low-risk. These risk tiers appear in each merchant’s profile on Coris, alongside their MCC classification and their delayed delivery days (DDD) risk. DDD is similar to NDX, and refers to the risk that a merchant is unable to deliver goods/services a consumer has already purchased.
Having access to MCC data through Merchant Real Industry and IndustryMark is useful throughout the risk management process:
Onboarding & underwriting: If a merchant’s MCC is deemed low-risk and they pass other onboarding checks, risk teams can automatically approve their application. If a merchant’s MCC is deemed high-risk, teams can conduct a deeper dive through a manual review. This reduces friction for good merchants and reduces the number of false positives analyzed in manual reviews.
Ongoing risk monitoring: A merchant’s DDD – either custom or benchmark – will automatically populate for customers using Fuzio, our new merchant risk platform. With IndustryMark, customers can have the exposure automatically computed and trigger a series of automated actions or forward the case to manual review. This centralized decision-making improves detection of risky merchants (false negatives).
Case management: Having up-to-date MCC signals gives underwriters and risk analysts sufficient context when conducting manual reviews in Coris’s case management system. They don’t have to rely on stale data in a spreadsheet or manually contact merchants for updated information. This can significantly shorten manual review times.
Want to learn more?
We’ll continue to improve on IndustryMark by crowd-sourcing MCC risk tiers and NDX time frames from leading payment companies, and build a true “source of truth” for this assessment. If you’d like to contribute to this data set and receive aggregated benchmark data in return, please reach out.