Fraud Detection

MOTIVATION

Enhance and optimize the existing static ML model for fraud detection

Fraud detection and prevention are inevitable as more organizations adopt the online transaction mode to generate invoices, receipts, and e-payments. Fraud is a financial loss and a significant compliance risk, and a dent in the company’s reputation. Thus, it is vital to building a robust risk management mechanism in Organizations to identify and prevent any fraudulent transactions. An increasing market proportion for Fraud Detection software is a valid testimony.

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The Problem Canvas

Fraudulent payments in e-commerce marketplaces, counterfeit invoices, receipts haunting various organizations, financial frauds, and money laundering in the BFSI sector make fraud detection one of the biggest emerging challenges. Safe and shady transactions have their specific characteristics based on a typical behavioral pattern.

Rubiscape builds Machine Learning based Fraud Detection models that employ Data Analysis and Pattern Recognition to identify a Fraud.

  • The model analyses any financial transaction data to accept, reject, or further scrutinize it to establish an automated decision-making mechanism.
  • No Pre-trained Model, Customized Classification model has to be built
  • With the columns of the dataset, it won’t be easy to generate stories out of it
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The Data Question
  • Identify non-obvious fraud patterns and monitor operations to spot potential fraudsters when they’d otherwise remain unexposed
  • Identify complex patterns to detect behavioral changes of perpetrators

Solution Canvas

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Approach:

Rubiscape assists fraud analysts to build and validate a model that can predict whether a payment is fraudulent. This helps them to detect more accurately both fraud and non-fraud transactions. This should help them inculcate an open and flexible mind regarding people’s activities on the internet.

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Toolset:

RubiStudio – Data Exploration, Data Joiner, Merger, Statistical Hypothesis, Code Fusion
RubiML – Clustering models, Silhouette Score – Code Fusion
Rubisight – Story and Dashboard.

Get started

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Skillset:

Machine Learning
Domain Knowledge
Data wrangling
Data Visualisations

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Dataset:

The given dataset contains information related to the amount involved during each transaction and time.

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OUTCOMES

Business Impact

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  • Increased capacity and capability to handle a variety of fraud-based data from multiple sources
  • Automated fraud data tasks – cleansing, formatting, parsing
  • Data visualizations convey exactly how fraudulent activity may occur in the future.
  • Faster pilots for new use cases on sandbox and release in production in just a few weeks
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