Credit LensesRequest DemoReduce review time without losing context
Credit Lenses structures transaction evidence before it reaches an analyst, reducing repetitive statement review while keeping material income, expense, and commitment patterns visible.
Reduce Manual Work
Eliminate hours of manual bank statement review. Our AI processes thousands of transactions in seconds, freeing your team to focus on decision-making.
Improve Accuracy
Manual categorisation can miss relevant patterns. Credit Lenses surfaces potential BNPL commitments and gambling-related activity for validation by a qualified reviewer.
Approve with Confidence
Make faster, better-informed lending decisions with structured evidence that fits your existing policy and human-review process. Increase approval speed by up to 10x.
The Problem
Where manual review slows decisions
Slow & Labour-Intensive
Loan officers spend 2–4 hours per applicant manually reviewing bank statements, cross-referencing PDFs, and categorising transactions in spreadsheets. This bottleneck delays approvals and frustrates borrowers.
Inconsistent Results
Different analysts categorise the same transaction differently. One may flag a recurring payment as a subscription, another as a loan repayment. This inconsistency introduces risk into the credit decision.
Commitments Are Easy to Miss
Buy-Now-Pay-Later commitments, gambling-related spend, and payday loan cycles can be difficult to identify consistently across thousands of transaction rows and require contextual review.
Complex Patterns Need Attention
Potential synthetic income, round-tripping, and staged deposits can be difficult to identify at scale. Pattern indicators help analysts prioritise transactions that warrant closer investigation.
By the Numbers
Measurable Impact
10x
Faster underwriting decisions
85%
Reduction in manual review time
3x
More hidden liabilities detected
40%
Lower default rates for early adopters
Comparison
Credit Lenses vs. Manual Review
| Capability | Manual Review | Credit Lenses |
|---|---|---|
| Processing time | 2–4 hours | < 5 seconds |
| Categorisation accuracy | ~70–80% | 99.2% |
| BNPL detection | Often missed | Automatic flagging |
| Gambling-related indicators | Rarely checked | Pattern flagging for review |
| Fraud indicators | Limited | Synthetic income & round-tripping detection |
| Scalability | Linear (more staff needed) | Unlimited concurrent requests |