
A vendor risk assessment is the process of evaluating the risks a third party introduces before and during a business relationship; automating it uses software and AI to gather, score, and monitor that risk at scale.
In an environment of increased risk and volatility, organizations are becoming increasingly aware that automation is no longer a “nice to have” for vendor risk management. For most organizations, with more than a few hundred vendors, it is strategically critical. The ability to automate vendor risk assessments is proving particularly valuable during the pandemic as organizations find they have to quickly pivot to new vendors for a variety of reasons, including local lockdowns and disrupted supply chains. Organizations with automated risk assessment processes have been able to onboard new vendors more quickly and efficiently and assess for emerging and changing risks more effectively, while organizations using manual processes like email, spreadsheets, and shared drives often struggled to adapt. Automating vendor risk assessments enables organizations to respond faster, reduce errors, and strengthen decision-making across their third-party ecosystem.
Vendor risk assessments are a critical part of the due diligence stage of onboarding a new vendor. As the number of vendors that an organization works with grows, it often finds that the manual processes associated with gathering, validating, evaluating, and maintaining vendor risk assessment data are resource intensive, error prone, and time consuming. When unexpected events occur, manual processes can break down, the business lacks critical information, and the organization cannot respond with the agility required in a rapidly changing environment. This blog explores why vendor risk assessment automation delivers such value and how vendor risk management teams can take it to the next level with artificial intelligence and machine learning.
About two-thirds of organizations use vendor risk assessments as part of their third-party risk management program, but just 46% of organizations require an initial risk assessment of all their third parties pre-contract, according to Aravo’s 2020 TPRM Benchmarking Survey. Key best practices for the vendor risk assessment process are emerging from within the discipline, although implementation of them varies considerably. Five fundamental best practices are:
Best practices around vendor risk assessments can be difficult to implement if the program relies on manual processes. Capturing, processing, and analyzing the data involved in these best practices can become cumbersome once vendor risk management programs grow beyond a few dozen vendors and impossible as they grow beyond a few hundred.
The benefits of automation for vendor risk assessment are considerable, for both the vendor risk management team and for the business. Three core benefits are:
Automation delivers value for both the vendor risk management team, for the business, and for senior management and the board. Stakeholders have the information they need to make decisions with confidence, delivering enhanced business value.
The benefits of automation for vendor risk assessment are considerable, for both the vendor risk management team and for the business. Three core benefits are:
Data Gathering – A straight reimplementation of a (usually) spreadsheet-based process, whereby every individual answer on an assessment must be reviewed. This stage can also extend to manual review of collected third-party intelligence data (e.g. negative news). While the data collection process is somewhat automated, what you can do with it is highly manual.
Business Rules – By putting in place a robust set of business rules around vendor risk assessments in the software, organizations can eliminate the need for manual intervention at key decision points, saving time and human resources. Rules drive the automation process and are based on static thresholds. For instance, if a third party answers “yes” to the question, “will you be processing PHI as part of your services?”, there is a business rule that triggers a data privacy assessment. Rules work well, but trying to create every rule for every contingency in a complex process can be tedious and error-prone.
Analytical Evaluation – A mathematical calculation and evaluation are used to drive automation. For instance, a third party’s answers on an assessment are assigned various percentage weights to arrive at a single score. Based on that score, the system determines that the third party is low risk, approves the third party for onboarding, and triggers the onboarding process. Analytical evaluations can also become highly complex to build manually.
Machine Learning – The vendor risk management system learns from the actions taken by expert users and is able to make similar decisions without the need to have an explicitly defined model. For instance, when a third party completes an assessment, the system knows how humans would respond based on an analysis of all of the similar decisions that it has been exposed to. This can speed up the vendor risk assessment process even further and reduce the risks associated with building and maintaining either a regime of business rules or mathematical calculations.
The AI approach a team uses within its vendor risk assessment process will depend on a variety of factors, including the maturity of the program. One ancillary benefit of machine learning is that it can also provide insight into the robustness of your human decision-making processes. If the machine’s level of confidence in its decision is low, this could mean there’s been inconsistent decisions made over time by your risk experts, requiring more training or upskilling to course correct.
In conclusion, automating vendor risk assessments through TPRM software can transform the vendor onboarding process, provide the organization with better risk management data to support improved decision-making, and increase the agility of the business. By automating best practices, and applying AI as programs mature, organizations can enable the business to better align vendor relationships with overall strategy, helping it to meet its goals.
Aravo AI is a configurable natural language processing and machine learning platform built into the larger Aravo business process automation platform. Aravo clients can leverage AI for any use case in which users make decisions based on input data. These decisions, made over time, train the decision engine to automate future processes, from vendor risk assessments to ongoing monitoring and more.
Aravo clients can leverage AI for any use case in which users of the platform are making decisions based on review of particular sets of input data. These decisions made over time are used to train the decision engine to advise on or completely automate that decision-making process.
In The Forrester Wave for Supplier Risk and Performance Management, Q3 2020, Aravo was recognized as “an SRPM leader thanks to its domain expertise and AI vision.”
The report noted that “Aravo is ahead of its competitors in applying AI to streamline risk assessment and monitoring.”
Aravo customers benefit from a unique combination of 20 years of experience in delivering solutions to the world’s largest brands and developing award-winning technology.
To find out more, download our whitepaper AI for Third-party Risk Management
Automating vendor risk assessments is crucial for organizations to respond faster, reduce errors, and strengthen decision-making across their third-party ecosystem. It enables quicker onboarding of new vendors and more effective assessment of emerging risks, especially during disruptions like local lockdowns and supply chain issues.
Effective vendor risk assessments require maintaining a single source for all vendor data and using a structured approach with consistent questionnaires. Organizations should also gather business insights about the vendor relationship to tailor assessments, covering risks like data privacy, IT, cyber, and financial viability, and ensuring ongoing monitoring.
Automation significantly boosts efficiency by streamlining tasks like sending, tracking, and data wrangling, allowing teams to focus on strategic analysis. It accelerates the overall assessment process, enabling faster sharing of data, quicker onboarding decisions, and increased organizational agility and operational resilience.
Vendor assessment questionnaires should comprehensively cover risks relevant to the product or service level, including data privacy, IT security, and cyber threats, especially if personal data is involved. Additionally, assessments should evaluate holistic risks such as the vendor’s financial viability to ensure overall stability.
Artificial intelligence and machine learning can dramatically improve vendor risk assessments by learning from expert decisions to automate future processes. This speeds up assessments, reduces human errors, and eliminates the need for complex, manually built business rules or mathematical calculations, enhancing accuracy and consistency.
Organizations should consider automating vendor risk assessments when managing more than a few dozen vendors, as manual processes become cumbersome and error-prone. Automation becomes strategically critical for those with hundreds of vendors, enhancing agility and resilience in rapidly changing environments.
A vendor risk assessment is not a one-time activity; it forms the foundation of an ongoing monitoring program. Vendor relationships, business conditions, and associated risks continuously evolve, requiring regular reassessment and monitoring to ensure sustained compliance and manage emerging threats effectively.
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