When we talk about all the parties involved in the various aspects of vendor management, the list can grow long: project managers, procurement experts, legal experts, finance representatives, risk management, compliance officers, and, of course, the vendor relationship owners themselves. What we never talk about is the important role that data scientists can play, but we think there is an opportunity to involve them in the future.
Vendor risk management is exhausting, because of the sheer volume of available data for analysis and the organization’s obligation to report a complete, accurate, and timely risk register. Firms request service organization controls (SOC) reports, due diligence questionnaires (DDQs), and other content-rich materials from their vendors, all of which require scrutiny. The most difficult aspect is reading the tea leaves within the data. Could we apply artificial intelligence (AI) techniques such as natural language processing (NLP) to analyze responses?
Purely quantitative risk data is readily available to monitor vendor location risk, cybersecurity risk, financial risk, and ESG risks. Investment managers can consume this information to red flag changes and escalate new areas of risk to explore. And this is already happening ─ institutions and vendor management solutions are integrating and analyzing data supplied by vendors such as Dow Jones, RapidRatings, and SecurityScorecard. But rather than simple rules to detect changes, can we probe deeper and make predictions?
Applying AI and data science techniques to structured and unstructured data is exactly what quants in the front office excel at. The methodologies that quants use to find alpha with hypothesis setting and testing could be employed to assess and predict vendor risk. We believe that there’s an untapped opportunity to put data science skills, alternative data, and sophisticated technologies together to evaluate current risk and to predict future risk.
Is this too far-fetched? It probably is today, because the scarce data science skills are focused on investment opportunities, and vendor management doesn’t yet afford the same ROI justification. However, we do not think it’s out of the realm of possibility that data analytics methodologies will soon be implemented in the vitally important area of a firm’s vendor risk management efforts.
Angela Centeno is a Research Director at Cutter with more than 20 years of financial services experience in managing enterprise market data, investment operations outsourcing, vendor relationships, and custodian relationships.
Laura Jesson, a Senior Research Analyst at Cutter, has more than 20 years of experience in the financial services industry as a project manager, business analyst, and consultant.
At our upcoming member meetings, Advance Together, in June, we’ll examine current industry trends related to vendor management, including best practices and the types of tools, data, and services members use to perform vendor/provider due diligence and ongoing oversight. View the full agenda and register now.
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