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Hadoop-based Fraud Detection for Banking Institution

Services Covered:

Big Data Analytics and Business Intelligence • AI and Machine Learning
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Industry
Industry

Financial Services

Duration
Duration

45 months

Budget
Budget

10K - 49K

Start Date
Start Date

1 June 2018

Team Size
Team Size

1-5

Engagement Model
Engagement Model

Onshore

Project Capability Score (PCS)

gauge meter
75/100
Good

Project Capability Score (PCS) estimates how capable Profinit is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

Ceska sporitelna

map Czech Republic

Project Summary

Profinit provided consulting and Hadoop development services to a banking institution (Ceska sporitelna) to integrate payment transaction sources and build analytical models for fraud detection. They implemented data integration into a Hadoop-based data mart, built analytical models using Spark and Python, and integrated the models into an online transaction monitoring front-end. The engagement led to a significant decrease in fraudulent transactions, improved client NPS, effective self-managed teams led by senior consultants, and cost savings.

Key Challenges

  • Implement real-time anti-fraud monitoring for payment transactions
  • Integrate multiple payment transaction sources into a centralized big-data platform
  • Build analytical models to detect fraudulent or suspicious transactions
  • Integrate models into an online transaction monitoring front-end

Project Deliverables

  • Data integration pipelines for payment transactions
  • Hadoop-based data mart
  • Analytical fraud detection models
  • Integration of models into online transaction monitoring front-end
  • Governance and analytical model lifecycle management

Project Solution

Profinit provided consulting and development expertise in Hadoop technologies, proposing a 5-person expert team. They prepared data integration for payment transaction sources, collected relevant data in a Hadoop-based data mart, developed analytical fraud-detection models using Spark and Python, and integrated those models into an online transaction monitoring front-end. Profinit also assisted with overall governance and analytical model lifecycle management.

Project Outcome

  • Significant decrease of fraudulent transactions
  • Improved client NPS ratio
  • Effective workflow with self-managed teams led by senior consultants and architects
  • Cost savings compared to original estimation

Platforms

  • WebWeb

Tech Stack

  • Apache HadoopApache Hadoop
  • Apache SparkApache Spark
  • PythonPython

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“The banking institution saw a significant decrease in fraudulent transactions and an improved client NPS ratio. Profinit's workflow was absolutely effective. They worked truly well as self-managed teams led by senior consultants and architects. Their services were also highly cost-effective.”

Martin Gerneš

Tribe lead

Note: This endorsement is based on publicly available client feedback from external review sources.

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