Apply the power of machine learning and natural language processing for intelligent automation, decision-support, and prediction across the third-party lifecycle.
From onboarding through continuous monitoring, achieving program agility is a challenge when users are overwhelmed with data and tedious manual processes. Business rules and mathematical calculations can reduce the burden though automation, but they can often require a lot of time and resources, which are often scarce in TPRM programs.
Aravo AI is a machine learning/natural language processing tool that leverages neural networks to “learn” the underlying business rules of your organization without an explicit business model and can be applied to practically any activity in Aravo where a user must make a decision based on a given set of data.
Helps ensure that your organization’s policies and procedures are embedded within the system by using data generated by your users. Some AI solutions use aggregate data from multiple organizations to train AI algorithms, resulting in models that may not reflect your organization’s risk appetite and other unique needs.
Not dependent on particular data inputs or on access to large third-party data sets. Because Aravo AI uses your organization’s data, you have control over the attributes and quality of the data used to train your Aravo AI solution.
Can be incorporated into any Aravo solution where users are making decisions based on review of particular sets of input data. Rather than limited, pre-determined AI solutions that prescribe when the technology can be used, Aravo gives you the ability to decide which risk domains and processes you want to enable with machine learning, whether you’re using a pre-configured Application or have a custom-configured solution.
Supports bulk processes. In addition to managing new data or processes, Aravo AI can analyze existing data to perform tasks such as changing values (e.g. status) or checking data to identify potential non-compliance (e.g. a potential error in manually assigning status) by making sure actual values match expected values.
Allows user control of AI usage. You choose how Aravo AI is configured based on your specific requirements, such as determining if you want it to fully automate a decision or make a recommendation to a user, and what level of certainty is acceptable when making decisions.
Launch workflows for complementary business processes. Because it is a component of the Aravo Platform, Aravo AI can seamlessly trigger other business processes based on the analysis of the data, such as enhanced due diligence or a corrective actions workflow.
Even with highly automated systems, TPRM teams can often be overwhelmed with the amount of data they have to manage, from assessments and related documents, to continuous monitoring data, to external risk intelligence data. The sheer volume of data increases the risk of human error, such as missing important details or subtle changes that can be detected by a machine learning engine.
By supporting and validating decision-making processes, machine learning instills more faith in the decision-making expertise of the first line of defense (the risk owner) and the second line of defense (the risk expert). That confidence, in turn, improves the activities of the third line of defense (audit).
Growing reliance on third parties, increasing and evolving regulations, and increased C-level/board focus on risks like cybersecurity and supply chain resilience increase the pressure on TPRM programs despite the fact that that the majority of programs don’t feel they are adequately resourced. AI can help teams work more efficiently and scale their programs to meet these demands with greater quality and fewer mistakes.
Because Aravo’s practical AI approach uses your organization’s decisions to train the system, your organizational expertise is trained into the system. There are systems that attempt to use aggregated data from a buyer community to train machine learning, but TPRM is not a use case that is suited to a one-size-fits-all approach.
