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Lead Analyst - Portfolio Intelligence & Fraud Analytics
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingAs a Portfolio Intelligence & Fraud Analytics Lead Analyst, you will play a critical role in helping Nium understand, monitor, and manage portfolio performance, fraud risk, payment abuse, and emerging customer behavior trends across its global payments ecosystem.
You will leverage data, analytics, and risk intelligence to identify opportunities, detect emerging risks, support fraud prevention initiatives, and provide actionable insights to business stakeholders.
Monitor portfolio KPIs and KRIs and flag significant changes or risk signals
Investigate fraud events to find root causes, attack patterns and control gaps
Support fraud rule and threshold tuning and assess fraud-control effectiveness
Build and maintain portfolio and fraud dashboards and recurring reports
Analyse customer friction metrics to balance fraud prevention with customer experience
From the employer’s posting
As a Portfolio Intelligence & Fraud Analytics Lead Analyst, you will play a critical role in helping Nium understand, monitor, and manage portfolio performance, fraud risk, payment abuse, and emerging customer behavior trends across its global payments ecosystem.
You will leverage data, analytics, and risk intelligence to identify opportunities, detect emerging risks, support fraud prevention initiatives, and provide actionable insights to business stakeholders. Working closely with Fraud, Risk, Compliance, Product, Operations, and Data teams, you will transform complex data into meaningful intelligence that supports business growth, effective risk management, and operational decision-making.
Monitor key portfolio KPIs and KRIs, identifying significant changes and potential risk indicators.
Investigate fraud events to identify root causes, attack patterns, and control gaps.
Support development and optimization of fraud rules, thresholds, and monitoring strategies.
Build and maintain dashboards using Looker, Power BI, Tableau, or similar analytics platforms.
Analyse customer friction metrics to balance fraud prevention objectives with customer experience.
What you’ll bring
All qualificationsCore experience
- 4-7 years of experience in Fraud Analytics, Portfolio Analytics, Payments Risk, Financial Crime Analytics, Business Intelligence, or Data Analytics.
- Experience working with large datasets and identifying actionable insights.
- Proficiency in Python for data analysis and automation.
- Experience building dashboards and visualizations using Looker, Power BI, Tableau, or similar tools.
- Understanding of payment ecosystems, fraud typologies, chargebacks, and transaction-risk indicators.
- Strong communication and stakeholder-management skills.
Preferred experience
- Experience within fintech, payments, banking, card issuing, or acquiring environments.
- Knowledge of Visa and Mastercard fraud monitoring programs.
- Familiarity with AML, transaction monitoring, or financial crime controls.
- Experience with machine learning models or predictive analytics.
Qualification wording
4-7 years of experience in Fraud Analytics, Portfolio Analytics, Payments Risk, Financial Crime Analytics, Business Intelligence, or Data Analytics.
Experience working with large datasets and identifying actionable insights.
Proficiency in Python for data analysis and automation.
Experience building dashboards and visualizations using Looker, Power BI, Tableau, or similar tools.
Understanding of payment ecosystems, fraud typologies, chargebacks, and transaction-risk indicators.
Strong communication and stakeholder-management skills.
Experience within fintech, payments, banking, card issuing, or acquiring environments.
Knowledge of Visa and Mastercard fraud monitoring programs.
Familiarity with AML, transaction monitoring, or financial crime controls.
Experience with machine learning models or predictive analytics.
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 2, 20262 days ago
- Recorded sightings
- 3
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Nium is hiring
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- BigQuery
- Databricks
- Looker
- Power BI
- Redshift
- Snowflake
- Tableau
Where the posting names them
- Advanced SQL skills and experience performing complex data analysis. - Proficiency in Python for data analysis and automation. - Experience building dashboards and visualizations using Looker, Power BI, Tableau, or similar tools.
You will leverage data, analytics, and risk intelligence to identify opportunities, detect emerging risks, support fraud prevention initiatives, and provide actionable insights to business stakeholders. Working closely with Fraud, Risk, Compliance, Product, Operations, and Data teams, you will transform complex data into meaningful intelligence that supports business growth, effective risk management, and operational decision-making. This role is ideal for a highly analytical professional who combines strong SQL and data analysis capabilities with an understanding of payments, fraud risk, portfolio monitoring, and customer behavior. Key Responsibilities:
- Build and maintain dashboards using Looker, Power BI, Tableau, or similar analytics platforms. - Develop and optimize SQL queries for fraud, portfolio, compliance, and operational reporting. - Create recurring and ad hoc reports for Risk, Compliance, Product, and senior stakeholders.
- Experience working with large datasets and identifying actionable insights. - Advanced SQL skills and experience performing complex data analysis. - Proficiency in Python for data analysis and automation.
- Experience with machine learning models or predictive analytics. - Exposure to cloud data platforms such as Snowflake, Redshift, Databricks, or BigQuery. Employment type
- Support remediation activities for emerging fraud risks and elevated loss trends. - Build and maintain dashboards using Looker, Power BI, Tableau, or similar analytics platforms. - Develop and optimize SQL queries for fraud, portfolio, compliance, and operational reporting.
- Proficiency in Python for data analysis and automation. - Experience building dashboards and visualizations using Looker, Power BI, Tableau, or similar tools. - Understanding of payment ecosystems, fraud typologies, chargebacks, and transaction-risk indicators.
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Job description
About the role:
As a Portfolio Intelligence & Fraud Analytics Lead Analyst, you will play a critical role in helping Nium understand, monitor, and manage portfolio performance, fraud risk, payment abuse, and emerging customer behavior trends across its global payments ecosystem.
You will leverage data, analytics, and risk intelligence to identify opportunities, detect emerging risks, support fraud prevention initiatives, and provide actionable insights to business stakeholders. Working closely with Fraud, Risk, Compliance, Product, Operations, and Data teams, you will transform complex data into meaningful intelligence that supports business growth, effective risk management, and operational decision-making.
This role is ideal for a highly analytical professional who combines strong SQL and data analysis capabilities with an understanding of payments, fraud risk, portfolio monitoring, and customer behavior.
Key Responsibilities:
- Analyze customer, product, corridor, and market performance to identify emerging trends, risks, and growth opportunities.
- Develop and maintain portfolio monitoring dashboards and reporting frameworks and perform deep-dive analysis on customer segments, transaction behavior, payment flows, and portfolio concentration risks.
- Monitor key portfolio KPIs and KRIs, identifying significant changes and potential risk indicators.
- Conduct ad hoc analytical reviews to support business initiatives and strategic decision-making.
- Monitor fraud, chargeback, dispute, and payment abuse trends across products and geographies.
- Investigate fraud events to identify root causes, attack patterns, and control gaps.
- Support development and optimization of fraud rules, thresholds, and monitoring strategies.
- Assess fraud-control effectiveness and provide recommendations for improvement.
- Analyse customer friction metrics to balance fraud prevention objectives with customer experience.
- Support remediation activities for emerging fraud risks and elevated loss trends.
- Build and maintain dashboards using Looker, Power BI, Tableau, or similar analytics platforms.
- Develop and optimize SQL queries for fraud, portfolio, compliance, and operational reporting.
- Create recurring and ad hoc reports for Risk, Compliance, Product, and senior stakeholders.
- Develop data visualizations that communicate actionable insights effectively.
- Partner with Data Engineering teams to improve data quality, accessibility, and reporting capabilities.
- Monitor portfolio performance against internal risk thresholds and payment network requirements.
- Track fraud-to-sales ratios, chargeback ratios, dispute rates, approval rates, and transaction-risk indicators.
- Analyze emerging payment-risk trends and provide early warning indicators to stakeholders.
- Provide analytical support for fraud prevention, risk management, and portfolio optimization projects.
Requirements:
- 4-7 years of experience in Fraud Analytics, Portfolio Analytics, Payments Risk, Financial Crime Analytics, Business Intelligence, or Data Analytics.
- Strong analytical and problem-solving skills.
- Experience working with large datasets and identifying actionable insights.
- Advanced SQL skills and experience performing complex data analysis.
- Proficiency in Python for data analysis and automation.
- Experience building dashboards and visualizations using Looker, Power BI, Tableau, or similar tools.
- Understanding of payment ecosystems, fraud typologies, chargebacks, and transaction-risk indicators.
- Strong communication and stakeholder-management skills.
- Ability to present analytical findings to both technical and non-technical audiences.
- Preferred Qualifications
- Experience within fintech, payments, banking, card issuing, or acquiring environments.
- Knowledge of Visa and Mastercard fraud monitoring programs.
- Familiarity with AML, transaction monitoring, or financial crime controls.
- Experience with machine learning models or predictive analytics.
- Exposure to cloud data platforms such as Snowflake, Redshift, Databricks, or BigQuery.
Employment type
Full-time
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bangalore
Working pattern and location restrictions need checking in the full posting.
- Work authorization
- Experience within fintech, payments, banking, card issuing, or acquiring environments. - Knowledge of Visa and Mastercard fraud monitoring programs. - Familiarity with AML, transaction monitoring, or financial crime controls.