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Cloud & Data · Fintech

Fintech Risk Monitoring Dashboard

Data dashboard with alerting, rule-based risk scoring, and AI-generated analyst summaries.

CLOUD & DATA
Real-time
anomaly detection (was T+1)
90 min
saved per analyst per day
100%
risk rules version-controlled
The challenge

What the client was facing

A fintech operations team needed real-time risk visibility across multiple product lines — and their existing setup was a Frankenstein of spreadsheets, BI tools, and Slack alerts that nobody fully trusted.

What we built

The solution

  • Unified data pipeline ingesting from transaction and ledger systems
  • Rule-based risk scoring engine with auditable, version-controlled rules
  • Real-time anomaly alerting with confidence-tiered routing
  • AI-generated analyst summaries for daily and weekly risk briefings
In production

What it looks like

Illustrative screens — actual client UI, branding, and data redacted under NDA.

Fintech Risk Monitoring DashboardOverview82%Accuracy3.2kItems12Today4.7ScoreTrend
Fintech Risk Monitoring Dashboard — analytics 1 2 3 4 5 6 7 8class ClaimsExtractor: def __init__(self, llm, schema): self.llm = llm self.schema = schema def extract(self, document): prompt = self.build_prompt(document) raw = self.llm.complete(prompt) return self.schema.validate(raw)AI suggestion ▸
AI-first delivery angle

Why AI-first mattered here

AI summarises and contextualises — it does not score risk. The scoring engine is deterministic, auditable, and explainable to regulators. Senior engineers drew that line and held it.

Technologies

Stack

Python Snowflake dbt FastAPI OpenAI AWS

Have a similar problem to solve?

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