AI in B2B world: Navigating AI in vendor & third-party relationships

Leveraging artificial intelligence (AI) for optimizing business operations has been the talk of the town for about a decade now. In the last few years, organisations across different sectors have deployed AI tools for several day-to-day functions in an effort to increase productivity and efficiency. One such area in the B2B landscape where AI is making its presence felt is third-party risk management.

As tie-ups with third parties are far more cost-efficient and convenient in delivering a wide range of goods and services when compared to producing everything in-house, vendor and supplier risks have increased. Additionally, managing compliance within a complex regulatory landscape becomes a challenge with third-party solutions.

Similarly, complex global supply chains also make it incredibly difficult to have clear visibility into the risk management practices of a growing number of third parties. Here are a few fundamental causes intensifying the risks associated with third-party solutions:-

* As the volume of cyber data continues to increase, it requires more time and effort to analyse and review it. 

* Analysis processes require diverse documentation types because the risks may be varied -- financial, operational, compliance, reputational, or information technology-related.

* Regulatory requirements may overlap or be unclear, complicating remediation and reporting.

Therefore, there is a strong case for the deployment of AI to mitigate third-party risks. In the context of vendor risk management, AI can help:

* Identify potential vulnerabilities and patterns indicative of risk. This helps businesses take corrective measures before the situation spirals out of control. 

* Analyse vendor behaviour and interactions within the supply chain to detect suspicious activities. Essentially, they show patterns indicative of risk thereby giving businesses a chance to make an informed choice on whom to conduct business with and whom not to. 

* Analyse contracts, agreements, and other textual documents to identify clauses related to security, privacy, and compliance requirements. This is then useful to implement a course correction right in time to avoid losses.

* Leverage predictive analytics to forecast potential risks associated with specific vendors or supplier relationships based on historical data, market trends, and external factors. 

* Throughout the business cycle, the AI tools analyse vendor behaviours and interactions within the supply chain to detect anomalies, suspicious activities, or regulatory violations.

* Continuously track vendor activities and performance metrics in real-time, enabling organisations to promptly identify deviations from expected behaviour or compliance issues. This is facilitated through the analysis of historical data, market trends, and similar external factors that suggest how healthy a vendor’s performance is in the supply chain and the broader markets.

Vendor risk management in a B2B scenario is the due process established to tackle the potential risks associated with making purchases or outsourcing/supplying to third-party suppliers. These measures protect businesses from supply chain disruption, data breaches, regulatory issues, or even reputational damage, all of which result in significant losses.

How to pick best AI TPRM solutions?

An effective AI solution for third-party risk management (TPRM) can cut across a lot of tedious work for risk managers and enable them to focus on strategic activities that benefit the business as a whole. Here is what you should look for in an AI TRPM solution:-

AI TPRM solutions must ensure that the data used to train the model is accurate, diverse, and representative of real-world scenarios. Also, such solutions must continue to fine-tune themselves by learning the context and nuances specific to third-party risks.

It’s absolutely essential to update AI models to mitigate potential bias. Though biases can be difficult to detect, human reviewers can identify bias in AI-generated content.  

To keep your interests safe, it is crucial to not input proprietary data into third-party large language models. AI TRPM solutions should have strong access controls and authorisation mechanisms to prevent unauthorised individuals or systems from accessing and manipulating the data.

-- Vaibhav Kanyalkar, CTO, PrivEzi.