A leading financial services company faced growing challenges in managing its lending and leasing operations. Data was fragmented across systems, manual reconciliations delayed close cycles, and reporting lacked the agility needed for compliance and executive decision-making.
To solve this, the company implemented a modern architecture built around Oracle Financial Services Lending and Leasing (OFSLL), supported by a robust analytics and reporting framework. The solution enabled a single source of truth across finance, operations, and risk- improving performance visibility, streamlining compliance, and preparing the business for AI-driven insight.
The legacy LMS had become a constraint across the business. Teams were relying on spreadsheets, disconnected tools, and slow manual processes to reconcile core financials, track performance, and respond to audits. Leadership lacked the visibility needed to confidently manage receivables, portfolio health, and regulatory compliance.
The business faced:
Disconnected data and reporting across finance, collections, remarketing, funding, and legal
Manual reconciliations and workarounds that drained resources and delayed month-end close
No unified operational view—performance data was spread across dozens of static reports
Regulatory pressure around CECL, 1099-C, and military account handling
Out-of-the-box OFSLL reports that didn’t meet the business need – each report operated in isolation with no consolidated dashboards or executive visibility
They needed an architecture that could support scale, a data model the business could trust, and insights that would actually help teams work smarter.
Persepta’s team was a leading element of the full transition from legacy LMS to OFSLL – guiding the client through system architecture, operational alignment, data conversion, reporting strategy and go-live execution. But most importantly, we built the tools that enabled the business to validate the conversion, gain control over performance metrics and move forward with confidence.
Enterprise architecture for all OFSLL environments (dev, test, prod)
Full legacy system migration, including conversion logic, trial runs, validation tooling, and cutover execution
Operational performance tuning and NTP scheduling to support high-volume data activity
Custom-built reporting layer to support internal validation and daily operations
Automation of key compliance workflows, including 1099-C, SCRA tracking, and CECL data readiness
Cross-functional analytics framework delivering hundreds of performance KPIs across the business
Rather than drop in a pile of static reports, we delivered real-time visibility into core operations—giving each team the specific insights they needed to take action, respond to change, and improve performance.
Receivables health, cash activity, suspense and reversal tracking
Daily GL reconciliation against operational data
Close process support with drill-down transparency
Promise-to-pay performance
Queue aging and account flow
Charge-off, settlement, and deficiency analysis
Recovery outcomes by strategy and region
Auction returns by dealer, state, and product
Repo-to-liquidation timelines
Recovery rate comparisons and cycle performance
Contract eligibility and funded volume by dealer
Risk segmentation by recourse, geography, and credit tier
Dealer chargeback and credit exception metrics
Title processing timeframes and dealer reconciliation
Military account compliance tracking
Legal expense trends and state-level compliance activity
What started as a system migration became a foundation for operational excellence. The organization gained:
Clean, trusted data across the entire loan lifecycle
Automated validation tools that accelerated reconciliation and audit readiness
Role-based insights that empowered decision-makers in every department
Stronger compliance posture with proactive reporting and controls
A single operational data layer, replacing fragmented reports and outdated workaround
With the core system and data foundation in place, the client is now ready to pursue AI-driven opportunities across the business. Their new architecture supports the following AI future roadmap features:
Predictive collections modeling
Risk-based pricing optimization
Early warning systems for delinquency and recovery
Dealer performance scoring
CECL forecasting and stress testing at the portfolio level
By building it right from the ground up – migrating with precision and embedding insight into every layer – the business is now positioned for the next phase of transformation: one led by data, automation, and intelligence.
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