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Fraud Detection in Banking Project

MNDA

Enterprise/Corporation
Company Type
NY, United States
Location
Project work
Engagement Model
11 - 25 people
Team Size
12 - 24 Months
Duration
$1M - $5M
Budget

About

ML-based solution developed by Innowise to detect transaction anomalies in the banking industry. Innowise has developed an ML-powered system that monitors digital transactions and detects suspicious or fraudulent behavior. Our customer is a large commercial bank with a network of branches across the country, offering deposits, loans, and other services.

Challenge, approach, and impact

Challenge

Banks prioritize account holder satisfaction and security, managing accounts, investments, and liquidity daily. Yet, sophisticated fraud threatens customers and the industry. Traditional manual rules-based systems are failing. A major U.S. bank turned to Innowise for an ML solution to detect financial fraud. As transactions grew, malicious activities risked safety and reputation. Their existing AML system had high false positives, vulnerable to account takeovers and payments

Solution

Innowise recommended integrating an ML-powered extension into the banking ecosystem to analyze big data volumes and safeguard funds from malicious activities. Account holders' transactions are analyzed, and alerted if any uncharacteristic, suspicious, or fraudulent behavior is detected. Using deep learning in fintech algorithms, our project team analyzed vast amounts of data to detect abnormalities that could indicate fraud.

How we built

Banking & Trading Platforms
Data Analytics and Visualization
AI & ML Solutions
Web Apps
Website Design
Enterprise Software
Integrations

Testimonials

Anonymous

Innowise Sp. z o.o

Verified Testimonial

As Team Lead on Innowise's ML-based fraud detection project for a major U.S. bank, I led our team through a complex challenge: building a robust system that accurately flagged fraud across diverse user profiles, including new accounts with limited data. We delivered exceptional results—optimized metrics via domain-specific features, real-time threat alerts with tiered responses (5% auto-approve, 6-70% extra verification, 80%+ auto-reject), and full model explainability for seamless UX. Using Scrum with 3-week sprints and close client collaboration, we completed the project on time, significantly enhancing the bank's security and reputation.

Team structure

Client team

David's avatar

David

Project Manager

Daily point of contact

The client stakeholders were working closely with the team at Innowise Sp. z o.o

Agency team

1 x Project Manager's avatar

1 x Project Manager

Production

1 x Business Analyst's avatar

1 x Business Analyst

Production

2 x Frontent Engineer's avatar

2 x Frontent Engineer

Production

2 x Backend Engineers's avatar

2 x Backend Engineers

Production

3 x ML Engineers's avatar

3 x ML Engineers

Production

2 x Data Engineers's avatar

2 x Data Engineers

Production

1 x UI/UX Designer's avatar

1 x UI/UX Designer

Production

1 x QA's avatar

1 x QA

Production

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