Segment
Who the customer is in market terms: bank, digital lender, EMI, asset manager, tokenization issuer, embedded-finance partner. Without the brand name.
Client Outcomes
What banks, lenders and issuers ship on CoreFi.
CoreFi customers run regulated workloads: direct lending books, modernized cores, wallet programs, tokenization. Where contractual confidentiality applies, we publish the story anonymized: segment, region, problem, modules used, outcome ranges, the implementation timeline that produced them, and what the customer is doing next.
How to read these stories
Seven fields per story. No fluff.
Every CoreFi client outcome is written to the same shape so a buyer can compare a digital-lender deployment to a bank modernization or a tokenization launch on the same axes. Where a customer has not authorized public attribution, segment, region and metric ranges replace names. The modules, the timeline and the outcome shape are real.
Region
The jurisdiction the deployment serves. Useful when the regulatory frame (ECSPR, MiCA, PSD2, CRR) changes how the story reads.
Problem
The operational or commercial constraint that drove the engagement: typically a capacity, time-to-market, cost or compliance gap the customer could not close internally.
CoreFi modules used
Which parts of the CoreFi platform were deployed: Core Banking Engine, Lending Automation, Customer Onboarding, Headless APIs, AI Workflow Control Plane, White-Label Channels, Asset Tokenization, Stablecoin Infrastructure, Custody, Cross-Border Payments.
Outcome (metric ranges)
Improvements expressed as ranges, never as invented precision. Percentages appear only where the customer's own measurement has been published in an anonymized article; everywhere else the outcome is described structurally and figures are available on request, subject to the customer's agreement.
Implementation timeline
Calendar weeks from kickoff to first production workload, plus the rollout shape (single market, multi-market, dual-core parallel run, full migration).
Next phase
Where the deployment goes from here: the workloads, markets or modules the customer has scoped as the next step. Stated as direction, not as a commitment on the customer's behalf.
Story 01 / Digital Lender at Scale
Direct digital lending, run on CoreFi without rebuilding the back office.
A regulated financial institution with a non-lending core business in asset management, corporate finance and advisory needed to operate a growing direct-lending book without diverting resources, hiring a servicing team or building a loan-management stack from scratch. CoreFi ran the platform and the servicing operation; the institution kept the customer relationship.
Segment
Regulated financial institution running direct lending as a non-core line; primary business in asset management and corporate finance.
Region
European Union. Single-market deployment, EU regulatory perimeter.
Implementation timeline
Roadmap analysis and platform set-up in weeks; data migration and connector build to existing systems; phased cutover with the customer's team trained on the LaaS console before live traffic.
The problem
The institution's loan portfolio kept growing, but the servicing workload that comes with 36–72 month tenors did not match its operating model. Each loan needed payment processing, late-fee handling, transaction reconciliation, accounting entries, borrower communications and audit-quality recordkeeping for the full life of the loan. Building the platform in-house meant a multi-year fintech project; outsourcing piecemeal meant fragmenting the audit trail. Neither matched the strategic priority, which was to keep capital and headcount on the core business.
What CoreFi delivered
CoreFi deployed an end-to-end Lending-as-a-Service stack on its Core Banking Engine: origination, underwriting, servicing, payment processing, late-payment management, customer service and reporting, all governed by the same permissioning and audit layer that runs the rest of CoreFi. Connectors integrated the LaaS platform with the institution's existing systems so the loan book lived in one record. CoreFi's servicing team operated day-to-day; the institution kept oversight and approvals through the LaaS console.
Modules used: Core Banking Engine, Lending Automation (Lending-as-a-Service), Customer Onboarding, Headless APIs, White-Label Channels.
Outcomes
Ranges below are taken from the customer's own measurement, as published in the anonymized case study.
~40%Less time spent on loan-servicing tasks across the team.
~25%Fewer late-payment incidents after automated detection and borrower communications.
~30%Higher consistency in borrower repayment behaviour, measured month-over-month.
~35%Lower operational cost of loan servicing under predictable LaaS pricing.
~50%Faster customer-service response times on borrower queries.
36–72 moLoan tenors fully managed end-to-end on the LaaS platform.
What changed in the business
Capital and management attention moved back to the institution's primary lines. Lending stopped being an operational tax: cost per serviced loan became predictable, the audit trail consolidated under one platform, and the loan book could grow without proportional growth in headcount. The customer's team kept the strategic decisions, pricing, credit policy and approvals, while CoreFi ran the platform, the workflows and the borrower-facing servicing.
Next phase
Growing the serviced book on the same stack, with collections and reporting workflows as the next automation surface. Updated figures land here when the customer authorizes them.
Story 02 / Bank AI Onboarding
A bank moving customer onboarding onto governed AI workflows.
A bank whose onboarding ran through manual review queues: every application waited for a human, regardless of risk. The cost sat in the review team; the abandonment sat with the applicants who waited. The bank moved the journey onto CoreFi's Customer Onboarding stack with the AI Workflow Control Plane in front of it.
Segment
Bank, onboarding retail customers under its own licence.
Region
European Union, EU regulatory perimeter.
Problem
Manual onboarding review for every application: cost growing with volume, applicant wait times driving abandonment, and KYC evidence scattered across tools.
CoreFi modules used
Customer Onboarding, AI Workflow Control Plane, Core Banking Engine, Headless APIs.
Outcome
Clean applications proceed straight through under policy gates; human review concentrates on the cases policy flags. Every decision, automated or human, writes to the same append-only audit record. The bank's measured figures are available on request, subject to the customer's agreement.
Implementation timeline
First journey live on the standard 8–10 week path, then iterative tightening of the policy gates with the bank's compliance team.
Next phase: extending the same governed-onboarding pattern to additional products, with the bank's reviewers keeping the approval gates they already operate.
Story 03 / EMI Wallet Program
An e-money institution launching a wallet program without building the core.
An e-money institution with a licence and a market, but no ledger, no onboarding stack and no servicing workflows. Building that plumbing in-house meant spending the launch window on infrastructure. The EMI launched the program on CoreFi instead.
Segment
E-money institution running a consumer wallet program under its own licence.
Region
European Union, EU regulatory perimeter.
Problem
Time-to-market: the licence and the distribution were ready before the infrastructure. An in-house ledger, onboarding and compliance build would have consumed the launch window.
CoreFi modules used
Core Banking Engine (accounts and ledger), Customer Onboarding, Headless APIs, White-Label Channels.
Outcome
The wallet program runs on governed rails: accounts, balances and transactions on one ledger, onboarding under policy gates, and an account base that grows without proportional growth in the operations team. The EMI's measured figures are available on request, subject to the customer's agreement.
Implementation timeline
First journey live on the standard 8–10 week path; subsequent releases shipped iteratively on the same deployment.
Next phase: additional payment rails and markets as the program grows, composed from the ecosystem layer per jurisdiction.
Story 04 / Bank Legacy Coexistence
An incumbent bank running CoreFi alongside its legacy core.
A bank that could not justify a big-bang core replacement, and did not have to. CoreFi runs selected journeys next to the legacy core: each workflow moves through discovery, shadow, parallel run and cutover on its own schedule, the pattern documented on /migration-coexistence.
Segment
Incumbent bank with an established legacy core and a modernization mandate.
Region
European Union, EU regulatory perimeter.
Problem
A full core migration carried more risk than the board would accept, while the legacy core made new journeys slow and expensive to ship. The bank needed a path that contained risk per workflow.
CoreFi modules used
Core Banking Engine running alongside the legacy core, Headless APIs, Lending Automation, with the phased coexistence pattern governing each cutover.
Outcome
Selected journeys run on CoreFi while the legacy core remains the system of record for everything not yet moved. On each migrated workflow, CoreFi’s audit record is structured to reconcile with what the legacy core records on the same case, and the share of work on CoreFi grows per workflow rather than in one cutover. The bank's measured figures are available on request, subject to the customer's agreement.
Implementation timeline
Phased per workflow: discovery, shadow, parallel run, cutover. Each workflow carries its own timeline and rollback point; no big-bang date.
Next phase: additional journeys on the same strangler-fig pattern, sequenced by the bank's own risk and value ranking.
Story 05 / Tokenized Asset Issuer
An issuer putting tokenized instruments on a governed, auditable rail.
An issuer bringing tokenized instruments to market needed the same things a bank needs from a core: an authoritative record, policy gates ahead of every action, and an audit trail a supervisor can read. The issuance program runs on CoreFi's tokenization stack under MiCA-aligned controls. Which regulatory regime applies to each instrument is the issuer's determination with its own advisers.
Segment
Issuer of tokenized instruments, operating under its own regulatory arrangements.
Region
European Union, MiCA-aligned control environment.
Problem
Issuing tokenized instruments without a governed rail meant gluing together wallets, registries and manual controls, with no single audit record behind the program.
CoreFi modules used
Asset Tokenization, Custody, Core Banking Engine, Headless APIs.
Outcome
Instruments are issued and serviced under the same policy gates and append-only audit record as every other workload on the platform: issuance, transfers and corporate actions reconstructible end to end. The issuer's measured figures are available on request, subject to the customer's agreement.
Implementation timeline
Scoped issuance program: platform set-up and control configuration first, then the issuance calendar on the customer's schedule.
Next phase: additional instrument types and distribution flows on the same rail, as the issuer's program and authorizations expand.
Story 06 / Embedded Lending Partner
A consumer-finance fintech embedding BNPL on a governed core.
A consumer-finance fintech offering Buy-Now-Pay-Later at the point of sale. The product lives or dies on approval speed, but every approval is still a credit decision that has to stand up to audit. The fintech runs the journeys on CoreFi, with the same audit record as the rest of the platform. The long-form story is published here.
Segment
Consumer-finance fintech embedding BNPL into partner checkouts.
Region
European Union, EU regulatory perimeter.
Problem
Point-of-sale lending demands instant decisions and merchant-grade integration, while every decision still needs underwriting discipline and an audit-quality record. Building both at once in-house did not fit the launch window.
CoreFi modules used
Customer Onboarding, Headless APIs, Core Banking Engine, Lending Automation.
Outcome
BNPL journeys run end to end on the governed core: real-time decisioning under policy gates, instalment servicing on the platform ledger, and one audit record from checkout to final payment. The fintech's measured figures are available on request, subject to the customer's agreement.
Implementation timeline
First journey live on the standard 8–10 week path, then merchant integrations added iteratively through the Headless APIs.
Next phase: additional merchants and markets on the same integration surface, with servicing capacity scaling on the platform rather than in headcount.
From single outcomes to a scaling model
Each story is one lever of the 200K to 2M growth model.
Onboarding conversion, lending automation, partner distribution and country replication: every outcome on this page maps to a lever in CoreFi's scaling model from 200K end users toward 2 million. The model is strategic, not a promised user count; the levers are the ones these customers already run in production.
Want a story like these for your book?
We will walk through the modules that fit your segment, the outcome ranges they typically produce, and a realistic implementation timeline for your jurisdiction. Reference calls with comparable customers can be arranged on request, subject to their agreement and under NDA.