I help financial institutions improve transaction monitoring processes and operational effectiveness.

This includes optimizing workflows using AI, process mining, and automation tools such as Power Automate to reduce processing times and improve investigation efficiency.

I also support the introduction of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to assist analysts with report writing, case documentation, and intelligence analysis.

From a risk management perspective, I ensure that risk appetite statements, SIRA (System Integrity Risk Assessments), and financial crime risks are correctly mapped to monitoring controls. Controls are designed and tested using structured methodologies to confirm that they effectively detect the intended risk.

Monitoring performance is supported with operational dashboards and monitoring metrics to track effectiveness and operational performance.

Many monitoring frameworks generate high volumes of alerts that do not lead to suspicious activity reports.

I help institutions reduce false positives through structured monitoring optimization and model improvement.

This includes techniques such as:

• alert backtesting and non-alerted transaction analysis

• scenario tuning and threshold calibration

• above-the-line and below-the-line testing

• redesigning monitoring scenarios to better align with financial crime risks

Where AI models are used, I apply techniques such as feature engineering and feature swapping to improve model performance and reduce unnecessary alerts.

The objective is to improve detection quality while reducing operational workload for investigation teams.