Enhancing Fraud Detection in Banking with Rule-Based Decision Engines

There is a never ending arms race in the banking industry. Frauds are coming up with new methods of cheating the system, and banks are defending themselves. Some banks are even hiring frauds on deep web to try and bypass their security systems.

71% of financial institutions reported a security breach via business email.

Credit card fraud is the most popular one with 448 thousand reports in 2022 and 426 thousand reports in 2023.

Fraud is a costly challenge for the banking industry. According to the 2023 Fraud and Financial Crime Report by Kroll, financial crime risks are increasing, with two-thirds of global financial institutions planning to invest more in technology to combat this threat. In 2022 alone, over $1.2 billion was stolen through fraud in the UK, highlighting the scale of the issue.

Identity theft affects more than 1 million people every year. It's declining, but it's still a big deal.

It's even harder now with AI, ML and deep fake technologies available for thieves and fraudsters. Banks need to take extra measures to prevent it.

But frauds and thieves don't have ML, AI and rule engines to themselves.

Learn how these technologies are helping prevent digital banking fraud.

The Role of Rule-Based Decision Engines in Fraud Detection and Prevention

Rule-based decision engines are crucial in detecting and preventing banking fraud. These systems can process transactions in real-time, applying pre-defined rules to flag suspicious activities immediately. For example, if a transaction exceeds a certain amount or originates from a high-risk location, the engine can halt the transaction for further investigation.

One of the significant advantages of rule-based engines is their flexibility. Banks can dynamically adjust or add new rules in response to emerging fraud trends without needing extensive IT intervention. This agility is essential given the constantly evolving nature of fraudulent tactics.

Moreover, the combination of rule-based engines with advanced technologies like machine learning and artificial intelligence enhances fraud detection capabilities. By integrating data analytics, financial institutions can analyze vast amounts of transaction data to identify patterns indicative of fraud, reducing false positives and improving the accuracy of fraud detection efforts.

Combining Technologies with Rule-Based Engines for Robust Fraud Detection

Integrating Machine Learning and AI with Rule-Based Systems

To effectively combat sophisticated banking fraud, financial institutions are increasingly combining rule-based decision engines with advanced technologies such as machine learning (ML) and artificial intelligence (AI). This integration enhances the accuracy and efficiency of banking fraud detection, leveraging the strengths of both approaches.

Machine Learning for Pattern Recognition and Anomaly Detection

There are four main types of machine learning algorithms:

Symbolist: Uses logic theory and formal systems. Includes Decision Trees, Random Forests, and Isolation Forests. Decision Trees are the only interpretable algorithm and were chosen for this research.
Bayesian: Utilizes statistical techniques and probabilistic inference. Includes Gradient Boosting Trees, Adaboost, Naïve Bayes (NB), Logistic Regression, and Hidden Markov Models. NB outperforms others but lacks interpretability.
Analogy-based: Compares transactions to past examples to detect fraud. Includes LOF (Local Outlier Factor), RecSys (Recommender Systems), SVM (Support Vector Machines), and KNN (k-nearest neighbors). LOF gives high false positives, RecSys lacks interpretability, and SVM/KNN are computationally intensive.
Connectionist: Deep Learning algorithms where the input-output path is hidden. Includes ANN (Artificial Neural Networks), FNN (Feedforward Neural Networks), CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), LSTM (Long Short Term Memory), and GRU (Gated Recurrent Units). GRU and LSTM perform best, with GRU having fewer parameters.

Machine learning models excel at recognizing patterns in vast datasets and identifying anomalies that may indicate fraudulent activity.

Unlike traditional rule-based systems, which rely on predefined rules, ML algorithms can learn from historical data and adapt to new types of fraud.

For instance, ML can analyze transaction data in real-time to detect fraud, identifying irregularities that might be missed by static rules alone.

AI for Dynamic and Predictive Analytics

AI technologies enhance banking fraud detection by providing predictive analytics and real-time decision-making capabilities. AI systems can continuously learn and improve from new data, offering dynamic rule adjustments that keep pace with evolving fraud tactics. Techniques such as anomaly detection and risk scoring help financial institutions to preemptively identify and mitigate fraud risks.

Benefits of Combining Technologies

Reduced False Positives: This ensures that legitimate transactions are not erroneously flagged, improving customer experience and operational efficiency.
Enhanced Fraud Detection Rates: Integrating AI and ML with rule-based engines allows for more accurate detection of fraudulent transactions. For example, PayPal's use of these technologies has led to a substantial reduction in their fraud loss rate while handling increased transaction volumes.
Scalability and Flexibility: Advanced AI models can handle large-scale data processing and adapt to new fraud patterns without extensive manual intervention.

Real-World Applications

Several financial institutions have successfully implemented these integrated systems.

American Express uses generative AI to produce synthetic data that mimics fraudulent behavior, enhancing their fraud detection capabilities.
JP Morgan Chase employs a combination of machine learning and and AI. They are analyzing historical transaction data to train machine learning models to identify patterns indicative of fraudulent behavior.
Citibank employs ML algorithms to uncover potential fraud rings by analyzing behavior patterns across multiple accounts. The algorithms can detect connections between seemingly unrelated accounts that may indicate coordinated fraudulent activities.
Capital One utilizes deep learning models trained on vast historical data to analyze checks and detect forgeries, altered amounts, and counterfeit checks. The models identify patterns and anomalies suggesting fraud (Soloway.tech).
HSBC uses a combination of ML and rules engines to detect credit card fraud. The rules engine provides transparency by clearly showing why a transaction was flagged, while ML models identify new fraud patterns.
Santander Bank uses trust and deny lists (rules engines) to block or allow transactions based on predefined customer attributes like card numbers or BINs, while also utilizing ML models to detect new fraud patterns.
Deutsche Bank has deployed an AI-powered fraud detection system that combines ML models with rules engines to monitor transactions in real-time and detect both known and unknown fraud patterns.

Other financial institutions should follow the leaders.

Advanced Fraud Detection Techniques

To prevent fraud and protect banking customers from being robbed financial institutions are investing colossal amounts of money. Juniper Research report pointed out that finance companies were projected to spend $9,3 billion per year on fraud prevention.

It includes advanced fraud detection techniques, such as:

Transaction Monitoring: Continuously monitoring transactions for signs of unauthorized transactions or other suspicious activities.
Two-Factor Authentication (2FA): Adding an extra layer of security to prevent unauthorized access to customer accounts.
Anti Money Laundering (AML) Measures: Implementing robust AML protocols to detect and prevent money laundering activities.
Biometric Data: Utilizing biometric authentication methods to verify the identity of customers and prevent identity theft and account opening fraud.
Fraud as a Service (FaaS): Keeping up with evolving fraud tactics that criminals offer as services on the dark web, ensuring proactive measures are in place.

Conclusion: Enhancing Security Through Technology

There are three technology-based tools that will strengthen banking fraud protection and lower the fraud risk – Artificial Intelligence, Machine Learning, and Rules Engines.

And Higson can provide you with the latter. It offers:

Fraud Detection Rules: Allows defining complex rules to identify suspicious patterns, inconsistencies, and deviations indicating potential fraud.
Integration Capabilities: Seamlessly integrates with existing systems and data sources to access relevant data for enhanced fraud detection.
Scalability: Can scale to handle increasing data volumes and processing demands as an organization grows.
Real-time Fraud Prevention: Operates in real-time to detect and respond to fraud attempts as they occur.
Continuous Improvement: Provides safeguards for testing and validating new rules without affecting live operations, enabling continuous refinement of fraud detection strategies.
All of this while helping you lower your insane AML, KYC and other compliance costs.

Let us know of your use case for Higson, and we'll let you know how we can help.

This battle will go on. Higson business rules engine can be your ally.

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