Build vs. Buy in Model Risk Management

EXECUTIVE SUMMARY

1. AI-assisted coding tools have cut initial build time. The upfront cost is lower than it used to be.

2. Maintenance is the largest line in Total Cost of Ownership, and AI does not shrink it.

3. An internal platform is paid for in full by a single bank. A vendor spreads the investment across an entire client community.

4. An in-house build comes with large risks. With Yields, you de-risk the project with a lower and highly predictable cost of ownership.

A decision that is back on the agenda

Most financial institutions today operate dozens to hundreds of models across credit, market, operational, and increasingly, AI use cases. Regulatory expectations continue to evolve, audit cycles are demanding more structured evidence,  while generative and agentic AI are adding a new layer of governance scrutiny.

As model portfolios scale and supervisory expectations evolve, tooling choices become a strategic decision. Excel-based registers and highly customized legacy vendor solutions remain widely used because they are accessible and flexible, but they rely heavily on internal effort and individual ownership, which becomes increasingly difficult to sustain at scale. That leaves most banks weighing two real options: build a dedicated model risk management (MRM) platform internally, or adopt a specialised external one.

The case for building in-house

Building internally is often chosen to retain control, align with internal IT strategy, and tailor features to the bank's own way of working. The argument is intuitive: no vendor dependency, full ownership of the codebase, and the ability to integrate deeply with existing data and identity infrastructure.

In practice, building a model risk management system is more than building a database with a workflow on top. It is a sophisticated software product that requires a multidisciplinary team of MRM experts and technology specialists working together and continuing to do so for as long as the platform exists.

In order to build software, it is important that all features are correctly specified by domain specialists so that the developers understand what needs to be implemented. In a niche domain such as model risk management, this means that your MRM experts will have to spend considerable time (typically several person years) to create the software specification. This is not a straightforward activity because specifying an MRM platform requires a high degree of abstraction. The constant evolution in MRM caused by changes in the regulatory landscape as well as by the introduction of new model types drive changes in the MRM policy and framework, which in turn lead to new functionality requirements for the MRM platform. E.g. SR 26-2 places a clear emphasis on assessing model risk in aggregate. To meet the aggregate risk requirement, an MRM system needs to support a dependency graph.

Hence, specifying the requirements doesn’t mean that you need to describe how your current MRM process works. It rather means that you need to describe what are the software features needed to operationalize your current and future policies and standards.

If you do not have MRM experts with a proven track record in software, or when there is insufficient time allocated for the specification effort during the entire lifecycle of the platform, then this dependency poses an existential threat to the project.

The case for buying a specialised platform

Adopting a specialised platform shifts the responsibility of platform evolution to an external provider whose entire focus is model risk. Timelines to reach production tend to be shorter and more predictable. Moreover, ongoing maintenance is included rather than competing internally for engineering capacity.

There are real trade-offs to consider on this side too. A specialised platform has to be configurable enough to fit a bank's specific governance structure, integration architecture, and reporting needs. Vendor selection, contractual terms, data residency, and exit strategy all become important. The roadmap is influenced by the broader client community rather than a single bank's priorities, which is often an advantage, but worth being explicit about.

Predictable and Transparent Costs

Hidden costs are a frequent concern in vendor solutions, a sentiment echoed in the Deloitte MRM survey. To address this, Yields employs a transparent cost model featuring a fixed implementation cost and a clear Annual Recurring Fee, which is calculated per module and per user across modules for the duration of the contract term. This comprehensive model ensures that maintenance, continuous updates, and enterprise support are all included.

Observations from the field

Across institutions that have attempted internal builds, a few patterns recur.

• A major bank located in the APAC region, has pivoted away from a legacy in-house system because it failed to scale with new standards, resulting in brittle Excel-managed workarounds and resource-heavy maintenance cycles and is now customer of Yields.
• Time to value counts. A large Asian bank was on an internal build trajectory. By the end of 2024 they realized they would miss the fall 2025 deadline set by the regulator. They went live on Yields by mid 2025, reaching production in just 3 months.
• While Total Cost of Ownership is mostly driven by the maintenance, the initial build is risky and requires a considerable effort by both business and IT. A European G-SIB spent 3.5 years before giving up on internal build to adopt Yields.

These are not isolated cases. They reflect a more general observation: model risk management tooling is highly dynamic, with 2026 marking a critical global overhaul driven by the US replacing SR 11-7 with SR 26-2, the ECB implementing a shift to Ex-Post IRB approvals by October 1, and the UK solidifying its stringent SS1/23 framework. Moreover, new methodologies appear, and AI use cases now sit alongside traditional models. Any platform, internal or external, has to keep up.

AI is NOT changing the buy vs build calculus

While AI-assisted coding tools can certainly make the initial build faster and cheaper, this creates a 'speed illusion.' We must remember that the maintenance cost is the single largest factor in Total Cost of Ownership (TCO). When it comes to ongoing evolution and maintenance, the economic model strongly favors a specialized vendor. A vendor can amortize the cost of continuous evolution, security updates, and regulatory compliance across their entire global customer base. An internal IT team, by contrast, has to fund that entire, persistent maintenance cost for a solution that only serves one client, the bank.

Build vs. Buy at a glance

The choice between building an internal Model Risk Management solution and buying a specialized platform involves critical trade-offs in time, cost, and long-term evolution. The following overview summarizes the key differences at a glance.

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Sources: [1] Alice Labs (2026), AI automation ROI benchmark. [2] Klotz, D. (2026), The buy-or-build decision revisited, arXiv. [3] MLQ.ai (2025), The GenAI Divide Report.

Choosing what fits

There is no single tooling choice that fits every organisation. Scale, complexity, regulatory environment, and internal capabilities all influence what works best. What is consistent across banks is the need for tooling that can evolve over time, rather than remain fixed at the point of implementation.

This is where Yields fits in. The Yields Model Risk Management platform has been in production at financial institutions for nine years and is used by organisations including HSBC, BNP Paribas (personal finance), Novobanco, Euroclear, Banco do Brasil,... It is a highly configurable platform backed by dedicated model risk and regulatory experts, with regulatory developments, supervisory expectations, and emerging trends such as AI continuously translated into configurable capabilities. The roadmap is shaped by the aggregate input of the entire MRM community, so every client benefits from work driven by the broader market. For banks weighing build vs. buy, that combination of immediate availability, sustained evolution, and embedded regulatory expertise is exactly what an internal build typically struggles to deliver.

Conclusion

Deciding whether to build an in-house model risk management (MRM) platform or purchase a specialized solution is a high-stakes choice for financial institutions navigating expanding model portfolios and complex regulatory overhauls. While building internally offers full codebase ownership, it requires years of design effort from domain experts and exposes the bank to substantial project risks. Furthermore, while AI-assisted coding tools can accelerate initial development, they do not reduce the long-term maintenance burden, which remains the largest contributor to the Total Cost of Ownership.

In contrast, adopting a specialized platform like Yields dramatically accelerates time-to-value while offering a highly predictable, transparent cost structure. Because an external provider amortizes the ongoing costs of compliance and system updates across a global client community, the platform naturally stays ahead of shifting supervisory expectations and emerging AI use cases. Ultimately, a specialized solution delivers the immediate operational readiness, sustained innovation, and embedded regulatory expertise that internal bank teams typically struggle to maintain alone.

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