AI-Assisted Fraud Detection Improves Recall by 10–12% While Increasing Analyst Case Reviews by 30–35%

A financial institution processing high transaction volumes strengthened fraud detection and investigation with AI-assisted risk scoring, governed case intelligence and human-in-the-loop decisioning, delivering a 10–12% improvement in recall at controlled thresholds, reducing false positives by 15–20%, and enabling analysts to review 30–35% more cases.

About the Client

A bank or payments provider processing high transaction volumes, facing increasingly complex fraud patterns and growing investigation workloads while operating under strict requirements for explainability, governance and auditability.

At a glance

Industry
BFSI
Engagement / Service
Artificial Intelligence, Fraud Detection & Investigation
Coverage
High-volume transaction processing and selected transaction types
Challenge
Rule-based fraud controls generated significant false positives, while fragmented investigation data and emerging fraud patterns increased analyst workload and slowed priority-case response.

Success highlights

  • 10–12% improvement In recall at controlled thresholds
  • 15–20% reduction In false positives
  • 30–35% more cases Reviewed per analyst
  • Near real time Priority-case triage reduced from minutes

The challenge

Corporate meeting

Detecting evolving fraud without overwhelming investigators

Balancing detection accuracy, analyst workload, and regulatory governance

Rule-based fraud controls were generating high volumes of false positives, while new fraud patterns were emerging faster than rules could be updated. This created a difficult balance between improving detection rates and avoiding unnecessary investigation workload.

Investigators were spending substantial time manually gathering transaction, device, customer, and case evidence across fragmented systems. At the same time, operating in a highly regulated financial environment meant any AI recommendations influencing operational decisions required strict model governance, explainability, and auditability.

Our approach

Combining advanced detection techniques with governed investigator intelligence

TECEZE combined established fraud controls with machine learning, anomaly detection, and graph-based analysis to enhance risk assessment while establishing a governed, explainable workspace for investigation teams.

From fragmented detection to a unified, governed AI approach, the transformation included:

Multi-model fraud risk scoring

Combined rules, supervised ML models, anomaly detection, and graph features to score selected transaction types and improve threat identification.

Governed investigator workspace

Centralized evidence assembly using retrieval-augmented generation (RAG) to summarize case data and drastically reduce manual gathering effort.

Human-in-the-loop decisioning

Kept operational decisions under human control, supported by clear reason codes, model explainability (SHAP), and continuous threshold tuning.

MLOps & model-risk controls

Established rigorous governance covering model approval, versioning, performance monitoring, drift detection, and rollback capabilities.

Technology stack and tooling architecture

Because operating in a regulated financial environment requires strict compliance, the underlying architecture balances high-throughput processing with enterprise-grade governance:

Layer Technologies / Tools Operational Role
Data & Streaming Kafka, Feature Store, Graph Database (Neo4j), Encrypted Platform Real-time ingestion, continuous feature engineering, and relationship tracking
Modeling & Analytics Python Models, Databricks, SageMaker/Azure ML, SHAP Multi-model fraud scoring, anomaly detection, and model explainability
GenAI & Workspace Vector DB, Enterprise LLM Gateway, LangChain/Semantic Kernel Retrieval-augmented generation (RAG) for automated case summarization
Governance & Operations MLflow, Model-Monitoring Tools, Power BI, Case Management APIs Model versioning, drift detection, auditability, and investigator workflows
The results: Following implementation

Stronger fraud detection with faster, more productive investigations

10–12%Improvement in recall at controlled thresholds
15–20%Reduction in false positives
30–35%More cases reviewed per analyst
Near real timePriority-case triage reduced from minutes
GovernedHuman-in-the-loop decisioning & explainability
Multi-ModelRules, ML, anomaly detection & graph analytics

What the client says

“The AI-assisted approach has helped our teams identify higher-risk activity with greater precision while reducing the manual effort involved in investigation. The combination of explainability, human review and strong governance gives us greater confidence in using AI within fraud operations.”

Delivery partnership

Bringing fraud intelligence, investigation workflows and AI governance together

TECEZE worked with the financial institution’s fraud, investigation, data and technology teams to integrate detection models with existing case-management workflows. The engagement brought together machine learning, graph analytics, retrieval-augmented investigation support and model-risk controls while maintaining human oversight of operational decisions.

Looking ahead

Extending AI-assisted intelligence across fraud operations

The established architecture provides a foundation for expanding AI-assisted detection and investigation across additional transaction types and fraud scenarios.

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