Third-Party Model Risk: Advanced Due Diligence and Contractual Oversight for Embedded AI/ML Solutions in SaaS Core Banking and Risk-as-a-Service Platforms -- A Model Governance and Regulatory Risk Framework for Financial Institutions.

Abstract

The transformation of financial institutions toward modular, platform-based operating models has altered how quantitative and algorithmic decision systems are developed, deployed, and governed. Software-as-a-Service (SaaS) core banking platforms and Risk-as-a-Service (RaaS) providers increasingly embed externally developed Artificial Intelligence (AI), Machine Learning (ML), and quantitative models into functions such as credit underwriting, fraud detection, capital estimation, and regulatory reporting. While this shift offers meaningful efficiency and analytical benefits, it also introduces a structurally distinct form of model risk driven by external control, limited transparency, continuous vendor-managed change, and increasing concentration on a small number of technology providers.Existing regulatory frameworks including the Federal Reserve’s SR 11-7, the UK Prudential Regulation Authority’s SS1/23, the EU’s Digital Operational Resilience Act (DORA), and the Monetary Authority of Singapore’s Technology Risk Management (TRM) Guidelines establish that institutions retain responsibility for the governance and risk management of third-party models [1–4]. However, these frameworks are intentionally principle-based and provide limited operational guidance on how to govern, validate, and evidence effective challenge over opaque, externally operated models in practice.

This paper addresses that gap by developing a unified governance and validation framework specifically designed for embedded third-party AI/ML and quantitative models. It makes three primary contributions. First, it formalizes third-party model risk as a distinct category of model risk characterized by structural features that differ materially from those of internally developed models, particularly with respect to transparency, control, and concentration [5,6]. Second, it proposes a lifecycle-based governance and black-box validation framework that enables independent challenge, performance monitoring, and regulatory defensibility even where access to source code, training data, or internal model logic is limited [7]. Third, it integrates legal, audit, and technical controls into a single operational approach, translating high-level supervisory expectations into enforceable contractual rights, audit evidence standards, and validation practices [3,4].By shifting institutions from passive reliance on vendor assurances toward active, evidence-based governance of embedded analytical systems, the framework supports regulatory compliance, operational resilience, and systemic stability in an increasingly platform-driven financial ecosystem [5,6]