Data Analyst (Finance, Risk & Fraud)

About the role

This position is designed for a Data Analyst who wants to build the systems behind the numbers, not simply consume them to produce static reports. You will be responsible for developing the analytical frameworks that power fraud detection, risk management, and the accuracy of financial reporting across the business. Your work will involve investigating complex transaction and financial data in close partnership with Finance, Risk, and Compliance teams to strengthen reporting accuracy, proactively catch fraud, and surface emerging risks before they escalate into financial losses. You will play a key role in validating and shaping the AI platforms that automate these analytical workflows, ensuring models are grounded in clean, reliable data and sound business logic. This role requires a balance of technical depth and business acumen, as you translate ambiguous problems into structured analytical investigations. The ideal candidate will use data not only to describe what happened but to explain why it happened and how it might evolve. You will be a critical bridge between technical data systems and the operational teams that rely on insights to make decisions.

Key facts

What you'll do

Investigate transaction anomalies and financial data patterns alongside Risk and Compliance to identify potential fraud indicators and support timely escalation.

Develop and maintain analytical models and datasets that power fraud detection, risk scoring, and transaction monitoring activities across digital asset flows.

Collaborate with Finance stakeholders to validate financial reports, reconcile data discrepancies, and ensure accuracy in accounting and reporting outputs.

Translate ambiguous business questions from cross-functional partners into structured analytical plans with clear metrics, definitions, and validation steps.

Design and iterate on dashboards and visualizations to communicate risk trends, fraud signals, and financial health indicators to both technical and non-technical audiences.

Partner with data engineering and product teams to define data requirements and shape the roadmap for automated analytical pipelines that improve reliability over time.

Apply experimentation and statistical methods to test hypotheses about fraud patterns, helping to refine rules and models that reduce false positives and improve detection rates.

Support the evaluation and adoption of AI and machine learning tools in risk and fraud contexts, assessing their outputs and ensuring alignment with compliance and audit standards.

Own the end-to-end analysis of specific financial and fraud-related initiatives, documenting findings, insights, and recommendations in clear formats for decision-makers.

Contribute to the continuous improvement of data quality and documentation, ensuring that analytical assets are maintainable, transparent, and auditable by other teams.

Work within a regulated environment by adhering to internal controls, data governance policies, and stakeholder expectations around confidentiality and accuracy.

Assist in the development of forecasting and reporting mechanisms that improve financial planning, risk assessment, and operational decision-making.

Coordinate with Compliance and Operations to ensure that analytical outputs support timely responses to regulatory inquiries and internal audits.

Drive a culture of curiosity and rigor by asking probing questions, challenging assumptions in data, and encouraging evidence-based discussions across teams.

Requirements

Candidates must possess a bachelor's degree in a quantitative field such as mathematics, statistics, computer science, economics, or a related discipline, or have equivalent practical experience.

You must have 2-4 years of professional experience in data analytics or business intelligence roles within financial, transaction, or risk-related environments.

Demonstrated strong proficiency in SQL is required for querying large datasets, writing efficient joins and aggregations, and ensuring data accuracy.

You must be capable of using Python for data analysis, scripting, and automation of repetitive reporting tasks.

Experience working with financial, transaction, or risk/fraud data is essential to understand the context and implications of analytical findings.

You should be able to transform unclear business questions into structured analytical investigations with clearly defined hypotheses and metrics.

Strong communication skills are required to explain complex analytical results to non-technical stakeholders and to support cross-functional collaboration.

You must be comfortable working in a fast-paced, dynamic environment where priorities can shift quickly based on regulatory or business needs.

A strong attention to detail is required to ensure that analytical outputs are accurate, consistent, and auditable.

You must be able to work independently with minimal supervision while managing multiple analytical requests and deadlines.

An interest in applying AI/ML techniques to risk, fraud, and reporting challenges is preferred, with hands-on experience being a plus but not mandatory.

Experience with version control practices and basic understanding of data modeling concepts is expected.

You should be comfortable following documented processes and contributing to improvements in data workflows and standards.

A commitment to maintaining data privacy and regulatory compliance in all analytical work is required.

Nice to have

Experience in financial services, fintech, or crypto is preferred, along with familiarity with financial reporting, reconciliations, and close processes.

Hands-on experience with dashboarding tools such as Power BI, QuickSight, or Tableau is highly valued for building stakeholder-facing visualizations.

Exposure to fraud detection, anomaly detection, AML, transaction monitoring, or risk modeling is considered a strong advantage.

Comfort contributing to automated data pipelines and ETL workflows is preferred to support reliable and scalable analysis.

Familiarity with cloud services and data-engineering tools such as AWS, dbt, or Airflow will be viewed favorably for collaboration with engineering teams.

Practical notes

The role operates on a standard full-time schedule during Singapore business hours.

The office is located in the CBD area and includes a well-stocked pantry and comprehensive benefits such as generous annual leave, medical coverage for GP, Specialist, and TCM, self-care benefits, and fitness workshops.