Enhancing Fraud Detection in Banking mathew Assessment

Assessment

Introduction

The banking sector is facing increased financial loss and loss of reputation due to the rapid increase of highly sophisticated financial fraud (fig 1). Traditional fraud detection techniques, mainly rely on rule based human oversight and linear information perception, these techniques are unable to address the networked crime nature. The proposal below mainly aims to solve the issues in a three-step analytical solution to the problem, using enhanced analytics to improve the working performance of fraud prevention by a significant margin.

 Depth of Analysis

This project targets the Australian banking sector in which operators like Commonwealth Bank, ANZ, NAB, Westpac, and HSBC are highly in charge of fraud that are now sophisticated cybercrime syndicates and identity theft. The root of the problem is the imbalance between the abilities of existing systems and the networked character of fraud, which comes in the form of unobservable networks of accounts, devices, and people that are interconnected. The big banks are now investing heavily in ai and graph databases

SWOT analysis

• Strengths
  • Access to large amounts of structured and unstructured customer transaction data.
  • Strong regulatory support for fraud detection initiatives.
  • Established customer trust and brand recognition.
• Weaknesses
  • Legacy systems with poor data integration.
  • High rates of false positives leading to customer dissatisfaction.
  • Dependence on static, rule-based fraud detection systems.
• Opportunities
  • Adoption of graph databases to model complex transaction relationships.
  • Integration of machine learning for real-time fraud detection.
  • Regulatory compliance efficiency
• Threats
  • Increasing sophistication of fraud schemes.
  • Cybersecurity threats and reputational damage.
  • Resistance to tech adaptation

Importance and Relevance

The approach used in this research is grounded in proven analytical approaches. Analytics for fraud detection has been historically developed from static rule-based systems to sophisticated machine learning and graph analytics techniques that are used today Rule-based systems are static and cannot adapt to new patterns, which was highlighted by Gandomi and Haider (2015). The step further towards predictive models like Logistic Regression and Decision Trees improved capabilities in detecting suspicious transactions (Marr, 2016). Above all, the advent of graph analytics has been termed as being important, with graph databases being best able to take advantage of the interconnectedness of transactions (Data in Brief, 2024) this enables detection of more complex fraud rings and money mules networks that are unpredictable to traditional models.

Descriptive Analytics

The first of these phases, Descriptive Analytics, attempts to discover past patterns of fraud using graph analytics. This will happen using SAS Viya visualization such as frequency distributions and network graph. This can be used to reveal usual transaction clusters. Traditional databases struggle to capture indirect links, so graph database are applied to map the relationships between customers, accounts and devices.

Predictive Methods

The second stage, Predictive Analytics. This will create and issue a supervised machine learning model for watching real time and scoring the frauds accordingly. Due to how highly performing and interpretable they are, a Logistic Regression model or a Decision Tree will be employed (fig2). Logistic regression will estimate the probability of fraud based on features such as transaction size, device ID and frequency. Decision tree will generate simple if/else rules, making it easy and practical for business users to understand. Models will be trained against both regular transactional data as well as important new graph-derived features. Deliverable for this phase is a real-time scoring model that imposes a fraud probability on new transactions so that instant classification can be done. 

 Prescriptive Methods

The final phase, Prescriptive Analytics determines the optimal actions. Fraud detection involves a trade-off false positive reduces customer satisfaction whereas the false negatives will cause a financial loss. An optimization model will be used to recommend the optimal response strategy for a high-risk transaction. A simulation model will simulate the whole fraud detection process, testing a number of response strategies, including Hard Block/Decline (for most likely), Request Customer Verification (for medium risk), and Flag for Manual Review (for doubtful scores). It will look to run the simulation to find the best combination of rules and thresholds in a bid to reduce the total cost, considering financial loss, operational costs, and customer friction. The simulation model will test the system robustness under different conditions.

Brief Summary of Assessment Requirements

Assessment type & purpose
Create a practical, end-to-end analytical proposal (and supporting artefacts) that improves fraud prevention for large Australian banks by moving beyond static, rule-based approaches to a three-phase analytics solution: Descriptive (graph) → Predictive (ML scoring) → Prescriptive (optimised response & simulation). The project must show technical feasibility, business relevance and measurable impact on false positives/negatives and cost.

Core deliverables

• Problem statement and sector context (Australian retail banks: CBA, ANZ, NAB, Westpac, HSBC).
• SWOT and stakeholder analysis.
• Descriptive analytics plan using graph analytics (tools/visuals) to expose fraud networks.
• Predictive modelling approach (feature set, model choice e.g., Logistic Regression, Decision Tree), training/validation plan and real-time scoring design.
• Prescriptive analytics: optimisation strategy and simulation of response policies (hard block, request verification, manual review) to minimise total cost (fraud loss + operational/customer friction).
• Implementation considerations: data, infrastructure (graph DB, streaming scoring), governance, explainability, and regulatory/compliance notes.
• Evaluation metrics and success criteria (precision/recall, cost reduction, operational load).
• Final recommendations and rollout roadmap.

Key pointers to cover

• Why legacy rule systems fail for networked fraud.
• How graph databases reveal indirect relationships (accounts/devices/people).
• Feature engineering including graph-derived features.
• Model interpretability and business usability.
• Trade-offs between false positives and false negatives and how optimisation balances them.
• Simulation to stress-test thresholds under varying attack/signal conditions.
• Practical delivery: tech stack (e.g., SAS Viya visualisation, graph DB), stakeholder change management, monitoring and continuous improvement.

How the Academic Mentor guided the student step-by-step

Step 1 Clarify scope & objectives

• Mentor ensured the student framed the business problem (financial loss + reputational risk) and defined measurable goals (e.g., reduce fraud losses by X%, lower manual investigations by Y%).
• Agreed target stakeholders (fraud ops, risk, compliance, IT, customer experience).

Step 2 Research & context building

• Reviewed industry context (major Australian banks, recent fraud trends) and compiled relevant references.
• Ran a SWOT exercise with the student to prioritise technical and organisational constraints.

Step 3 Data & architecture planning

• Identified required data sources (transaction logs, device IDs, customer profiles, KYC, alerts).
• Mapped data pipeline needs: ingestion, graph construction, feature store, model scoring endpoint.
• Advised on choice of graph DB for relationship modelling and on SAS Viya for visualization/prototyping.

Step 4 Descriptive / graph analytics phase

• Demonstrated how to build network graphs (nodes: accounts/devices/people; edges: transactions/ownership).
• Taught methods to surface suspicious clusters (community detection, centrality measures) and visualise findings for stakeholders.

Step 5 Feature engineering & predictive modelling

• Coached on deriving graph features (e.g., degree, shortest path to known fraud, cluster density) plus transactional features (amount, velocity, device changes).
• Guided model selection: start with interpretable models (Logistic Regression, Decision Tree), explain training/validation splits, handling class imbalance, and cross-validation.
• Emphasised explainability for operational adoption.

Step 6 Prescriptive analytics & optimisation

• Showed how to define the objective function (cost of fraud vs cost of customer friction & manual review).
• Built a simulation framework to test response rules and thresholds across scenarios; iterated to find an optimal policy mix.

Step 7 Evaluation, deployment & monitoring

• Defined KPIs: precision, recall, AUC, cost per prevented fraud, manual review volume, time-to-detect.
• Advised on deployment architecture (real-time scoring, alert queueing) and post-deployment monitoring (concept drift detection, feedback loops).

Step 8 Communication & final deliverables

• Coached the student on presenting technical results to mixed stakeholder audiences: highlight business impact, show concise visuals, and include an implementation roadmap.
• Reviewed and refined all artefacts (graphs, model summaries, simulation results, recommendations).

Outcome what was produced and how success was measured

Delivered artefacts

• Problem brief and SWOT analysis tailored to Australian banking context.
• Graph analytics visualisations showing previously hidden clusters and suspected mule networks.
• Feature set documentation (including graph-derived features), and trained baseline models (Logistic Regression + Decision Tree) with validation metrics.
• Real-time scoring design and API/streaming architecture sketch.
• Prescriptive optimisation model and simulation results demonstrating preferred policy mixes.
• Final report and stakeholder presentation with rollout roadmap and governance checklist.

Learning objectives covered

• Domain knowledge: Understand modern fraud typologies and banking sector constraints.
• Analytical pipeline: Build end-to-end analytics: data collection → graph modelling → feature engineering → predictive modelling → prescriptive optimisation.
• Model selection & interpretability: Choose and justify interpretable models for business adoption.
• Graph analytics skills: Map complex relationships and extract meaningful graph features.
• Evaluation & trade-offs: Quantify and balance false positives/negatives using cost-based metrics and simulation.
• Technical implementation: Specify architecture for real-time scoring and integration (graph DB, feature store, scoring service).
• Stakeholder communication: Translate technical findings into business impact and a practical rollout plan.
• Ethics & governance: Consider explainability, regulatory compliance and operational monitoring to mitigate risk.

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