AssetArchive Analytics
AssetArchive provides real-time ABL data analytics, trend analysis, anomaly detection, and optional confirmation tools designed to help lenders identify exceptions, unusual items, and potential fraud early in the process.
The system addresses long-standing industry concerns: cat‑and‑mouse borrower behavior, unusual invoices, fake accounts, and the lack of time to investigate underlying data beyond month‑to‑month trends.
AssetArchive enables operations teams and account managers to investigate anomalies as reports arrive, perform debtor‑level step‑down analysis, correct inconsistent customer names and addresses, and run risk‑based confirmations.
Why AssetArchive Matters
Lenders have long needed a way to archive AssetReader data to extract trends, compare borrowing bases, analyze concentrations over time, and generate focused confirmations. AssetArchive was built specifically to meet these needs.
With over 40 years of ABL data analysis experience, including audit, operations, fraud investigations, and training, FinSoft developed analytics that highlight exceptions, anomalies, and unusual items automatically.
AssetArchive combines AI, machine learning, and business intelligence to uncover unusual items within large volumes of collateral data.
The integrated analytics and optional confirmation module make AssetArchive one of the most intelligent monitoring tools available in ABL risk management.
Key Capabilities
- Calculate ineligibles in minutes and age reports correctly.
- Archive AssetReader data for trends, averages, variances, and exceptions.
- Corporate-wide access to reports.
- Concentration trend analysis over time.
- Identify new debtors requiring additional review.
- Machine learning “Circuit Breakers” that retain borrower-specific thresholds.
- Risk-based exception identification for immediate inquiry.
- Optional confirmation module with custom letters and tracking.
| Features | Benefits |
|---|---|
| Downloaded data | Calculate ineligibles in minutes; age reports correctly |
| Data details | Factoring-like detail without the work and cost |
| Archive of AssetReader data | See trends, averages, variances, and exceptions |
| Corporate-wide access | Operations-driven, but available company-wide |
| Concentration trends | Assess AR and AP concentration trends over time |
| New debtors | Identify new account debtors requiring additional review |
| AI “Circuit Breakers” | Machine learning thresholds for each borrower; highlight risk-based items; supports confirmation module |
| Invoice value testing | Highlight invoices exceeding defined parameters |
| Debtor value testing | Highlight balances exceeding defined parameters |
| Track ineligible changes | Highlight material changes in ineligible balances |
| Step-down testing | Aging, per-debtor, and invoice-level step-down analysis |
| Time and cost savings | Reduce preparation time for monthly reports |
| Optional confirmations | Risk-based confirmations via email, fax, mail, or phone; custom letters; exclusion rules; tracking; name/address corrections |
| MS SQL or Oracle | Robust server support |
| .NET Core | Modern design, speed, deployment |
| More... | Call for a demonstration |
Why AssetArchive Delivers Results
AssetArchive gives lenders and operations teams the ability to analyze collateral data over time, identify exceptions, and investigate anomalies that are often missed in month‑to‑month reviews.
The system was designed to address long‑standing ABL challenges: inconsistent customer names, unusual invoices, fake accounts, new debtors requiring review, and limited time to investigate underlying data.
With optional confirmation tools, lenders can generate risk‑based confirmations, track results, and produce summary reporting. Custom letters, exclusion rules, and address corrections make the confirmation process faster and more accurate.
AssetArchive’s analytics, machine learning, and business intelligence combine to create one of the most intelligent monitoring tools available in ABL risk management.
If you have not seen a demo, let us WOW you with what we have automated.