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© Fiskil 2026. All rights reserved.
Product Comparison
Updated 3 February 2026
When building with Fiskil's Banking API, you have access to multiple data types for different use cases. Two commonly compared approaches are deriving income insights from transaction data versus accessing categorised transaction histories. This comparison helps you understand how each data type works and when to use them.
Derived income insights from transaction analysis, identifying salary deposits, regular income patterns, and employment verification signals.
Official SourceRaw and categorised transaction records showing all account activity, including deposits, withdrawals, transfers, and payments.
Official SourcePurpose: Income data is derived for verification; transactions are raw financial records
Processing: Income data is pre-analysed; transactions require client-side processing
Use Case: Income for lending decisions; transactions for spending insights
Granularity: Income is summarised; transactions are individual records
Compliance: Income data designed for responsible lending; transactions need interpretation
Multi-source: Income separates sources automatically; transactions require manual analysis
Criterion | Income Data | Transactions |
|---|---|---|
Primary Use Case | Income verification, affordability assessment, lending decisions | Spending analysis, budgeting, financial insights, account aggregation |
→ Income data is specifically derived for verification; transactions are the raw source. | ||
Data Source | Derived from transaction patterns, employer identification, deposit frequency | Direct from bank APIs via CDR endpoints |
→ Income insights are computed from transaction data. | ||
Accuracy for Lending | High - specifically designed for income verification with pattern recognition | Requires additional processing to identify and validate income sources |
→ Income data pre-processes transactions for lending-specific insights. | ||
Data Granularity | Summarised income figures, employer names, payment frequency, income stability scores | Individual transactions with merchant details, amounts, dates, categories |
→ Income is aggregated; transactions are granular. | ||
Historical Coverage | Typically analyses 3-12 months for income pattern stability | Minimum 90 days; many banks provide 12+ months |
→ Both access the same underlying transaction history. | ||
Processing Required | Pre-processed by Fiskil with income categorisation and verification | Requires client-side categorisation and analysis for income insights |
→ Income data reduces development effort for lending use cases. | ||
Compliance Suitability | Designed for responsible lending obligations and affordability checks | Provides evidence trail but requires interpretation |
→ Income verification helps meet regulatory requirements. | ||
Multi-source Income | Identifies and separates multiple income sources (salary, freelance, benefits) | Shows all deposits but requires manual separation of income types |
→ Income data handles complex income situations automatically. | ||
Real-time Updates | Recalculated when new transactions arrive | Near real-time (typically within minutes of bank posting) |
→ Both stay current with latest account activity. | ||
API Endpoints | /accounts/{id}/income-summary, /accounts/{id}/income-streams | /accounts/{id}/transactions, /accounts/{id}/balances |
→ Different endpoints serve different data needs. | ||
Both accessed via Fiskil's Banking API
Both use the same CDR consent and authentication
Both provide historical data coverage
Both update in near real-time
Both require the same CDR accreditation
Both available from 100+ Australian institutions
Both support the same security standards (OAuth 2.0, FAPI 2.0)
Choose income data when you need quick, reliable income verification for lending, affordability assessments, or employment verification. The pre-processed insights save development time and are designed for compliance. Choose transaction data when you need full financial visibility for budgeting apps, spending analytics, or account aggregation features. Many applications use both: income data for quick verification decisions and transaction data for detailed user-facing features.
Yes, a single CDR consent grants access to both data types. You can request income insights and full transaction history simultaneously.
Fiskil's income data uses proven pattern recognition and is continuously improved. Unless you have specialised income analysis expertise, the pre-processed data is typically more accurate and consistent.
Income verification is important for responsible lending. While you could derive this from transactions, pre-processed income data provides audit-ready insights designed for compliance requirements.
Yes, the income analysis identifies multiple income streams including irregular patterns from freelance, gig economy, or variable commission-based income.
Income insights are recalculated as new transactions come in, typically within minutes of banks posting new activity.
For budgeting apps, use transaction data for the spending breakdown and categorisation features users expect. You might also use income data to show income vs expenses summaries.
Our team can help you navigate regulatory compliance and determine what you need to meet your open banking obligations.
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© Fiskil 2026. All rights reserved.