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Reliability Score
81
33 → 81(+48 pts)
Fintech: 54 near-duplicate rows detected (identical on all fields except key columns). This suggests duplicate ingestion or recording.
BayesIQ's audit surfaced 3 findings and enabled targeted remediation that improved the reliability score from 33 to 81.
P&L showed $299K in fee revenue — actual is $274K. $25K phantom revenue on regulatory filings constitutes a material misstatement
Reported
299,461
fee revenue · 2025-12
Audited
274,383
Overstated 8.4%
Decision Exposure
P&L showed $299K in fee revenue — actual is $274K. $25K phantom revenue on regulatory filings constitutes a material misstatement
54 near-duplicate rows detected (identical on all fields except key columns). This suggests duplicate ingestion or recording.
BayesIQ Data Reliability Audit
Fintech
Audit Period: December 2025
Reliability Score
Good — some issues need attention
Illustrative example — representative of BayesIQ audit deliverables
BayesIQ audits a fintech company's transaction fee revenue pipeline across merchant reconciliation, fee calculations, and regulatory reporting. The audit identifies 3 findings — including 54 near-duplicate transaction records, null merchant IDs breaking downstream aggregations, and an 8.4% fee revenue overstatement constituting a material misstatement. After remediation, the reliability score improves from 33 to 81.
Key Metrics: Reported vs Audited
| Metric | Period | Reported | Audited | Delta |
|---|---|---|---|---|
| fee_revenue | 2025-12 | 299,461 | 274,383 | overstated 8.4% |
Top Findings
fee_revenue misreported for 2025-12 (off by +8.4%)
P&L showed $299K in fee revenue — actual is $274K. $25K phantom revenue on regulatory filings constitutes a material misstatement
Near-duplicate rows detected
54 duplicate transaction records inflating fee calculations and distorting merchant reconciliation
54 rows affected
Null values in required column: merchant_id
28 transactions missing merchant_id — cannot reconcile these fees to merchants or verify compliance
28 rows affected
Recommended Actions
- 1
Investigate duplicate records. Add dedup logic keyed on non-key fields.
Owner: Data Engineering|Effort: Mediumhigh - 2
Fix null values in required column 'merchant_id' at the source.
Owner: Data Engineering|Effort: Mediumhigh - 3
Investigate root cause of fee_revenue discrepancy for 2025-12. Check for duplicate events, missing data, or filter logic differences.
Owner: Data Engineering|Effort: Smallmedium
Deliverables
Why this matters
fee_revenue misreported for 2025-12 (off by +8.4%)
mediumP&L showed $299K in fee revenue — actual is $274K. $25K phantom revenue on regulatory filings constitutes a material misstatement
Near-duplicate rows detected
high54 duplicate transaction records inflating fee calculations and distorting merchant reconciliation
Null values in required column: merchant_id
high28 transactions missing merchant_id — cannot reconcile these fees to merchants or verify compliance
What working with BayesIQ looks like
Discovery
3 findings identified
Remediation
Data Engineering resolved key issues
Steady State
Metrics verified and trustworthy
Your Data
This could be your story
What makes BayesIQ different
Findings connected to business meaning
Operational and executive surfaces produced
Remediation governed and tracked
Evidence and decisions preserved
Metric confidence legible over time
Book a Fintech Data Diagnostic
A focused $7,500 engagement. We audit your fintech data, score your metrics, and deliver a remediation roadmap in 2 weeks.
Book a diagnosticMonthly Metric Reliability Program
Ongoing monitoring, governed corrections, and executive-ready reporting. Starting at $2,500/month.
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