Commercial Risk Intelligence Buyers Guide

1. Data breadth, depth, and provenance

Data is truly the foundation of commercial risk intelligence, and probably the most important aspect to consider. Without complete, comprehensive data, your analysts and investigators will still need to cobble together different pieces of information and spend significant time and effort validating their findings.

But it’s also essential that data is reliable and clearly sourced, what is called data provenance. Data provenance provides a clear line between the end-user analyst and the original source, not only to prove its accuracy, but also as corroborating evidence in official investigations. Many providers aggregate data from third parties, which limits this direct connection, while others only provide the original source information upon request, adding processing time.

And finally, it’s important to consider how comprehensive the data is. Certain data providers specialize in specific data types, like financial intelligence or supply chain intelligence. But the focus on one aspect of commercial activity limits analysts’ ability to see the big picture, such as detailed ownership networks and potential risks that may be a few steps away from the target entity. This combination of a data types provides more context on the rationale behind business activities and surfaces deeper insight into relationships, ensuring transparency and highlighting high-risk connections.

2. Network generation and visualization

Network visualizations have revolutionized how analysts and investigators see data, moving from rows and columns in spreadsheets to dynamic, interactive graph networks. But not all visualizations are built the same, and it’s not always easy to tell them apart.

The main difference between available visualization technologies is graph analytics. Graph analytics is a technology that is designed to analyze a variety of data types and represent data and connections graphically. Because of this intentional design, commercial risk intelligence platforms that utilize graph analytics work faster and have no trouble analyzing multiple levels of connections, because that’s exactly what the technology does.

But many providers do not use graph analytics for network creation. Instead, they use a traditional database and then create networks ad hoc as users request them. This not only requires additional configuration but also leads to often lengthy loading times. How lengthy? There have been cases where a simple query that should take seconds results in a 10-minute wait time to generate a graphical network and display it visually.

Utilizing graph analytics technology that is designed for visual analysis eliminates this issue and helps teams of analysts and investigators handle increasingly complex relationship analysis quickly.

3. Purpose-built to provide transparency for analysts and investigators

This is an often overlooked but essential aspect of commercial risk intelligence: it must achieve the ultimate purpose of helping your teams of analysts and investigators uncover, resolve, or validate the issue at hand. While that may seem obvious, providers typically offer either simple datasets with little structure or amorphous toolsets and generic technologies that organizations have to build to suit their needs. A true commercial risk intelligence platform provides relevant analysis and investigation capabilities natively, not only leading to fast adoption by investigative teams, but also simplifying deployment and configuration.

On the first point, a solution that is designed for the specific professional or role using it tends to require less onboarding and initial training. It also means the features and workflows tend to be more intuitive and valuable for these users because that is who the platform is built expressly for.

Similarly, when it comes to implementation and configuration, a purpose-built platform will have the majority of the required capabilities already included. Conversely, a toolset with a large set of possible uses sacrifices broad applicability for increased effort during design, implementation, and configuration to meet the organization’s needs. This distinction could mean the difference between going live with the technology next month or next year.

Lastly, purpose-built platforms are more likely to have relevant data. If you’re focused on trade compliance, for example, a purpose-built platform would be sure to supply comprehensive trade data. Having the right information at hand is as critical as having the appropriate features.

4. Enterprise-grade integration, security, and scalability

Having an effective, standalone platform is no longer realistic; solutions also have to integrate with the other technologies and workflows in your organization. This is especially true when connecting commercial risk intelligence data as a feed into your existing solutions through an application programming interface (API). Understanding the amount of integration effort, on a scale from “out-of-the-box” to “multi-year endeavor,” is step one. Ensuring that you can harness the value you expect from the combined solutions is also essential but may take some time to truly assess. It’s best to select providers that are not only open to integrating with your existing systems but also have an established relationship with that system’s provider, so they can openly collaborate and work through potential issues for you and their other combined clients.

Data security is obviously a critical concern for organizations, so it is important to select a commercial risk intelligence provider that has robust information security protocols for their platform as well as for themselves and their customers. That means understanding what data and user behavior they are capturing, where they are storing that feedback, and whether that is used for product enhancement, targeting, or the benefit of other customers. Especially in this area, sharing what your users are doing with an outside party may be a non-starter and considered not only sensitive, but proprietary intelligence.

Another important factor to consider when selecting a commercial risk intelligence platform is enterprise scalability. As your organization and team of users grows, it is important that this platform maintain performance. Organizations should consider processing time to ensure users can complete tasks quickly. Likewise, they should also prioritize throughput so your teams can input lists of thousands of customers, suppliers, or entities of interest without needing to do searches individually. Some technologies may struggle with large-scale datasets, which can limit their usefulness or require more time or resources to complete tasks.

5. Time-to-value

This last factor is by no means least but is often an afterthought in the selection process, unfortunately. Many applications of commercial risk intelligence are for compliance, intelligence, or risk management use cases, so the investment is seen as a necessary expense. However, that does not mean organizations should not understand the time it will take to deploy, tune, train, on-board, adopt, and see success. As with some of the previous factors, different platforms and approaches will have vastly different times-to-value ranging from weeks to years.

One fundamental difference between providers is whether they are deployed as a Solution-as-a-Service (SaaS) or on-premises technology. The former can remove many of the obstacles to implementation as either a turn-key service or a lighter implementation effort since the technology is running for other customers. On-premise solutions may be required for air-gapped or secure environments to meet an organization’s information security requirements.

Following deployment, these platforms can vary in configuration and tuning time, no matter whether they are SaaS or on-premise. As with the third consideration factor above, purpose-built commercial risk intelligence platforms require less configuration because they are designed around that specific use case and user. Technologies that cover a broader range of use cases or require more complex programming will need to be customized to fit your application. This can range from months to well over a year, so get an understanding of which elements of the platform are usable at which time.

Once deployed, configured, and ready for users, the last element to consider is how intuitive the platform is. Ask questions like, “do my users need technical expertise,” “how much training is typically required,” and “how long does the average user take to be proficient?” These are all critical considerations to understand how long it will take to reap the rewards of your investment.

Ready to take the next step?

While this list is not exhaustive, it provides the top factors that can mean the difference between a commercial risk intelligence platform that delivers on your expectations or a lengthy project that leaves you looking for other options.

As you put together your short-list of commercial risk intelligence providers, consider how Sayari Graph addresses these considerations:

• Data breadth, depth, and provenance: Sayari Graph has the most comprehensive commercial risk data covering more than 2.8 billion records, 455 million companies, 522 million key individuals, and 250 global jurisdictions. This includes a variety of data, including corporate ownership, trade and supply chain data, and risk data like sanctions, PEP, and other regulatory lists.
• Network generation and visualization: Sayari Graph uses graph analytics and prepopulates more than one billion entity relationships, making it easy for your investigation and analyst teams to find hidden connections at a glance.
• Purpose-built to provide transparency for analysts and investigators: Sayari Graph is designed by and for analysts and investigators, whether performing supply chain mapping, third-party due diligence, financial crime investigations, or national security intelligence gathering. This focus was the primary reason for founding Sayari and remains the core mission today.
• Enterprise-grade integration, security, and scalability: Sayari Graph provides easy integrations to dozens of enterprise tools, whether used as a SaaS interface or an API. Sayari also meets the scale and security requirements of more than 200 customers, from leading regulators and government agencies to global financial and Fortune 500 organizations.
• Time to value: As a SaaS platform, Sayari Graph can be deployed in as little as a few minutes and includes everything you need to get started. On average, first-time users find their target of interest within 5 minutes of using Sayari.