A blueprint for moving loan servicing off a legacy CRM and shared inboxes onto Service Cloud, where the data model, audit trail and migration carry regulatory weight.
This is a blueprint, not a client case study. The situation described is a composite of the conditions we design for, not a named engagement. Targets below are measures we would baseline and report against — not results already achieved.
A mid-market lender services around 60,000 active loans across three products. Customer servicing runs on a CRM bought nine years ago, extended until the vendor stopped supporting the customisations, plus four shared mailboxes and a collections spreadsheet.
Agents open three systems to answer one question. A borrower calling about a hardship arrangement exists as a contact in the CRM, an account in the servicing platform, and an email thread in a mailbox — with no reliable link between them.
The regulatory problem is sharper than the efficiency one. When a complaint escalates, reconstructing what was said and when takes days of mailbox searching, and the audit trail depends on whether an agent remembered to log the call.
The core loan servicing system is the system of record for balances and payments. Salesforce sits alongside it, never in front of it, and must not become a second version of financial truth.
Regulatory obligations require reconstructing the full interaction history on demand. The audit trail has to be a by-product of the workflow, not an agent's discipline.
The legacy CRM has duplicate contacts, orphaned records and free-text fields carrying structured meaning. Migration cannot assume anything about it.
Agents take calls every business day. Cutover has to happen without a servicing blackout, and rollback has to be genuinely available.
Access to sensitive borrower data is governed by role, and segregation of duties has to be demonstrable to an auditor.
The shape of the solution, before any of the detail below.
Read-heavy, write-narrow integration
These are the calls that determine whether the engagement works — most of them made before any build starts.
The natural Salesforce instinct is a contact-centric model. For servicing, the loan is the organising object — a borrower may hold several, a loan may have multiple parties, and hardship arrangements attach to the loan rather than the person. We design the object model against real servicing scenarios before touching configuration.
Salesforce reads balance, status and payment history from the servicing platform, and writes back only a deliberately narrow set of servicing actions through a defined API contract. This keeps the financial system of record unambiguous and keeps integration surface small enough to reason about.
Omni-Channel routing, email-to-case and CTI integration mean interactions are captured because they flowed through the platform, not because someone logged them. Field history tracking on sensitive fields, and an immutable interaction timeline on the loan record, make reconstruction a query.
Source profiling first — how many duplicates, how many orphans, which free-text fields carry structured meaning. Then a mapping design, a deduplication and merge strategy with rules agreed by the business, and repeated full-volume dry runs with reconciliation reports until variance sits inside an agreed tolerance.
One loan product moves first with its servicing team. Two weeks of stabilisation, then the next. This limits blast radius, gives the second wave a trained internal support network, and makes rollback a realistic option rather than a paragraph in a plan.
Durations are indicative for an engagement of this shape. Yours would be sized during assessment.
Servicing process mapping, regulatory requirement capture, and a full profile of the legacy CRM data before any design decisions are made.
Activities
Object model, security model, integration contract and migration strategy designed and validated against real servicing scenarios.
Activities
Iterative configuration with fortnightly playbacks to the servicing team, and integration built against a contract agreed in design.
Activities
Full-volume migration dry runs with reconciliation, UAT against real servicing scenarios, and compliance sign-off on the audit trail.
Activities
Product-line-by-product-line go-live with stabilisation windows between waves, then handover into managed support.
Activities
Every one of these is baselined before work starts, so the comparison at the end is defensible rather than flattering.
| Measure | Target | How it's measured |
|---|---|---|
| Systems touched per enquiry | OneObserved agent workflow sampling before and after, measuring how many applications an agent opens to resolve a standard servicing enquiry. | Observed agent workflow sampling before and after, measuring how many applications an agent opens to resolve a standard servicing enquiry. |
| Interaction history reconstruction time | Minutes, self-serviceTime for a compliance officer to produce a complete interaction history for a given loan, measured against the pre-migration baseline of days. | Time for a compliance officer to produce a complete interaction history for a given loan, measured against the pre-migration baseline of days. |
| Migration reconciliation variance | Inside agreed tolerance, all objectsRecord counts and field-level checksums between source and target, signed off before each cutover wave. | Record counts and field-level checksums between source and target, signed off before each cutover wave. |
| First-contact resolution | Baselined at wave 1, tracked per waveCases closed without transfer or callback, reported per queue in the monthly service review. | Cases closed without transfer or callback, reported per queue in the monthly service review. |
| Audit evidence completeness | 100% of interactions captured structurallySampled audit against the interaction timeline, validating that capture does not depend on agent logging discipline. | Sampled audit against the interaction timeline, validating that capture does not depend on agent logging discipline. |
Named up front. A risk register written after something goes wrong is just a post-mortem.
Data remediation is planned as its own workstream with its own timeline from week one. Dry runs surface reality early enough for the plan to absorb it.
The integration contract defines a narrow write path. Balance and payment data is read-only in Salesforce, enforced at the field-security level rather than by convention.
Declarative-first is a stated principle, and every Apex decision goes into a decision log with the reason recorded for future upgrade review.
Wave one super-users are embedded into wave two training. Adoption is measured by queue with intervention triggered on specific teams rather than reported in aggregate.
It never does. Tell us where yours differs and we'll tell you honestly whether we're the right people for it.
Or email us directly at reachus@segakiv.com