Data Science

Build an AI-Powered Fraud Detection System for Banking Transactions

Simulate four weeks inside a bank's Fraud Risk & Intelligence team, building transaction baselines, investigating alert surges, diagnosing recurring fraud patterns, and presenting your findings to the Chief Risk Officer.

Certified by

Role

Data Analyst

Industry

Education

Menterns at work

Level

Intermediate

Time Commitment

60 Hours

Duration

30 days

Tools you’ll learn

Here’s What You Work On

About the Company

You are stepping into the Fraud Risk & Intelligence team of a mid-sized retail bank. The bank processes millions of transactions daily across five customer segments - Retail Savings, Retail Current, SME Business, Premium Banking, and Digital-Only. Over the past quarter, three patterns have emerged that the team cannot fully explain.

You will work through them in sequence - building the baseline, investigating the alert surge, diagnosing the ATO recurrence, and finally synthesising everything into a fraud intelligence brief for the Chief Risk Officer.

Explore

the following work techniques

Fraud Data Analysis

Pattern Recognition & Hypothesis Testing

Model Explainability (SHAP)

Alert Investigation & SLA Tracking

Root Cause Analysis (5-Whys)

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Bridging the gap

Over the past quarter, three patterns have emerged that the team cannot fully explain:

The Digital-Only segment has seen a sustained rise in fraud rate over 30 days, now approaching the critical threshold. The team does not yet know if this is a new fraud pattern establishing itself or a deterioration in detection logic.
A surge in fraud alerts during Q3 has revealed that 96% of Priority 1 alerts — the highest-value, fastest-response tier — are breaching their 5-minute acknowledgement target. The cost in delayed fraud containment is significant and growing.
Account Takeover fraud is recurring in the same customer segments despite repeated investigations. The team suspects device intelligence gaps and a specific overnight attack window, but has not yet proven it with data.

Apply

the following skills

Data Analysis

Data Visualization

Root cause analysis

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Expected output

Across four components, you will produce ten portfolio deliverables that span the full fraud analyst workflow:

A colour-coded Fraud Baseline Report and written interpretation from Component 1;
An Alert Analysis Dashboard, SLA Breach Register, and written summary from Component 2;
A Feature Impact Summary, 5-Whys root cause writeup, and Recommendation Memo from Component 3; and
An Executive Summary, unified Fraud Intelligence Dashboard, and a 5-Slide Executive Briefing Deck for the Chief Risk Officer from Component 4.

Together, demonstrating your ability to move from raw transaction data all the way through to a Board-ready fraud intelligence brief.

Create

the following deliverables

Fraud Baseline Report

Alert Analysis Dashboard

SLA Breach Register

Fraud Intelligence Dashboard

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What you’ll need before starting

Basic proficiency in Excel or Python (pandas) — you should be comfortable loading a CSV, filtering rows, and computing averages. You do not need to be a programmer, but you must be able to work with tabular data without hand-holding.
Familiarity with at least one visualisation tool — Power BI, Tableau, or Python (Plotly/Matplotlib). You don't need to be an expert, but you should have built at least one chart before.

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Build an AI-Powered Fraud Detection System for Banking Transactions

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