Pain Point
Fixed rules missed edge cases
The existing automation handled applications that followed common patterns. Applications outside those rules required a separate manual assessment.
Case Study - Multi-Product Fintech
An international fintech used the AI Data Room to assess high-value onboarding applications. After deployment, manual case reviews fell by 70 percent and time-to-yes shortened.

The Impact
These results apply to the onboarding workflow and operating context described in this case study.
70%
Fewer manual case reviews
Shorter
Time-to-yes for high-value customers
The Problem
An international fintech operating multiple products encountered onboarding delays as it expanded into new jurisdictions.
Pain Point
The existing automation handled applications that followed common patterns. Applications outside those rules required a separate manual assessment.
Pain Point
Roughly 10 percent of high-value applications got stuck in manual review, frustrating customers and delaying revenue.
Pain Point
Adjusting risk parameters or changing data providers required engineering support. The onboarding team could not update the workflow directly when requirements changed.
The Solution
The deployed workflow combined document analysis, configured risk appetite and recommendations while keeping the final onboarding decision with the company’s analysts.
Intelligent Orchestration
The orchestration workflow prepared applications that did not fit the existing pass or fail rules. Analysts received the case information for review before making a decision.


The AI Data Room
The AI Data Room analysed passports and bank statements against the company’s configured risk appetite. It returned recommendations and supporting checks for analysts to verify.
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The AI Data Room supports document assessment workflows for onboarding, due diligence and underwriting in Risk Llama.
Risk Llama
See how Risk Llama applies configurable checks to onboarding documents and returns recommendations for analysts to verify.
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