New opportunity

AVP/Senior Assoc, Data/Business Analyst, Group Legal, Compliance & Secretariat

DBS Bank Ltd · Singapore

About this role

The Group Legal, Compliance & Secretariat (LCS) division at DBS is seeking an AVP/Senior Associate Data/Business Analyst to manage legal, regulatory, and financial crime risks. This role focuses on applying data analytics to assess customer and real-time payment-message screening performance across all DBS operating countries. Responsibilities include: Strategic Screening Optimisation: Conduct analytical assessments and recommend enhancements to detection logic and algorithms for customer onboarding, monitoring, and SWIFT payment screening to balance alert efficiency with risk coverage. Cross-Functional Collaboration: Partner with Compliance, Technology, and Business Units as a subject matter expert (SME) to present findings and define screening requirements. Advanced Analytics: Perform detailed data analysis on screening performance, alert drivers, and emerging risks; develop and backtest recommendations for rules, thresholds, and risk scoring. Regulatory Support: Maintain analytical methodologies and provide evidence for audits and regulatory reviews. Implementation Support: Translate findings into requirements and provide SME support during UAT and post-implementation validation. Performance Reporting: Deliver actionable insights on screening performance indicators and alert trends. Requirements: A Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or a related field is required. Candidates must have 8–10 years of progressive experience in financial services, consulting, or a regulated industry, with expertise in financial crime prevention and data analytics. Technical proficiency in Python is mandatory, while experience with big data technologies, data warehousing, and Tableau is highly desirable. Strong analytical, problem-solving, and communication skills are essential, along with foundational knowledge of AML/CFT/KYC regulatory requirements. Hands-on experience with screening analytics and SWIFT payment-message formats is preferred.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonData AnalyticsFinancial Crime PreventionBig Data TechnologiesData WarehousingTableauAML/CFT/KYCSWIFTStatistical AnalysisRisk Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Strategic Screening Optimization
The core objective is to balance alert efficiency with comprehensive risk coverage. You must analyze detection logic, matching rules, and thresholds for customer and SWIFT payment screening, ensuring the bank maintains regulatory compliance while minimizing false positives.
Cross-Functional SME Advisory
You will act as the primary subject matter expert, bridging the gap between technical teams and business stakeholders. This requires translating complex analytical findings into actionable requirements for Compliance, Technology, and Operations teams.
Regulatory and Audit Evidence Management
You are responsible for maintaining rigorous documentation of all analytical methodologies, testing evidence, and decision rationales. This is critical for providing defensible, transparent support during internal audits and external regulatory reviews.
Advanced Data Analysis and Backtesting
Using Python and big data tools, you must perform deep-dive analysis on large datasets to identify alert drivers and emerging risks. You are expected to develop and backtest proposed changes to screening rules before they are approved for implementation.

Likely challenges to think through

Balancing Risk Mitigation with Operational Efficiency
A common challenge in financial crime analytics is the trade-off between tightening controls to catch illicit activity and increasing the volume of false positive alerts that burden operational teams. Prepare by demonstrating how you use data-driven backtesting to find the optimal threshold that satisfies both risk and efficiency KPIs.
Influencing Stakeholders Without Direct Authority
While you provide the analytical recommendations, the implementation is handled by other technology and delivery teams. Prepare to discuss how you build rapport and use evidence-based communication to influence cross-functional partners to adopt your proposed screening enhancements.
Navigating Complex Regulatory Expectations
The role requires staying current with evolving AML/CFT/KYC standards across multiple operating countries. Prepare by highlighting your process for monitoring industry developments and translating shifting regulatory requirements into technical screening logic.

Practice your answers

Technical practice

Optimizing Screening Detection Logic

What to coverExplain your approach to balancing false positive reduction with maintaining comprehensive risk coverage in AML screening. Discuss how you would use backtesting to validate proposed changes to thresholds or matching logic before implementation.

Python for Large-Scale Financial Datasets

What to coverDescribe your experience using Python to process and analyze datasets containing millions of records. Mention specific libraries or techniques you use to ensure efficient data manipulation and performance when working with large financial crime datasets.

SWIFT Payment Message Analysis

What to coverHow do you interpret SWIFT message formats (e.g., MT or MX) during the screening process? Explain how you identify potential risks within these message structures and how you translate those findings into actionable detection rules.

Backtesting and Model Validation

What to coverDetail your methodology for backtesting new screening rules. Explain how you measure the impact of these changes on operational efficiency and risk mitigation, and how you document this process for audit purposes.

Data Quality and Screening Limitations

What to coverHow do you identify and address data quality gaps that impact screening performance? Discuss the importance of understanding analytical limitations when presenting recommendations to stakeholders.

Big Data Technologies in Compliance

What to coverIf you have experience with big data technologies or data warehousing, describe how you have leveraged these tools to support financial crime prevention. If not, explain how you would approach integrating large, disparate data sources for a holistic screening analysis.

Translating Analytics to Requirements

What to coverDescribe your process for translating complex analytical findings into clear, actionable requirements for UAT and implementation. How do you ensure that technical specifications align with regulatory expectations?

Visualizing Screening Performance

What to coverHow do you use data visualization tools like Tableau to communicate screening performance indicators and alert trends to non-technical stakeholders? Provide an example of how a visualization helped drive a strategic decision.

Behavioural practice

Influencing Cross-Functional Stakeholders

What to coverDescribe a situation where you had to influence Compliance, Technology, or Business stakeholders to adopt a new screening recommendation. How did you present your evidence-based findings to gain their buy-in?

Managing Regulatory and Audit Inquiries

What to coverTell me about a time you had to provide analytical evidence for a regulatory or audit review. How did you ensure your methodology and decision rationale were clear and defensible?

Handling Conflicting Priorities

What to coverDescribe a time when you had to balance urgent operational needs with long-term strategic screening optimization. How did you prioritize your tasks to ensure high-quality outcomes?

Navigating Complex Problem Solving

What to coverGive an example of a complex financial crime risk issue you identified through data analysis. What steps did you take to investigate the root cause and what was the final impact of your recommendation?

Sharpen your knowledge

Topics to prepare

Financial Crime Screening Logic
Review the mechanics of customer and payment-message screening, specifically focusing on matching logic, threshold tuning, and risk scoring. Exercise: Research how false positive rates are balanced against risk coverage in AML systems to prepare for discussions on optimizing detection algorithms.
AML/CFT/KYC Regulatory Frameworks
Refresh your knowledge of Anti-Money Laundering (AML), Countering the Financing of Terrorism (CFT), and Know Your Customer (KYC) requirements. Exercise: Prepare to explain how analytical methodologies can provide evidence for regulatory audits and demonstrate compliance control effectiveness.
SWIFT Payment Message Standards
Familiarize yourself with the structure and common fields of SWIFT payment messages (e.g., MT and MX formats). Exercise: Practice identifying potential risk indicators within these message structures to support your role as a subject matter expert in real-time screening.

Skills to revise

Python for Large-Scale Data Analysis
Demonstrate proficiency in using Python to manipulate and analyze millions of records. Exercise: Create a script that simulates the backtesting of a detection rule against a historical dataset to measure impact on alert volume and risk coverage.
Data Visualization with Tableau
Develop the ability to translate complex screening performance data into actionable insights for senior stakeholders. Exercise: Build a dashboard prototype that tracks key performance indicators like alert trends, false positive rates, and risk coverage over time.
Big Data and Data Warehousing
Understand how to query and extract data from large-scale enterprise environments. Exercise: Review best practices for optimizing SQL queries or data retrieval processes when working with massive financial datasets to ensure efficient analysis.

Your preparation checklist

Prepare a case study on screening optimization
Develop a structured walkthrough of a past project where you analyzed large datasets to tune detection logic or thresholds. Focus on how you balanced false-positive reduction with maintaining risk coverage, and be prepared to explain the specific metrics you used to measure success.
Demonstrate Python proficiency for financial crime analytics
Prepare to discuss how you use Python to manipulate and analyze large, complex datasets. Highlight your experience with libraries relevant to data cleaning, statistical analysis, or pattern identification in the context of AML/CFT/KYC or SWIFT payment screening.
Articulate your approach to cross-functional stakeholder management
Prepare examples of how you have translated complex analytical findings into actionable requirements for non-technical stakeholders, such as Compliance or Operations teams. Focus on your ability to influence decision-making and provide evidence-based recommendations for regulatory or audit reviews.
Formulate questions regarding the current screening landscape
Prepare thoughtful questions about the bank's current challenges with alert volume, the integration of new big data technologies into their existing screening infrastructure, or how the team balances evolving regulatory expectations with operational efficiency.

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