By Jake Osborne | Credit Sense
Accuracy in Bank Statement Analysis Has Never Mattered More
Bank statement analysis has become a cornerstone of modern credit decisioning. But as adoption has increased, so has a critical misconception that all bank statement solutions deliver the same outcomes.
They don’t.
The bank statement provider you choose can materially affect approval rates, default rates, fraud exposure, and ultimately portfolio performance. The maturity, consistency, and accuracy of your bank statement provider have a direct impact on your business results, and it is important to ensure your foundation is built on the right platforms.
The challenge when evaluating solutions
Bank statement analysis looks deceptively simple. Ingest transactions, categorise them, calculate income and expenses, and produce insights. In practice, doing this accurately, consistently, and at scale is extremely difficult.
Providers that pioneered have been incrementally optimising their solution as they have been able to:
- Observe millions of real-world customer behaviours across economic cycles
- Refine categorisation logic through exposure to edge cases and anomalies
- Learn how different banking technologies, employers, and merchants behave over time
- Continuously validate outputs against real loan performance outcomes
Given the criticality of bank statement insights in the credit decisioning process, choosing the right supplier is one of the most crucial operational decisions you will make.
The Top 4 Pitfalls to Avoid
Not all differences are visible in dashboards or reports. The most important distinctions show up in how insights are calculated and applied.
1. Customer Payday Identification & Repayment Alignment
Correctly identifying a customer’s payday and aligning debits to that day is one of the simplest ways to improve repayment performance.
When repayments are scheduled shortly after income is received, customers are more likely to have sufficient funds available. When they aren’t, late payments can occur despite adequate affordability. This timing problem can result in poor portfolio performance and unnecessary operational overhead.
Many solutions estimate payday crudely or ignore alignment altogether. Mature platforms use historical patterns and trend analysis to identify true pay cycles, reducing unnecessary arrears driven purely by timing.
Consequence of not getting this right?
- Poor collection accuracy leading to greater dishonours and defaults
- Increased operational overhead to manage arrears
- Additional friction between you and your customers
- Damaged brand reputation with dishonour fees and late payment costs
2. Accurate Ongoing Income & Expense Trends
True affordability isn’t a single number, it’s a trend.
Traditional analytics often rely on aggregated averages that mask volatility and change. This is especially problematic for items that change over time, like income, variable loan repayments, and even groceries. Your bank statement solution should clearly identify:
- One-off vs recurring transactions
- Historical vs ongoing behaviour
- Aggregated averages vs true trend analysis
Errors or relying only on historical averages can increase lenders’ risk of overstating or understating serviceability, and can often require greater manual intervention to get an accurate outcome. Accurate, ongoing trends provide better visibility when dealing with inconsistent or changed income and expense behaviour.
Consequence of not getting this right?
- Time to onboard a new customer may be extended due to increased manual review and validation
- Strategies to improve payment behaviour may be hamstrung by latent inaccuracies upstream in the credit assessment process
- Growth strategies may be more challenging and expensive to execute
3. Transparent and Defensible Insights
Insights only work if you trust how they’re calculated.
If you can’t replicate an insight, such as monthly grocery spend or ongoing income, on paper with a pencil, you can’t rely on the decision it drives. That lack of transparency can quietly affect both approval volumes and risk exposure.
Income is a common example. Relying on a simple average of the last 90 days can:
- Miss recent pay rises/reductions or new employment
- Smooth over and hide income instability
- Understate or overstate affordability
Consequences of not getting this right?
- Latent inaccuracies. Whether the inaccuracy increases or decreases affordability, it may result in either an increased credit risk or lost opportunities
- Decisioning engines may produce unrealistic risk forecasts
- Overwhelmed customer service teams that have to complete additional manual processes to investigate inconsistencies
Mature solutions have spent years building and iterating on this proprietary technology to manage this risk and deliver insights that are supported by the data and can be validated.
4. Accuracy of multi-bank aggregation
Research conducted by YouGov and BCU Bank indicates that 43% of Australians are customers of two or more banks, increasing the challenge to understand the full borrowing capacity of a consumer by viewing a single bank or account.
Without a 360º view of the consumer’s bank accounts that is consolidated into a single view of income and expenses, making an accurate assessment becomes more difficult and risky than it needs to be.
Common challenges with account aggregation include:
- Internal transfers counted as two incomes. Where a consumer’s salary is deposited into account A, then transferred to account B, if your bank statement analytics doesn’t identify this behaviour, it may treat it as two incomes.
- Duplication of liability and expenses. This is a common issue where consumers transfer funds to pay their credit card, then use that credit card for everyday expenses (groceries, etc.), creating a duplication of expenses.
- Missing Accounts. Budget visibility blind spots can develop when consumers aren’t prompted to connect all of the accounts required for an application. Missing accounts could contain savings and be used for financial management, or they could be used for general expenses, making it difficult for assessors to determine the true purpose of internal transfers.
Consequences of not getting this right?
- Inaccurate assessments that overstate or understate a consumer’s capacity, leading to increased risk or lost opportunity.
- Increased cost due to additional manual intervention to get an accurate outcome.
Experience, Accuracy, and Transparency Are No Longer Optional
As bank statement analysis becomes embedded in credit decisioning, the focus must shift from whether a solution exists to how well it works.
Working with a provider that has the experience to deliver a solution you can rely on with proven categorisation accuracy, and transparent, reproducible analytics and insights is no longer a nice-to-have. It’s a risk control.
Book a meeting here to learn more.
Credit Sense Team