Power BI Financial Technology Analytics Training Course

Course overview

Financial technology teams generate high-volume data from payment gateways, digital wallets, lending platforms, core banking systems, fraud engines and accounting ledgers. Yet finance professionals often rely on disconnected spreadsheets and static reports that cannot explain payment failures, customer profitability, delinquency trends, fee leakage or suspicious transaction patterns. This course equips participants to build governed Power BI analysis that connects operational fintech metrics with financial-control and accounting measures.

Participants learn to prepare transaction, customer, merchant, loan and ledger data using Power Query; model data with star-schema principles; and create DAX measures for revenue recognition, transaction fees, chargebacks, expected credit loss indicators, ageing, portfolio yield and customer cohort performance. They build interactive dashboards for payments operations, lending performance, financial planning and fraud-risk monitoring, while applying finance-specific controls such as reconciliation checks, period filters, exception thresholds and row-level security.

Training is delivered through instructor demonstrations, guided Power BI builds and realistic fintech datasets containing payment settlements, loan repayments, merchant fees and general-ledger extracts. Each participant develops a finance analytics report pack with a documented data model, reusable DAX measures, drill-through investigation pages and executive KPI views. The final workshop requires participants to present findings, explain data-quality limitations and define actions that a finance, risk or operations team could take.

The course is suited to finance and accounting professionals, fintech analysts and reporting specialists who already work with business data and need to replace manual reporting with credible, decision-ready Power BI analysis.

Course objectives

By the end of this course, participants will be able to:

• Build a star-schema Power BI data model linking transactions, customers, merchants, loan accounts and general-ledger entries
• Clean and reconcile payment, settlement and accounting extracts using Power Query transformations
• Write DAX measures for net transaction revenue, chargeback rate, portfolio yield, ageing and delinquency indicators
• Create payment operations dashboards with settlement status, authorization rate, failure reason and merchant drill-through analysis
• Develop lending portfolio reports using repayment schedules, arrears buckets, cohort analysis and exposure measures
• Implement finance-control checks for duplicate transactions, unmatched settlements, period cut-off exceptions and fee variances
• Configure row-level security roles for finance, risk, operations and merchant-management report audiences
• Produce a documented Power BI finance analytics report pack with KPI definitions, data lineage and action recommendations

Benefits of attending

Target competencies

Who should attend

• Financial Analysts — who need to connect operational fintech data to revenue, cost and profitability reporting
• Finance Managers — who require controlled dashboards for payment performance, reconciliation and management reporting
• Fintech Data Analysts — who turn transaction, customer and product data into operational and financial insight
• Management Accountants — who need repeatable models for fee analysis, variance investigation and period-end reporting
• Risk and Fraud Analysts — who monitor transaction exceptions, chargebacks and behavioural risk indicators
• Lending Operations Managers — who need visibility of repayment performance, arrears and portfolio quality

Requirements and prerequisites

Participants should be comfortable using Excel for sorting, filtering, formulas and pivot-style analysis, and should understand basic finance concepts such as revenue, expenses, general-ledger accounts, reconciliations, transaction fees and reporting periods. Familiarity with payment, lending or digital-banking data is useful but not essential. Prior Power BI experience is not required, although attendees should be able to navigate Windows, work with CSV or Excel files and interpret simple tables. The course does not require programming, SQL, advanced statistics, prior DAX knowledge or experience administering Power BI tenants.

Training methodology

The instructor alternates short technical briefings with live Power BI builds using a fintech case dataset. Participants import payment settlements, merchant records, loan schedules and ledger extracts; transform them in Power Query; then model and analyse them in Power BI Desktop. Guided exercises focus on reconciliation, DAX calculation patterns and exception investigation. Small-group reviews test whether a dashboard supports a finance or risk decision, not just whether it looks polished. On day five, each participant refines a report pack and records an application plan for their own reporting process.

Course outline

Day 1: Fintech data foundations and financial model design

• Fintech data flows across payments, lending, wallets and general-ledger systems
• Finance analytics requirements for revenue, settlement, risk and control reporting
• Power BI Desktop interface, report views and model views
• Importing CSV, Excel and folder-based finance data sources
• Power Query data profiling for transaction identifiers, dates and currency fields
• Star-schema design for transaction facts and finance dimensions
• Relationship configuration between transaction, merchant, customer and account tables

Workshop: Build an initial payments-and-ledger data model from supplied transaction, merchant, settlement and chart-of-accounts extracts.

Day 2: Power Query transformation and settlement reconciliation

• Power Query data types, locale settings and date-time handling
• Appending daily transaction files from a controlled folder source
• Merging authorization, capture, settlement and chargeback records
• Cleaning merchant identifiers, payment status codes and duplicate records
• Creating reconciliation keys for transaction-to-settlement matching
• Deriving settlement ageing, processing lag and exception categories
• Documenting transformation steps and data-quality assumptions

Workshop: Create a settlement reconciliation query that identifies unmatched, duplicated and late-settled payment transactions.

Day 3: DAX for transaction economics and finance KPIs

• DAX evaluation context for finance measures
• Calendar tables and time-intelligence patterns for period comparisons
• Net transaction revenue after fees, refunds and chargebacks
• Authorization rate, conversion rate and payment failure analysis
• Merchant profitability and customer cohort measures
• Variance measures for actual, budget and prior-period reporting
• DAX Studio query inspection and measure-performance checks

Workshop: Develop a reusable DAX measure library for payment volumes, fee income, chargeback rates and merchant profitability.

Day 4: Lending, risk and accounting control dashboards

• Loan portfolio data structures for balances, schedules and repayments
• Arrears ageing buckets and delinquency roll-rate measures
• Portfolio yield, repayment performance and cohort tracking
• Expected credit loss indicator dashboards and risk segmentation
• General-ledger mapping and management-account reporting views
• Exception thresholds, conditional formatting and alert-oriented pages
• Row-level security roles for finance, risk and operational users

Workshop: Build a lending and finance-control dashboard that flags deteriorating arrears, repayment exceptions and ledger variances.

Day 5: Decision-ready reporting and deployment planning

• Executive dashboard layout for finance, risk and operations decisions
• Drill-through pages for payment failures, merchants and delinquent accounts
• Tooltip pages, bookmarks and guided investigation paths
• Power BI Service workspaces, semantic models and scheduled refresh concepts
• KPI definitions, metric ownership and data-lineage documentation
• Report validation against source totals and finance-control checks
• Action planning for a live fintech reporting use case

Workshop: Complete and present a finance analytics report pack with executive KPIs, investigation pages, reconciliation evidence and an implementation plan.

Tools & standards covered

Microsoft Power BI Desktop, Microsoft Power BI Service, Power Query, DAX Studio

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions