Digitalization: driving a new era in model risk

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The views and opinions expressed in this article are those of the thought leaders as individuals, and are not attributed to CeFPro or any particular organization.

By Chris Smigielski, Model Risk Director, Arvest Bank

What examples of technological and analytical advances can you provide which are driving increased model complexity and use?

Technology, whether hardware or software, is a major driver of analytical advances and innovation in financial services. Innovation is occurring because the pandemic accelerated the transition to contactless delivery and sped up the adoption of fintech solutions to traditional financial services. According to Forrester, many consumers started using digital channels to manage their finances and tried digital payment methods for the first time during the pandemic. Artificial intelligence (AI) and machine learning (ML) approaches are foundational drivers to many customer experience solutions offered today. These appear as cutting-edge applications or existing models with enhanced AI/ML components. Chatbots use machine learning and natural language processing (NLP) to deliver a near-human-like conversational experience. New technology has the potential to be integrated more deeply into every facet of financial services delivery and bank operations, whether it is a natural language processor (AI chatbot) deployed as a customer service tool or an AI/ML approach within a software application. With regard to AI & ML, model explainability and interpretability are two issues which are key challenges to the first line business and model risk governance.

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