What insights are most predictive?

Stable net inflows, income regularity, debt service burden, return items, and cash withdrawal patterns. These features correlate with repayment ability and early fraud risk in new customer portfolios.

How do we handle varied formats?

Use template detection, tolerant parsing, and business rules for date and currency normalization. Maintain a feedback loop to correct labeling errors and retrain parsers with adjudicated outcomes.

Can customers redact data?

Yes, when policy allows. Focus on income and liability signals, and avoid collecting unrelated merchant details. Document any redactions and note their effect on confidence in the decision.

Is open banking better than PDFs?

Often, due to freshness and reduced tampering risk. Still accept documents for unsupported institutions or consent constraints, with stronger authenticity checks and manual validation steps.

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Bank Statement Analysis

Overview

Bank statement analysis extracts and interprets transaction data from statements to assess income stability, expenses, obligations, and risk behaviors. Automated parsers classify credits and debits, identify salary patterns, detect loan repayments, and derive metrics such as average balance, overdraft frequency, and volatility. In underwriting and fraud prevention, it validates affordability, flags circular transfers, and highlights indicators of mule or synthetic activity.

Accuracy depends on high quality ingestion, de-duplication, and reconciliation of missing pages or overlapping periods. Privacy requires limiting fields to business needs and redacting sensitive data not required for the decision. Outcomes should produce explainable features and reason codes for fair assessments. When available, API based aggregation is preferred over PDFs, but document uploads remain essential for edge cases. Combined with KYC, credit data, and transactional monitoring, statement analysis improves decision speed and reduces manual review.