As artificial intelligence continues to transform industries, financial services firms are facing a familiar dilemma: should they build their own AI solutions or buy them from external vendors? While AI introduces some unique complexities, many of the same considerations that applied to previous technology waves still hold true.
According to Saifr, executives must weigh factors such as the strategic importance of the solution, speed to market, technical complexity, internal expertise, budget, and data availability.
In many cases, purchasing an off-the-shelf AI solution may offer the most practical option. Research from Saifr revealed that most firms adopting AI have done so via vendor-supplied solutions, particularly in specialised RegTech areas. This approach is often the fastest way to address urgent needs, such as responding to a new regulatory requirement or mitigating a newly discovered risk. Vendors can often deploy solutions quickly, sometimes running in parallel with existing processes while firms work on full implementation.
For firms whose needs align with common industry use cases, buying is also the most resource-efficient option. Rather than investing significant internal resources to recreate something that already exists, financial institutions can leverage vendor solutions that come with built-in expertise and proven results. Even when minor customisations are needed, vendors frequently offer flexibility, such as adjusting models to flag certain risk levels.
A significant barrier to building AI in-house is the lack of internal expertise. Many firms either do not have AI specialists on staff or have teams whose focus lies elsewhere. Partnering with experienced vendors provides immediate access to sophisticated AI capabilities and allows firms to benefit from ongoing innovation in the vendor market, offering flexibility as AI evolves.
Data availability is another critical factor. Developing highly effective AI models requires substantial and often proprietary datasets. For instance, Saifr’s marketing compliance models were made possible by nearly 20 years of industry-specific data within Fidelity Labs, data that few firms would be able to replicate independently. Without robust data, even the most capable internal AI teams may struggle to build effective models.
Cost predictability is another advantage of buying. Vendor contracts can offer clear, fixed pricing, which helps financial institutions plan long-term budgets. In contrast, in-house development may involve unpredictable costs, both during development and for ongoing maintenance and updates.
Despite these advantages, there are scenarios where building AI internally may be the better route. In cases where a firm’s AI solution could create a unique competitive advantage—whether through superior customer service, back-office efficiency, or proprietary data insights—it may make sense to build and own that capability outright.
Data sensitivity is another concern that may push firms towards internal development. For organisations dealing with highly sensitive or proprietary data, working with third-party vendors may present unacceptable privacy or security risks. Building AI in-house allows firms to maintain full control over their data throughout its entire lifecycle.
Find the full RegTech Analyst post here.
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