How a Fintech Platform Pushed Fraud Precision to 99%

A fintech platform flagged a heavy share of document applications for manual review, with false positives piling on escalations. A purpose-built ML model now clears them at 99% precision — cutting manual review ~50%.

99%

Hit 99% fraud-detection precision

2–4 months

Implementation Time

Jay Singh

CEO & Founder

Casper Studios

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The Challenge

A large-scale fintech/proptech platform processing hundreds of document applications a day flagged a large share for human review. Manual verification consumed reviewer time, and existing tools produced high false-positive rates that drove unnecessary escalations — all without the option to add headcount.

What They Built

Casper built an interpretable ML classifier trained on 65+ engineered document-metadata features, using a Pass/Fail/Caution architecture of calibrated one-vs-rest models. An 'AI Assist' layer gives reviewers plain-English explanations to build trust, and a feedback-driven ETL pipeline captures human decisions to continuously retrain the model. After evaluation, the team deliberately chose traditional ML over LLMs for the core detection task.

Casper started by testing whether an LLM could do the job, and concluded a purpose-built, interpretable model would be more accurate and defensible. They engineered 65+ features from document metadata and trained calibrated one-vs-rest classifiers into a Pass/Fail/Caution architecture, so borderline cases route to review rather than auto-failing. To win reviewer trust, an 'AI Assist' layer explains each decision in plain English. A feedback-driven ETL pipeline captures every human decision and feeds it back into retraining, so the model keeps improving against new fraud patterns. The result reached 99% precision in production at a 1.2% false-positive rate — roughly ten times better than the LLM and baseline tools they benchmarked — and cut the manual escalation workload by about half.

AI Role

AI makes the fraud call and explains it. A purpose-built interpretable classifier scores each application, routing borderline cases to review instead of auto-failing, with an 'AI Assist' layer explaining every decision in plain English.

Infrastructure

Integration Points

Feedback-driven ETL pipeline captures reviewer decisions to retrain the model; the AI Assist explanation layer surfaces inside the reviewer workflow.

Impact

99% Precision

In production fraud detection.

1.2% False-Positive Rate

Roughly 10x better than the LLM and baseline tools benchmarked.

~50% Less Manual Review

Escalation workload roughly halved.

Technology Utilized

Implementation Complexity

Cost Reduction

Risk & Compliance

Best Fit For

Fintech, lending and proptech platforms doing high-volume document verification, where false positives drive costly manual escalations and interpretability matters for trust and compliance.

Jay Singh

CEO & Founder

Casper Studios

CEO and co-founder of Casper Studios, a product studio helping companies design, build, and integrate AI-powered products.

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Industry:

Financial Services

Business Function:

Operations

Legal & Compliance

Company Size:

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

AI Pattern:

Decision Support & Scoring

Document Processing & Extraction

Value Type:

Cost Reduction

Risk & Compliance

AI Model:

Custom interpretable ML (calibrated one-vs-rest classifiers); deliberately not an LLM

Frequently Asked Questions

How did a financial services platform push fraud-detection precision to 99% with decision support and document processing?

The financial services platform replaced high-false-positive tools with a purpose-built, interpretable ML classifier trained on 65+ engineered document-metadata features, using a Pass/Fail/Caution architecture so borderline cases route to review. An 'AI Assist' layer explains each decision in plain English and a feedback-driven pipeline retrains the model on human decisions. It reached 99% precision in production and cut manual review by about half.

What AI model and approach did the financial services platform use?

The team deliberately chose custom interpretable ML — calibrated one-vs-rest classifiers, not an LLM — after benchmarking showed it was more accurate and defensible. It used 65+ features engineered from document metadata, a Pass/Fail/Caution architecture, an explanation layer for reviewers, and a feedback-driven ETL pipeline for continuous retraining.

What results did the financial services platform achieve?

Three outcomes: 99% precision in production fraud detection, a 1.2% false-positive rate (roughly 10x better than the LLM and baseline tools benchmarked), and roughly 50% less manual review as the escalation workload was about halved.

How long did the engagement take?

Time to results was in the 2–4 month range.

Who is this fraud-detection approach best for?

Fintech, lending, and proptech platforms doing high-volume document verification, where false positives drive costly manual escalations and interpretability matters for trust and compliance.