Business case
Servicing operations
Mortgage servicing data modernization
Servicing and banking data had to move from the mainframe to Azure while business and technical teams aligned on the new data foundation.
Services
Data & Cloud Modernization
Trusted data, modern platforms, ready for what comes next.
We help banks, mortgage lenders, and servicers move from fragmented legacy data estates to governed, cloud-ready foundations — so reporting, AI, and operational change run on data the business can trust.
The questions we hear
- How do we consolidate loan, customer, and general-ledger data spread across cores, LOS, and servicing platforms?
- Which workloads belong in the cloud first — and what has to be remediated before they move?
- How do we build a data foundation that AI and advanced analytics can actually rely on?
- How do we migrate without breaking regulatory reporting or downstream feeds?
The challenges underneath
Fragmented source estates
Loan-level data lives in systems like ICE Encompass and MSP, servicing records in platforms such as Sagent LoanServ or Dara, and core banking data in Fiserv or FIS IBS/HORIZON environments — each with its own structures, timing, and quality rules. Reconciliation across them is manual, slow, and fragile.
Legacy dependencies
Decades of batch schedules, point-to-point interfaces, and undocumented transformations mean a single change can ripple through dozens of downstream reports. Modernization stalls when nobody can map what depends on what.
A cloud platform without a trusted data foundation
Standing up Microsoft Azure or another cloud platform is the easy part. Without governed ingestion, master data rules, lineage, and reconciliation controls, the cloud becomes a more expensive copy of the same problem.
Capabilities
- Data estate assessment and target-state architecture
- Cloud landing-zone design and governed ingestion patterns
- Migration planning, execution, and reconciliation
- Master data, lineage, and data quality frameworks
- Analytics and AI-readiness foundations
- Decommissioning roadmaps for legacy data stores
Outcomes we work toward
- A single governed view of loan, customer, and financial data
- Faster, defensible regulatory and management reporting
- Reduced run-cost from retired legacy stores and interfaces
- A data platform AI initiatives can build on with confidence
Approach & outputs
Step 1
Assess
Inventory sources, interfaces, and quality issues; quantify the case for change. Output: source inventory and dependency map.
Step 2
Architect
Define the target platform, governance model, and migration sequencing. Output: target architecture and sequenced migration roadmap.
Step 3
Migrate
Execute in controlled waves with reconciliation at every cutover. Output: source-to-target mappings and reconciliation plan.
Step 4
Operate
Embed ownership, quality controls, and decommissioning discipline. Output: ownership matrix, quality controls and retirement roadmap.
Platform context
Experience working across client platforms and vendor environments.
Microsoft AzureICE Encompass / MSPSagent LoanServ / DaraFiserv core platformsFIS IBS / HORIZON
Technical detail
How the data work is structured
Source formats and identifiers
We inventory each source by format (fixed-width extracts, delimited files, database tables, API payloads) and the identifiers it carries — loan, account, customer, investor — so keys can be matched and duplicates resolved before data moves.
Batch dependencies
Nightly and intraday schedules, upstream cut-off times, and downstream consumers are mapped so migration waves respect the order in which data is produced and used.
Source-to-target mapping and reconciliation
Field-level mappings carry transformation rules and are paired with reconciliation checks — record counts, balances, and control totals — at every load and cutover.
Target data products
Target structures are defined as data products with a stated purpose, consumers, refresh cadence, and quality rules, rather than as copies of source tables.
Ownership and acceptance
Each data product has a named business owner and agreed acceptance criteria, so sign-off is a decision with evidence rather than an assumption.
Applied in our programs
Who we serve
Start your transformation
Choose an assessment, mobilization or scope-led enterprise engagement for your priority.
Selected resources
Thinking and tools for the work ahead
Business case
Data & cloud
Bank document-management automation
A bank needed document-management automation within its wider workplace technology program, connecting information handling with business use.
White paper
Data & cloud
Mainframe to Cloud: Five Migration Decisions
Modernize the data foundation while preserving the operating commitments that depend on it.
White paper
Data & cloud
From Servicing Data to Trusted Data Products
Make data dependable at the point where servicing, finance and analytics use it.
Starter kit
Data & cloud
Data Discovery & Readiness Kit
Profile approved local CSV exports and establish source ownership, quality gaps and next decisions.
Start the conversation