Abstract
Fraud is no longer a peripheral operational issue, as it has grown from what we used to think it was. It is a systemic risk that sits at the intersection of technology, human behaviour, regulation, and organisational trust. Over the last decade, fraud patterns have evolved faster than most detection systems, driven by real‑time payments, social engineering, automation, and cross‑border digital services. Many existing fraud solutions are either vendor‑locked, opaque, or overly dependent on black‑box machine learning models that struggle with explainability, regulatory scrutiny, and operational adoption.
This article introduces a modular, open fraud detection framework designed from the perspective of a practitioner who has operated inside high‑volume financial crime environments. The framework is intentionally open‑source‑ready, developer‑friendly, and regulator‑aware. Its major goal is to enable software engineers, data scientists, and fraud teams to collaboratively build adaptive fraud detection systems that are transparent, auditable, and resilient to emerging threats.
What is the problem with traditional fraud detection systems?
Most fraud detection platforms today suffer from one or more of the following limitations:
• Over‑centralised logic which just follows rules, models, and workflows that are tightly coupled, making change slow and risky.
• Poor explainability with the machine learning outputs that are difficult to interpret, limiting trust from investigators and regulators.
• Reactive design where systems detect fraud after loss has occurred rather than interrupting risk in real time.
• Operational disconnect where the engineering teams build models that do not align with how fraud analysts actually work cases.
• Vendor dependency, where organisations become locked into proprietary systems that restrict innovation.
I can go on and on, but a sustainable fraud strategy must treat detection as a living system, not a static tool. It should update in real time and protect the vulnerables.
Design principles of the framework
This framework is built on five core principles. I believe following this model will help to build to last, enable real-time risk assessment and stay on top of the situation; respond rather than waiting to react.
Modularity:
Each component of the fraud lifecycle should be independently deployable, replaceable, and testable. Rules engines, machine learning models, data ingestion, and investigator tooling must not be tightly coupled.
Human‑in‑the‑loop by design:
Fraud systems should augment human judgement and not replace it. Analyst feedback is treated as first‑class data, continuously improving detection logic.
Explainability and auditability:
Every alert must be explainable. If a developer cannot explain why a transaction was flagged, the system is incomplete. If an analyst cannot understand why a transaction is flagged too and determine the risk factor, then the system is not built to last.
Real‑time first:
Detection latency is risk. The framework should prioritise near‑real‑time decisioning with graceful degradation for batch analysis.
Open collaboration:
The architecture should be designed to be shared, extended, and improved by the community, encouraging peer review and innovation.
High‑level architecture overview:
The framework should be organised into seven logical layers:
• Data Ingestion Layer
• Identity & Behaviour Profiling Layer
• Rules & Heuristics Engine
• Machine Learning & Anomaly Detection Layer
• Decision Orchestration Layer
• Case Management & Investigator Interface
• Feedback, Learning & Governance Layer
Each layer will expose well‑defined interfaces, allowing teams to adopt the framework incrementally.
Data Ingestion Layer:
Fraud systems fail when they rely on narrow data. The layer should ingest structured and unstructured data from multiple sources like:
• Transaction events (payments, transfers, card usage)
• Device and session metadata
• Customer behavioural signals
• External intelligence (blacklists, consortium data)
• Investigator actions and outcomes
Key design choice: events are immutable. All downstream decisions are reproducible from raw events, enabling audits and retrospective analysis.
Identity and Behaviour Profiling:
Rather than treating transactions in isolation, the framework must build continuous behavioural profiles for entities such as:
• Customers
• Accounts
• Devices
• Beneficiaries
Profiles evolve over time, capturing baselines such as transaction velocity, geographic consistency, device stability, and interaction patterns. Deviations from these baselines often reveal fraud earlier than static thresholds.
Rules & Heuristics Engine:
Rules remain essential, especially for:
• Regulatory compliance
• Known fraud typologies
• Rapid response to emerging threats
However, rules must be:
• Version‑controlled
• Testable in isolation
• Explainable in plain language
This framework treats rules as code and policy, allowing fraud SMEs and developers to collaborate using shared definitions.
Machine Learning & Anomaly Detection:
Machine learning is introduced after strong data and rules foundations are in place. Models should focus on:
• Behavioural anomaly detection
• Peer‑group analysis
• Risk scoring, not binary decisions
Crucially, model outputs are advisory, not authoritative. Final decisions are orchestrated using multiple signals, reducing false positives and over‑reliance on any single model.
Decision Orchestration Layer:
This layer will act as the system’s brain. It:
• Combines rules, models, and contextual signals
• Applies risk thresholds dynamically
• Determines outcomes: allow, challenge, block, or refer
Decisions are policy‑driven, enabling organisations to adjust risk appetite without rewriting code.
Case Management & Investigator Experience:
Detection is wasted if investigators cannot act effectively. The framework includes:
• Clear alert narratives
• Evidence‑based reasoning
• Workflow‑aware case queues
• Performance and quality metrics
Investigators should understand why an alert exists within seconds, not minutes, as fraud/scam can happen in seconds, and there is no time to spend too long reviewing a transaction.
Feedback, learning & governance:
Every investigator action feeds back into the system:
• Confirmed fraud strengthens signals
• False positives retrain models and tune rules
• Trends inform proactive controls
Governance features include:
• Full decision audit trails
• Model performance monitoring
• Bias and drift detection
Why this framework should be open source:
Fraud is a shared problem. Open‑sourcing this framework:
• Accelerates innovation
• Improves transparency and trust
• Allows smaller organisations to build robust defences
• Creates a shared language between engineers and fraud practitioners
The value is not in hiding logic, but in raising the collective standard of fraud prevention.
Conclusion:
Effective fraud detection is not about choosing between rules or machine learning, humans or automation. It is about systems thinking because customers are not an algorithm; they are real people, and missing the point can take so many down the vulnerability route. This framework reflects years of frontline fraud operations translated into an architecture that developers can build, extend, and trust.
By making fraud detection modular, explainable, and collaborative, we move from reactive loss prevention to proactive trust engineering.
This article is intended as a foundation for an open‑source fraud detection initiative. Developers, data scientists, and fraud professionals are encouraged to adapt, extend, and challenge the ideas presented here.
About the author:
Olawale Oladoja is a tech enthusiast, fraud operations specialist and subject‑matter expert with extensive experience in financial crime prevention, fraud investigations, and risk management within high‑volume banking environments. He has led fraud system implementations, investigator training, and productivity transformations that delivered measurable reductions in fraud losses and operational inefficiencies.
His work focuses on bridging the gap between fraud practitioners and technology teams, translating frontline fraud experience into scalable, explainable, and regulator‑ready detection systems. This framework reflects a practitioner’s approach to building fraud technology that works in the real world, not just in theory.
