Digital finance produces data at a scale and speed that traditional risk methods were never designed for. A busy mobile money platform records very large transaction volumes each day across agents, merchants, banks and partner apps, and digital lenders make credit decisions in seconds using phone and wallet histories. In this setting, the strength of an institution's analytics largely determines how quickly it detects fraud, how accurately it prices credit and how convincingly it can evidence control to regulators.

The Data and Analytics for Digital Financial Services, Payments & Fintech Risk Training Course builds those capabilities step by step. It opens with the structure of payment and lending data, data quality and the privacy principles that govern its use. Participants then explore and visualise data, develop risk indicators and dashboards, and move on to fraud and financial crime analytics, including anomaly detection and network methods. Digital credit scoring with alternative data follows, and the course concludes with validation, monitoring and governance so that analytical tools remain reliable once they are in production.

Most of the time is spent in guided labs using anonymised sample datasets, Python notebooks and a business intelligence tool. Each lab mirrors a real decision, and participants finish with a portfolio of queries, models and reports they can adapt at work.

On completing the course, participants will be able to:

• Describe the main data sources in payments, wallets and digital lending and how they link together.
• Write SQL queries that extract, join and aggregate transaction and customer data.
• Clean, profile and explore risk datasets using Python and pandas.
• Construct key risk indicator dashboards in Power BI for fraud, agent and credit portfolios.
• Develop fraud detection approaches that combine rules, anomaly detection and network analysis.
• Build and validate a digital credit scorecard using alternative data.
• Monitor model performance and drift with AUC, Gini, KS and population stability measures.
• Apply model governance, explainability and data protection principles to analytics work.

The course is relevant to professionals who carry out or commission data analysis in digital finance, such as:

• Fraud analysts and fraud strategy managers
• AML transaction monitoring analysts and financial intelligence staff
• Credit risk analysts and scorecard developers in digital lending
• Data scientists and data analysts in banks, telecoms and fintech firms
• Business intelligence and reporting specialists supporting risk teams
• Risk managers who want to commission and challenge analytics
• Internal auditors and model validators
• Supervisory analysts at central banks and financial regulators

Participants take away a toolkit they can reuse:

• A library of SQL queries for common fraud, agent and credit risk questions.
• A Python notebook for profiling and cleaning transaction data.
• A Power BI dashboard prototype with key risk indicators and drill-downs.
• A tested set of fraud rules alongside an anomaly detection model, with a note on the trade-offs between them.
• A working credit scorecard with a validation report.
• A model monitoring checklist covering drift, stability and fairness.
• A short data governance note for presenting analytics work to risk committees.

Learning happens mainly at the keyboard, supported by:

• Guided labs in SQL, Python notebooks and Power BI using anonymised sample datasets
• Short demonstrations before each lab that explain the method and its limits
• Paired work so participants with different coding experience support one another
• Case challenges built on realistic fraud, agent and lending scenarios
• Code and dashboard reviews with facilitator feedback
• A capstone analysis presented to a mock risk committee

Participants who attend all sessions and complete the daily labs and capstone analysis earn a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.

The certificate confirms the course title, dates and delivery mode, while the queries, notebooks, dashboard and scorecard built in the labs form a practical portfolio that demonstrates applied analytical skill.