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TRANSFORM Solutions stabilized their decision engine and protected high-value approvals.
A mid-size UK FinTech company was facing a growing number of inaccurate loan and application decisions due to an unstable AI-driven review engine. As volumes increased, the system began misclassifying documents, rejecting valid applications, and pushing inconsistent metadata into downstream workflows. These errors were creating friction for customers, exposing the company to compliance risks, and forcing internal teams to reprocess a large portion of the queue manually.
TRANSFORM Solutions deployed a Human-in-the-Loop (HITL)review layer built specifically to correct AI misreads, stabilize exception handling, and restore data confidence. Within 72 hours, the FinTech saw significant performance improvements, preventing over $150k in annual losses from wrongful rejections and manual rework.
The client operates a digital lending and financial verification platform focused on small-business and consumer finance. Their model relies heavily on automated document evaluation, identity checks, and financial data classification. As customer adoption increased, so did the complexity of incoming files, bank statements, income slips, ID documents, multi-page PDFs, handwritten additions, and inconsistent upload quality.
The existing AI engine was designed for clean, structured financial data. Real-world variation quickly exceeded those limits.
The system began failing at multiple points in the workflow:
Financial documents were being misinterpreted due to inconsistent formatting and image quality.
Valid applications were being sent to rejection queues without human review.
The AI model lacked an exception-handling layer, causing errors to repeat across batches.
Metadata inconsistencies created conflicts, especially when files came from third-party platforms.
These failures increased customer complaints, forced agents into constant cleanup cycles, and undermined confidence in the decision engine.
The consequences were immediate and expensive:
Incorrect denials jeopardized customer trust and long-term retention.
Compliance and audit exposure increased due to inaccurate decision logs.
Approval cycles slowed, affecting revenue and partner relationships.
Leadership could no longer rely on reporting accuracy for risk decisions.
Manual reprocessing created unexpected operational costs and backlogs.
Without a stable verification engine, the business risked slowing growth at a critical stage.
TRANSFORM Solutions introduced a structured HITL operations layer designed to work alongside the existing AI system, not replace it.
1. HITL Exception Review Layer
Human reviewers validated, corrected, and classified documents that the AI struggled with.
2. AI Misread Correction
Analysts reviewed common misinterpretation patterns and corrected them at the source.
3. Metadata Normalization
We standardized fields to eliminate recurring inconsistencies in downstream workflows.
4. Workflow Stabilization in 72 Hours
A rapid-deployment pod was activated to bring the decision workflow back to predictable accuracy.
Before
Unstable automation, high false-rejection rates, manual cleanup cycles, rising audit risk, and customer dissatisfaction.
After
Accurate approvals, consistent document interpretation, reduced manual work, and renewed leadership confidence in the decisioning system.
The FinTech achieved a 38% reduction in false rejections, a 27% improvement in decision accuracy, full workflow stabilization within 72hours, and prevented $150k+ in annual financial losses.
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