What is native AI and four key advantages for financial institutions

As financial institutions explore how AI can improve productivity, decision making and customer experiences, an important question is emerging: should AI simply be added to existing technology, or built into the platform from the ground up? 

This is where native AI comes in. 

What is native AI? 

Native AI refers to technology designed with AI as a core part of its architecture, rather than adding AI to existing technology as a separate feature or additional layer. 

This means AI can be integrated more deeply into the data, workflows, decisioning and experiences that underpin a platform. 

Four key advantages of native AI 

For financial institutions considering how AI could support loan origination, there are four key advantages. 

1. Built into workflows for greater automation 

Native AI is built directly into the platform and its workflows. Unlike non-native AI that operates as a separate tool or integration, native AI can analyse information and connect outputs directly to workflows and actions within the lending process. 

This creates greater opportunities for end-to-end automation while reducing the integrations, manual handoffs and movement of information that may be required when AI operates separately from the loan origination platform. 

For example, native AI could analyse supporting documents, extract relevant information, identify inconsistencies and route an application to the appropriate workflow for further assessment. With a separate AI tool, additional integrations or manual steps may be required to transfer the output back into the loan origination process. 

2. More context for better insights 

Native AI can work with the data, rules and context already available within the platform. This can give AI a richer understanding of an application than a separate AI tool with access to only selected information. 

In lending, this could include information about the applicant, product, application, lending policy, workflow status and previous interactions. Greater context can help AI provide more relevant insights and identify information that requires further investigation. 

For example, rather than simply analysing an applicant's income, native AI could consider their income alongside employment information, expenses, liabilities, application details and relevant lending requirements to identify potential anomalies or areas requiring further review. 

3. Governance built into how AI operates 

For financial institutions, adopting AI is not simply about what the technology can do. How AI is controlled, governed and overseen is equally important. 

Native AI can be designed to operate within the platform's rules, permissions, controls and human oversight. This can make it easier to define where AI can be used, what information it can access, when human review is required and how AI assisted activities are recorded. 

For example, AI could analyse an application and highlight areas requiring further review, while defined lending rules determine policy outcomes and a lending specialist manually reviews exceptions before the application progresses. 

4. More seamless customer and staff experiences 

Native AI can be incorporated directly into the experiences customers and staff already use. Rather than requiring users to switch between separate AI tools and systems, AI assistance can become part of the lending journey itself. 

For customers, this could create more natural and responsive interactions. For lending teams, it can provide relevant assistance and insights within their existing workflows without introducing another chatbot sitting alongside the workflow or separate application sitting alongside the lending process 

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For example, a lending specialist reviewing an application could receive an AI generated summary highlighting key information, potential anomalies and areas requiring attention directly within their existing workspace, without needing to transfer application information into a separate AI tool. 

Native AI goes beyond adding AI 

There is an important distinction between adding AI to existing software and building AI into the underlying platform. 

As financial institutions develop their AI strategies, the question should extend beyond “Does this platform have AI?”. 

The critical question to ask is “How deeply is AI integrated into the architecture, workflows, data and governance of the platform?”. 

Explore native AI with Cloudcase 

If you looking to better understand how native AI could support your loan origination strategy, then book a free AI and loan origination consultation with Cloudcase to learn more.