Financial Services

Credit Card Fraud Detection Using RapidCanvas AutoAI

Elevate protection against credit card fraud by analyzing patterns, detecting anomalies, and safeguarding transactions using RapidCanvas AutoAI

Why You Need AI to Fix Fraud Detection

With traditional fraud detection methods, financial institutions face numerous challenges such as high fraud losses, manual and inefficient fraud review processes, excessive false positives, and difficulty keeping up with new fraud patterns.
High Fraud Losses
Financial losses from payment fraud remain a major problem. Traditional methods fail to detect many fraudulent transactions.
Manual Processes
Reviewing transactions for fraud is still done manually in many institutions, which is slow, expensive and error-prone.
False Positives
Legacy systems often flag legitimate transactions as suspicious, leading to false positives, wasted time and poor customer experience.
Evolving Fraud Patterns
Criminals constantly find new ways to commit fraud. Traditional systems cannot quickly adapt to new fraudulent behaviors.

Stop Fraud Losses in Their Tracks with RapidCanvas AI

Elevate protection against credit card fraud by analyzing patterns, detecting anomalies, and safeguarding transactions using RapidCanvas AI solutions

Data Collection

Gather labeled transaction data to train machine learning models.
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Data Preprocessing

Clean and preprocess data to prepare it for modeling.
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Model Training

Train and test ML models on the data to detect anomalies and fraud patterns.
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Model Integration

Integrate the models into the transaction screening system.
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Automated Screening

Use the system to automatically screen transactions in real-time.
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Adaptive Learning

Continuously retrain models on new data to adapt to new fraud methods.
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Equip with AI-Powered Fraud Detection and Stay Ahead of Evolving Fraud Threats

RapidCanvas AI solution delivers powerful benefits for fraud detection and helps businesses empower their defense
Reduced Losses
Stop more fraudulent transactions, leading to dramatic reductions in fraud losses.
Improved Accuracy
Advanced AI has fraud detection rates over 90%, far better than manual processes.
Fewer False Positives
More accurate AI models reduce false positives compared to rules-based systems.
Adapt to New Fraud
Models are retrained on new data so they quickly adapt to new fraud methods.

Some of our results

Hear from Our  Customers

RapidCanvas has been an outstanding partner in our digital transformation journey. They showed exceptional speed of execution in understanding our business, gathering and analyzing data, and building actionable insights and data apps. Their data science team was able to quickly grasp the nuances of our business and deliver meaningful insights.
Don Curran
CTO, Shield Leasing

Why You Need AI to Fix Fraud Detection

High Fraud Losses
Manual Processes
False Positives
Evolving Fraud Patterns
High Fraud Losses
Manual Processes - Fraud
False Positives
Evolving Fraud Patterns
High Fraud Losses
Manual Processes - Fraud
False Positives
Evolving Fraud Patterns
High Fraud Losses
Manual Processes - Fraud
False Positives
Evolving Fraud Patterns
High Fraud Losses
Manual Processes - Fraud
False Positives
Evolving Fraud Patterns
High Fraud Losses
Manual Processes - Fraud
False Positives
Evolving Fraud Patterns

Why customers choose RapidCanvas for AI-led credit card fraud detection

See Rapid Time-To-Value
Address unique business needs without starting from scratch; state your business problem and the AutoAI discovery process will generate a matching AI solution within hours.
Build Expert-Led AI
Leverage the industry knowledge of data science experts, as required, to validate against industry benchmarks and ensure optimal AI solution performance
Access Actionable Business Insights
Create visual, interactive data apps, dashboards and reports to showcase business KPIs and outcomes, and monitor business performance
Use An End-To-End AI Solution
Achieve an end-to-end AI solution with an out-of-the-box setup for all steps from data orchestration, data preparation, transformations, model building and testing, through to model deployment and data apps