Modern Fraud Detection Methods: A Complete Guide to Implementing IBM Safer Payments

Today, the financial world faces unprecedented challenges. The growth in data volumes, the emergence of new alternative payment methods, and the increasing sophistication of professional criminals require businesses to act quickly. Fraud schemes change rapidly and shift between different payment channels. To meet these demands, businesses need systems that combine speed, support for multiple channels, and advanced machine learning. One solution that can help in this situation is IBM Safer Payments.

Why Are Models Driven Exclusively by Data Science Ineffective?

The use of machine learning (ML) and artificial intelligence is growing rapidly, but relying solely on them is ineffective. Experts identify five main challenges faced by “pure” artificial intelligence:

1. Data quality: Even if you have access to a massive amount of transaction data, this data is often incomplete, inaccurate, or contradictory. Training on such data leads to unreliable algorithm results.
2. Data availability: Fraudulent transactions occur much less frequently than legitimate ones. Because of this, it can be very difficult to collect enough “bad” examples to train the system properly.
3. Model complexity: Machine learning algorithms often become “black boxes.” They are too complex to understand, making it difficult to interpret the results and explain to a customer why their legitimate transaction was flagged as fraudulent.
4. Overfitting: This is a situation where the model “memorizes” old training data but proves ineffective in real-world conditions with new data. The result is a high rate of false positives, where the system blocks funds from honest customers.
5. High cost: “pure” machine learning is a very expensive investment. Companies have to spend significant resources on collecting and cleaning data, continuous training, deployment, and maintenance of such models.

So what’s the solution? On the other hand, traditional (human-created) rules result in a low rate of false positives and adapt quickly to threats, but they are poor at detecting entirely new types of fraud.

That’s why experts argue that only a combination of ML/AI (machine learning) assessment models and clear rules delivers the best results. This hybrid approach is the foundation of IBM Safer Payments.

Meet IBM Safer Payments

IBM Safer Payments is a solution created by fraud prevention professionals for their peers. It protects all non-cash payments in real time.

What does the system protect? The platform supports all major channels: from card issuance and online/mobile banking to real-time payments, SWIFT transfers, and commercial transactions.

Key features of the system:

• Behavioral profiling: The system analyzes each customer’s habits to distinguish their typical behavior from anomalous activity.
• Real-time decisioning: It analyzes a transaction and makes a decision (block, allow, or send for review) in milliseconds.
• Simulation (“What-if”): a unique feature that allows testing new rules on historical data before deploying them in production to avoid inconveniencing legitimate customers.
• Automatic rule generation: the system can analyze data on its own and propose rules that are most effective at filtering out fraudsters.

The Flexibility of Artificial Intelligence in IBM Safer Payments

The system allows you to use both its own built-in algorithms and external models:

• Internal models: The platform has a built-in Random Forest algorithm (based on the OpenCV library), which can be quickly configured and trained within the system.
• Support for external models (PMML): You can train your own models (such as neural networks or decision trees) in other applications and upload them to IBM Safer Payments using the PMML standard.
• Using Python: For the most complex non-standard tasks, text processing, or calling full-fledged external AI models, you can use integration with the Python programming language.

8 Tips from Experts on Machine Learning (ML)

For the system to work perfectly, it’s important to follow these rules:

• Use a variety of algorithms, since none of them is perfect.
• Use a large dataset to improve accuracy.
• Combine ML with human judgment. Human expertise should always oversee the machine’s operations.
• Train the model regularly, as fraudsters are constantly changing their tactics.
• Train models for specific, narrow tasks. General “one-size-fits-all” models perform significantly worse.
• Create high-quality profiling attributes. Human-defined parameters (such as typing speed or sequence analysis) are more important than the type of ML algorithm itself.
• When using Python, test the system under full load to ensure there are no delays.
• Train the model on data directly from IBM Safer Payments to avoid errors in data formats.

Real-world implementation results

Global financial giants have already proven the effectiveness of this approach:

• STET: saves $230 million per year by preventing fraud.
• SIBS: reduced financial losses by 72% while simultaneously reducing false customer blocks by 90%.
• FIS: reduced losses from fraud by 70%.
• JPMorgan Chase and American Express: use advanced machine learning to analyze user profiles, historical data, and behavior to accurately detect anomalies while minimizing the blocking of legitimate customers.

Why Choose the SOLIDITY Team?

SOLIDITY is a professional team with over 10 years of experience in financial security.

We have over 20 certified specialists and provide a full range of services:

• Developing a comprehensive anti-fraud strategy and selecting the right solution.
• Protecting financial data and automating compliance requirements (KYC, AML).
• Developing and training machine learning models for your business.

We’ll help you implement IBM Safer Payments so your customers stay safe and your business grows without financial losses. Contact us for a consultation!