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.
Custom use cases that are either highly specialised or so unique that no vendor currently offers a solution may also justify internal development. In these cases, firms may even find opportunities to commercialise their internally built AI solutions in the future.
Firms that already have strong internal AI expertise, along with access to sufficient data, are in a better position to develop their own solutions. Internal teams can build tailored products that meet specific business needs while ensuring regulatory compliance and long-term cost efficiency. Over time, self-developed solutions may reduce recurring costs compared to ongoing vendor fees, especially when initial development costs are front-loaded.
For many organisations, a hybrid approach may offer the best of both worlds. Hybrid solutions enable firms to build highly customised elements that are unique to their operations while integrating vendor-provided AI components where appropriate. This model can leverage APIs or cloud-based AI model catalogues like Microsoft Azure, which offers access to solutions such as Saifr’s AI content review models. These integrations allow firms to incorporate sophisticated AI without having to build every component from scratch.
Ultimately, AI adoption strategies depend on each firm’s specific circumstances. For mid-sized and smaller institutions, buying remains the most practical and cost-effective approach. Larger organisations may find a mix of buying and building offers the best balance between speed, control, and innovation. As AI technology continues to advance rapidly, companies that hesitate risk falling behind their competitors. The time to act is now—whether through building, buying, or adopting a hybrid model.
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