Fraud Detection Solutions for Finance
Fraud detection
challenges in finance
- A limited number of proven fraud cases creates imbalanced datasets that cannot cover all possible fraud variations.
- Decision-making errors, both excessive verification and fraud oversight, introduce additional organisational risks.
- False positives in detection systems decrease customer loyalty, particularly during frequent financial monitoring checks.
- Advanced mimicry techniques employed by fraudsters are designed to resemble legitimate user behaviour closely, increasing detection complexity.
- The requirement for human-in-the-loop approaches rather than fully automated solutions makes specialist verification necessary after AI processing.
- Significant unexplained variance in models due to incomplete consumer data makes accurate prediction and classification of future actions challenging.
Our expert thinking
Our cross-functional team of software architects, data scientists and cybersecurity experts evaluate your digital ecosystem, transaction flows, and access points to find potential vulnerabilities, including legacy systems, integration points, and business processes. We also analyse historical data to detect fraudulent patterns in your organisation's risk profile. At this stage, we'll be able to understand your risk exposure profile and the critical assets that may attract threat actors.
Using the results of the analysis phase, we create a fraud detection strategy that integrates with your existing systems and workflows. By aligning fraud detection solutions with your current technology stack and business processes, we help you establish a robust fraud prevention workflow with minimal disruption to your operations.
Using custom machine learning models and AI algorithms, we can accurately identify patterns of fraudulent activities while reducing false positives that can impact customer experience. With agentic AI (model-based agents) analysing transaction volumes across multiple channels in real time, enabling immediate intervention when potential fraud is detected. We retrain models with new data, so the solution adapts to emerging fraud tactics, providing sustainable and scalable protection.
Our integrated approach to fraud detection and prevention
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Data collection and feature engineering
We collect and analyse data across multiple channels to gain insights into an organisation's transactional patterns and customer behaviours. Feature engineering converts these raw inputs into meaningful variables that machine learning models can utilise for pattern recognition, identifying indicators of fraudulent activities that might otherwise go unnoticed.
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AI-based fraud detection and prevention
We develop custom machine learning models that instantly analyse thousands of transaction variables, enabling real-time fraud detection without disrupting the customer journey. With this AI-powered approach, financial organisations can reduce fraud losses while improving operational efficiency with fewer manual reviews required. We address any integration challenges with legacy systems through flexible architecture design.
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Anomaly detection and transaction monitoring
By implementing custom anomaly detection tools, you can create individual behavioural profiles for each account, which helps identify patterns of fraudulent transactions while minimising false positives. Continuous transaction monitoring across payment channels offers comprehensive visibility into potential threats, enabling teams to address suspicious activities before financial crime occurs.
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Integration with existing systems and tools
We design solutions with REST interfaces that integrate seamlessly with your existing systems without disrupting critical financial processes. We enhance your identity verification frameworks with additional risk intelligence layers that strengthen protection without requiring complete infrastructure replacement. This integration strategy ensures that transaction data flows smoothly between your fraud detection systems and tools, creating a unified ecosystem.
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Addressing false positives and customer friction
Our risk-based authentication approach effectively distinguishes between fraudulent transactions and legitimate unusual activity, helping us minimise false positives that frustrate customers. Machine learning algorithms continuously refine risk scores, or fraud probability scores based on actual outcomes to ensure accurate results. We maintain transparent data governance and adhere to all compliance and regulatory requirements during implementation, so we can successfully identify fraud patterns while safeguarding sensitive information.
Why choose ELEKS as your partner for fraud detection in finance
Our team of software developers, cybersecurity experts and data science specialists combines extensive experience in developing sophisticated fraud detection models and delivering complex financial security solutions. We've been a trusted software partner for financial institutions, insurance companies, and banking enterprises for over thirty years, giving us deep insight into transaction processing workflows and the unique security challenges facing modern financial operations.
Security and regulatory compliance form the foundation of our fraud detection approach. Drawing on our experience delivering cybersecurity services to global enterprises, we implement robust protective measures while ensuring strict adherence to financial regulations throughout implementation. Our commitment to data protection and compliance makes us a reliable partner for deploying advanced fraud detection systems in highly regulated financial environments.
