Migrate from Mambu to Apache Fineract

Migrate from Mambu to Apache Fineract: what the move involves

If your Mambu renewal, a pricing change or a residency or lock-in concern has put a move to Apache Fineract on the table, plan for a full data, configuration and integration migration, not a lift-and-shift. The core is only one part of the work: product rules, client and account history, schedules, accounting postings and every downstream feed must be rebuilt and proven on the new platform. This work needs a banking-capable team, and it is usually led by bank technology, product and operations staff, often with an implementation partner for the Fineract build and data work. Apache Fineract is licensed under Apache 2.0 and is a headless application with REST APIs. It does not bundle its own user interface, so your staff, customers and channels will need a front end, which the Mifos X web application can provide in the Kubernetes example the project points to, or which you build yourself.

The short answer on effort and risk is that the software license is the easy part. The Fineract README warns that a production deployment can be complex, costly and time-consuming, that the project does not provide a comprehensive production deployment guide, and that organizations may pay a vendor for deployment and maintenance. Plan for hosting, database operations (PostgreSQL is the recommended database), backup and recovery, security testing, monitoring and upgrades to be owned by your team or a contracted partner from day one. Ask any partner for a written scope covering environments, the data extraction approach, reconciliation evidence and cutover rehearsal, because these items decide whether the project stays on schedule. Confirm the current Fineract release and its support status on the Apache Fineract website before you fix a target version.

Which Mambu capabilities map cleanly to Fineract and which need rework

This table maps common Mambu capabilities to their closest Apache Fineract equivalents and marks each as clean, needs rework or gap. Read each row as a starting hypothesis, since your product catalogue, rules and integrations decide the final status. Verify every row against your own product configuration before you commit to a migration scope.

Mambu capability	Fineract equivalent	Status and what to confirm
Client records and KYC documents	Client data and KYC documents in the Fineract core	Clean for standard fields. Confirm custom client attributes, document types and retention rules your KYC process depends on.
Loan products and schedules	Configurable loan products with business rules	Needs rework. Map each product's interest, fee, repayment and penalty rules to a Fineract product, then test schedules line by line.
Savings and deposit accounts	Savings portfolio management	Needs rework. Confirm interest posting, tiered rates and dormancy rules, since these often need custom configuration or code.
Accounting and general ledger postings	Real-time integrated accounting	Needs rework. Confirm chart of accounts mapping and posting rules, and agree the reconciliation approach with finance before cutover.
Reporting	Reporting capability in Fineract	Needs rework. Regulatory and management reports built on Mambu exports must be rebuilt or re-pointed to Fineract data.
REST APIs and integrations	Headless REST APIs with batch and event options	Needs rework. Every Mambu integration must be re-implemented against Fineract endpoints. Confirm event delivery through ActiveMQ or Kafka if you rely on events.
Multi-tenancy	Tenant identifier header and per-tenant databases	Clean as a design pattern. Confirm how tenants map to your legal entities and environments.
Vendor-managed upgrades and cloud regions	No equivalent; your team or partner operates upgrades and hosting	Gap to confirm. Upgrade timing, backups, availability targets and region choice become your responsibilities.
Managed integrations and vendor-run support tooling	No direct equivalent; custom integration and monitoring needed	Gap to confirm. Identify each managed feature in use and plan its replacement before scoping.
Product rules with no Fineract configuration	Custom development or a documented workaround	Gap to confirm. Any rule without a standard configuration needs a design decision, testing and ongoing ownership.

How clients, accounts and schedules move out of Mambu

The extraction plan starts with the export path, and the choice of path decides how much transformation work lands on your team. Mambu's APIs can be read directly, Mambu Extract and the Streaming API support different data-pull patterns, and API database backups give you a point-in-time copy to work from. Confirm with Mambu which of these your tenant and contract include, and how each handles field-level detail for loans, deposits and schedules. The sibling page on exporting data from Mambu covers the mechanics of each path, so this section focuses on what you extract, in what order, and how it lands in Fineract.

Extract in dependency order. Start with clients and KYC documents, because every loan and savings account references a client and Fineract will not hold orphaned accounts. Next, pull product definitions, since accounts depend on the product rules that governed interest, fees, repayment frequency and amortization when they were opened. Only then extract accounts, current balances, repayment schedules and transaction history. Keep the original Mambu identifiers in a mapping table for every entity, so reconciliation can trace each Fineract record back to its source. Record the export timestamp for every batch, because a portfolio that keeps accruing during extraction will otherwise fail balance checks.

Fineract's recommended database is PostgreSQL, and the project's FSIP-9 proposal explains why MariaDB and MySQL are deprecated, so plan the target schema around PostgreSQL from the outset. Transformed data must match Fineract's own model, which differs from Mambu's in entity names, status codes and how schedules are represented. Load each batch into the correct tenant: every API request carries the Fineract-Platform-TenantId header, and tenant connections are resolved from the tenants database, so a loader that omits or misroutes that header will write records into the wrong tenant. Test this with one tenant and a small client set before running the full load, and confirm your loader's tenant configuration with the Fineract documentation for your release.

Which data sets and integrations need to be rebuilt

Six parallel workstreams make up a Mambu to Fineract move, and each one has a different source of truth on each side. Treat them as separate work packages with their own owners, extraction paths and reconciliation checks, because a gap in one (for example, a missing schedule or an unmapped fee) surfaces later as a balance difference in another.

Client and KYC records

Clients, identifiers, contact details and KYC documents must be exported from Mambu through its documented paths and loaded into Fineract's client model, which covers client data and KYC documents. Confirm with Mambu which document binaries are included in each export and which are referenced only by link. Decide early whether KYC evidence moves with the client record or stays in the document store of record, and check that every migrated client has a matching tenant and office in Fineract before any account loads.

Loan and savings portfolios with schedules

Open loan and savings accounts, their balances, repayment schedules, interest accruals and transaction history must be extracted per account and rebuilt as Fineract accounts with matching state. Fineract recalculates schedules from product rules, so an imported schedule can differ from the one Mambu produced if the product parameters are not identical. Ask Mambu which fields the Streaming API and database backups expose for schedules, and reconcile principal, interest and fees account by account before go-live.

Product configuration

Each Mambu loan and deposit product, including interest rules, fee structures, penalty logic and repayment frequencies, must be mapped to an equivalent Fineract product definition. Fineract's configurable products and business rules cover much of this, but some Mambu behaviours will need a documented decision: accept a different outcome, customize Fineract, or leave the product out of scope. Record every mapping decision in a signed-off matrix so auditors can trace why a parameter changed.

General ledger and accounting mappings

Mambu's accounting entries must be mapped to Fineract's real-time integrated accounting, which means every product, fee and payment type needs a defined ledger account and journal posting rule. Extract the accounting configuration and historical postings from Mambu, then confirm that the opening trial balance in Fineract equals the Mambu balance at the cutover date. Check with your finance team how intercompany and suspense accounts will be handled, since these are rarely covered by product mappings.

Payment and card integrations

Payment rails, disbursement and repayment channels, and any card or token references tied to Mambu must be inventoried and re-pointed to Fineract or to a separate payment layer. Mambu's own integration endpoints will stop serving the institution once the core moves, so each inbound and outbound interface needs an owner and a test plan. Confirm with the payment providers whether existing mandates and tokens can be reused, and keep card data out of the core extract unless your PCI DSS scope has been agreed in writing.

Reporting and regulatory feeds

Regulatory returns, credit bureau submissions, management reports and data warehouse feeds that read from Mambu must be rebuilt against Fineract's REST APIs or its database, depending on the approach you choose. Identify every consumer before cutover, since a feed that breaks silently can go unnoticed until a filing date. Ask Mambu for the reporting field definitions they publish, and agree in writing with each report owner which figures must match the old system during parallel running.

Migration sequence from inventory to decommission

This sequence moves a Mambu lending and savings portfolio to self-hosted Apache Fineract in controlled stages, with each stage closing on a written artifact that the next stage depends on. The order keeps the live Mambu core authoritative until reconciliation and a parallel run have confirmed the Fineract balances.

Inventory products, accounts and integrations
Your product owners, finance team and integration engineers list every loan and savings product in Mambu, the account populations on each, the scheduled jobs, the downstream feeds into the general ledger, reporting and card or payment systems, and the reports regulators receive. The output is a scoped inventory register that names each product, its volume, its owner and every interface that reads or writes banking data. You know this step is done when the business owners sign off that nothing in production is missing from the register.
Build the product mapping register
Architects and product specialists map each Mambu product's configuration, including interest rules, fee and penalty logic, repayment schedules, day-count conventions, accounting mappings and business rule settings, to the closest Fineract product definition. Every mismatch is recorded as a gap with a decision: rebuild, approximate with documented business rule, or retire. The output is a product mapping register signed by finance and risk. The step closes when every product in the inventory has a mapped target or an approved retirement decision.
Stand up a pilot tenant and Fineract environment
Platform engineers deploy a non-production Fineract environment in your own infrastructure, using PostgreSQL as the recommended database. Fineract is multi-tenant, so the pilot tenant is created through the tenants database and addressed with the Fineract-Platform-TenantId header. The Docker images are not production-ready, so the build must be hardened and tested before any production use. Read, write and batch instances are set by environment variables. Liquibase migrations run only on write instances, so on Kubernetes you run them separately with the liquibase-only Spring profile before starting the other instances. Read instances can point at database read replicas, and batch instances run scheduled work separately from the write path. The output is a documented, reproducible build and a runbook for the pilot tenant. The step closes when the product definitions from the register load cleanly into the pilot.
Extract and transform client and account data
Data engineers pull clients, KYC references, loan and savings accounts, repayment schedules, balances and transaction histories from Mambu through its APIs or the export options described in the Mambu export guidance. They transform the data into Fineract's client, account and schedule structures, and they keep each transformation rule in version control. Personal data is handled under your data protection controls, and extracts stay in your own environment. The output is a versioned transformation specification and a set of staging loads with row counts for every entity. The step closes when a full pilot load completes and every rejected record has a documented cause.
Run reconciliation on balances and schedules
Finance and operations staff compare Fineract against Mambu at the account, product and ledger levels: principal, interest, fees, arrears, deposit balances and the next scheduled events. Discrepancies are sorted by root cause, either a mapping gap, a transformation error or a real difference in calculation logic, and each is fixed and re-run. Reconciliation runs repeat after every material change to the mapping or data load. The output is a reconciliation report per run, with a list of accepted differences signed by the business owner. The step closes when the last run shows no unexplained differences above the thresholds your finance and risk teams set.
Operate a parallel run
For a defined period agreed in advance, Mambu stays the system of record for customers while Fineract processes the same transactions in shadow, or the two run side by side on a subset of products. Operations teams compare daily postings, schedule outcomes and ledger entries, and they test the reporting and integration paths that depend on the core. Monitoring and incident handling are exercised against the Fineract environment. The output is a parallel run log and a list of open issues with owners and status. The step closes when the agreed exit criteria in the parallel run plan are met and the steering group records the decision to proceed.
Execute cutover with a signed runbook
The cutover team freezes Mambu writes, runs the final extract and load, repeats reconciliation on the full population, switches integrations and reporting to Fineract, and confirms access and monitoring. Rollback steps are written and rehearsed in advance, including the point at which the team decides to revert. The output is a signed cutover runbook with timed checkpoints, named approvers and recorded evidence for each checkpoint. The step closes when the business, risk and technology owners sign the go-live record and Fineract is the system of record for the migrated portfolio.
Decommission Mambu and retain evidence
Once the post-cutover period passes without unresolved issues, the team confirms the retention obligations for Mambu data, exports the archives your records policy requires, and works with Mambu on the contract end and data return or deletion steps. Access to the old tenant is removed, and integration credentials are revoked. The output is a decommission record, a retained archive with its index, and an updated register of data locations for your audit file. The step closes when the archive is verified and the contract exit is documented. Confirm the exact contractual notice and data return terms with Mambu, since they depend on your agreement.

Running Mambu and Fineract in parallel before cutover

A parallel run is the control that proves the migration, so decide before it starts which side is the system of record for each product and who signs off the daily reconciliation. Until cutover, Mambu should remain the system of record for every client, account and posting, and Fineract should receive the same loans and savings accounts as a shadow copy built from the extracted data. Name the owners now: a finance controller for balances and accruals, a product owner for schedules and fee rules, and an operations lead who decides whether a difference blocks the run. Confirm with your Mambu administrator which export route (the Streaming API, API database backups or Mambu Extract) feeds the daily comparison, since each has different latency and completeness.

Each business day, reconcile three things between the two cores: outstanding principal, interest, fees and penalties per loan or deposit account, the repayment schedule per account (installment dates, amounts and remaining periods), and the journal postings generated for the day. Fineract's accounting module should produce the same ledger movements that Mambu produces, so compare by account and by transaction type rather than by total only. Accruals need separate attention: compare the daily interest accrual for a sample of accounts across each product type, including accounts in arrears, accounts with grace periods and accounts with penalty rules, because these are where product configuration mapping errors usually appear. Record every difference with an identifier, a root cause and an owner, and treat an unexplained difference as open until someone can show the cause in the product configuration or the source data.

Set rollback criteria before the first run, in writing, and agree who can invoke them. Typical criteria include any unexplained difference in principal or posted journals above a threshold the finance controller approves, any schedule mismatch on a live account, or a failed daily extract that cannot be re-run within the agreed window. Cutover should not proceed until every difference from the final parallel cycles is explained and closed, and the reconciliation evidence is retained for auditors. Note that Fineract's own documentation recommends PostgreSQL and warns that its images are not production-ready, so confirm that your reconciliation environment, backups and monitoring meet your own standards before relying on it as evidence.

What drives cost and duration of the Mambu to Fineract move

This table lists the cost and effort components of moving a lending or savings portfolio from Mambu to self-hosted Apache Fineract, with what raises or lowers each one and what to ask for in a quote. Read it as a checklist for scoping, since the largest swings usually come from product complexity, data quality and the number of integrations rather than from hosting.

Component	What raises or lowers it	What to ask for in a quote
Production infrastructure	Fineract's Docker images are not production-ready, so hardening, image builds and a PostgreSQL setup with read replicas add work. Multi-tenant layout, high-availability design and the number of read instances all change the bill.	Written list of hardening steps, the target topology (write, batch and read instances), backup and restore design, and which cloud or data center costs the client pays directly.
Product configuration	Each Mambu loan, deposit and fee product must be mapped to a Fineract product with equivalent interest, fee, schedule and accounting rules. Many bespoke products or custom fee logic raise effort, while a short, standard product catalog lowers it.	Product inventory with a mapping sheet, the list of products that will not map one to one, and sign-off steps for each product owner.
Data volume and quality	Client records, KYC documents, open loan schedules and savings balances in Mambu each need extraction, cleansing and validation. Duplicate clients, missing identifiers and inconsistent statuses increase the work.	Data profiling results, extraction method (Mambu API, Streaming API or database backup as applicable), cleansing rules and a data quality threshold for go-live.
Integration count	Every system that reads from or writes to Mambu today, such as payments, general ledger, credit bureau and reporting feeds, must be repointed to Fineract APIs or events. Fineract supports ActiveMQ and Apache Kafka for events, so the event design affects the scope.	Integration inventory, target design for each interface, test environments per integration and who owns the change on each side.
Reconciliation effort	Parallel runs, balance tie-outs by product and daily ledger matching drive the most staff time during cutover. The number of accounting entries and the frequency of reconciliation runs set the effort.	Reconciliation plan with tolerance rules, number of dry runs, reconciliation tooling, and the team and hours the client must provide.
Security testing	Testing the exposed REST APIs, tenant isolation via the Fineract-Platform-TenantId header, database access paths and the infrastructure layer adds scope. Regulated data and an external pen test requirement increase it.	Test scope document, methodology, retest terms and the findings report format. Confirm whether an independent auditor needs the evidence, since DeployOpen prepares evidence but does not issue audit reports.
Support and upgrades	Fineract publishes quarterly releases and archives end-of-life releases, so the upgrade cadence and testing per release recur. A ready front end, such as Mifos X, changes the front-end work.	Support hours, upgrade process, patch windows and whether the retainer covers testing of each quarterly release.
In-house skills to run the core	Running a core banking system needs Java, PostgreSQL, Kubernetes, messaging and banking operations skills. The project warns that production deployment can be complex, costly and time-consuming, so a skills gap adds cost either as hires or as a vendor contract.	Named skills the client team must hold, a training plan, a runbook handover list and the roles that remain with the client after go-live.

What usually goes wrong in a core banking migration

Most failed core migrations do not fail in the final cutover. They fail earlier, when product rules, balances and documents are carried over without proof. Each risk below maps to a check in the reconciliation runs or the pilot stage, so problems surface while Mambu is still the system of record.

Product rules that do not map

Mambu product settings for interest, fees, grace periods and penalty logic do not translate one-to-one into Fineract loan and savings products. Some rules will need to be rebuilt as configuration, and others may need custom code. Prevention: build a field-by-field product mapping sheet and have finance sign off each rule before any data moves. Run the pilot on one product per type and confirm that each rule produces the same result for a sample of accounts.

Schedule and interest drift

Small differences in day-count conventions, rounding, or how the first period is calculated can compound over the life of a loan. Repayment schedules recalculated in Fineract may differ from the schedules customers were given in Mambu. Prevention: compare generated schedules for a sample of loans, line by line, during each reconciliation run, and treat any difference in principal, interest or fees as a blocker until it is explained.

Incomplete KYC and document transfer

Client records can arrive without their identity documents, consent records or ownership links, especially for joint accounts and corporate clients. Document files are often stored separately from the client record and need their own export. Prevention: count clients and documents per type at source and target in every reconciliation run, and sample files to confirm they open and link to the correct client.

Fineract production hardening underestimated

Fineract is a headless application with REST APIs and no built-in user interface, and its README warns that a production deployment can be complex, costly and time-consuming. Teams often plan for the application and underplan for database high availability, read and batch instance separation, backups, monitoring and security testing. Prevention: confirm the hardening scope in the pilot, and treat its results as a go or no-go input for cutover.

Multi-tenant configuration errors

Fineract isolates each institution or business unit by tenant, and API requests must carry the correct tenant header. A misconfigured tenant connection can route a migration batch or a read request to the wrong database. Prevention: include tenant-level checks in the pilot, such as confirming that test data appears only under the intended tenant, and run the same checks again after any configuration change.

Cutover without a tested rollback

A cutover plan that cannot return the portfolio to Mambu within an agreed window leaves the institution with no safe fallback. Teams sometimes rehearse the forward migration but never the reverse. Prevention: rehearse both directions during the pilot, record the time each step takes, and agree in writing who can declare a rollback and what evidence triggers it.

How the migration differs by portfolio size and starting point

A lending-only book is the simplest starting point because the product set is narrow and the balances that must reconcile are mainly principal, interest, fees and penalties on active loans. Product mapping focuses on loan product definitions: amortization method, repayment frequency, interest accrual rules, fee and penalty structures, and any rescheduling or top-up behavior. Each Mambu loan product should be traced to an equivalent Fineract loan product, and any behavior with no direct equivalent should be documented as a gap to be closed by configuration or by a documented business decision.

Loan product mapping: Build a table that links every active Mambu loan product to a Fineract loan product, including the schedule and accrual settings that drive the repayment calendar.
Single tenant is often enough: A lending-only institution typically needs one Fineract tenant unless it runs separate legal or reporting perimeters.
Loan schedule validation: Compare generated schedules for a sample of loans across each product before any balances move.
Reconciliation scope: Reconcile outstanding principal, accrued interest, arrears and fee balances per loan, then at product and portfolio totals.
Cutover sequencing: Freeze new disbursements in Mambu for a defined window, extract a closing snapshot, load it, and then reopen disbursements in Fineract only after reconciliation passes.

Confirm with your team how Mambu represents restructured and rescheduled loans, because those histories are often the hardest part to replay correctly.

Questions buyers ask before moving off Mambu

How much does moving from Mambu to Apache Fineract cost?

The cost is mostly people and time, not the software. Fineract is Apache 2.0 licensed, so there is no license fee, but you pay for infrastructure, a production deployment, product configuration in Fineract, data extraction and transformation from Mambu, reconciliation, security testing, and ongoing support and upgrades. Infrastructure and staffing recur every year, while migration work is largely one-time. The Fineract README warns that a production deployment can be complex, costly and time-consuming, and that organizations may pay a vendor for deployment and maintenance. Ask any implementation partner for a quote built from a written scope that lists each product, each data set and each integration.

How long does a Mambu to Fineract migration take?

The timeline is set by portfolio size, the number of loan and savings products that need mapping, how many integrations depend on Mambu, and how much reconciliation your finance and risk teams require before cutover. The Fineract documentation does not give a standard duration, so treat any number you hear as unverified until it is tied to your own product inventory. Plan the work in stages: product mapping, extraction, test migrations, reconciliation runs, a parallel run and then cutover. Each stage can repeat until balances, schedules and accounting entries match.

What does the bank's team need to provide for the migration?

The bank needs to provide product owners who can explain each Mambu product's rules, finance staff who own the general ledger and chart of accounts, and risk and compliance staff who can approve reconciliation results. Engineers need API credentials and access to Mambu's data export options, a target environment in your own cloud or data center, and access to integration owners for payments, reporting and data warehouses. Confirm early who signs off on balances, accruals and KYC records, because those decisions drive the scope more than the code does.

Can our own team run Apache Fineract after the migration?

Your team can run Fineract only if it has the skills and capacity for a self-operated core banking system. Fineract is headless, so it has REST APIs but no built-in user interface, and it needs a database (the README recommends PostgreSQL), a deployment pipeline, monitoring, backups, upgrades and security hardening. The project publishes quarterly releases, and its Docker images are not production-ready on their own, so you will need to harden and test them. Many institutions combine an internal platform team with an external partner for deployment, support or retainer-based upgrades. Assess the skills gap before committing to a cutover date.

How does a parallel run work when moving off Mambu?

In a parallel run, Mambu and Fineract both process the same portfolio for a defined period, and the results are compared. Typical checks include loan balances, interest accruals, repayment schedules, savings balances and general ledger postings. Mismatches are investigated and either fixed in the mapping or documented as accepted differences. Fineract's multi-tenant design and separate tenant databases allow test tenants to be set up for these comparisons without touching production data. Agree the exit criteria for the parallel run in writing before it begins, so that cutover is a decision and not a hope.

What happens to KYC documents and client data during the move?

Fineract covers client data and KYC documents in its feature set, so client records and their attached documents can be moved into the new core. Files stored in Mambu must be retrieved through its export options or APIs, checked for completeness, and linked to the correct client records in Fineract. Confirm with your compliance team which documents must be retained, in what format and for how long, and where the source copies will be kept after decommissioning Mambu. Treat document migration as a separate reconciliation task with its own sign-off.

Does Apache Fineract need a front end, and what does that add to the project?

Yes, if your staff need screens. Fineract is headless and does not provide its own user interface, so you must either build one on its REST APIs or choose a front end. The Fineract README points to the Mifos X web application as the front end used in its Kubernetes example. Building a custom interface adds design, development, access control and testing work, which should be scoped separately from the core migration. Some lenders prefer a packaged distribution with a ready interface, and that choice should be made before product configuration starts.

When Fineract is the right destination and when it is not

Apache Fineract fits institutions that want to host the core themselves, set their own upgrade timing, and own the code, and that already have engineering capacity to run a core banking system. Its Apache License 2.0 and headless REST design let you place the ledger in your own cloud accounts or data centers and schedule releases against your own change calendar. The project ships quarterly releases, so you still need a process to test and adopt each one. Fineract does not give you a user interface. The README points to Mifos X as the front end used in its Kubernetes example, so your teams must either adopt Mifos X or build and maintain their own screens on the API.

Fineract is a poor fit if you lack in-house platform, database and banking operations skills, because the README warns that a production deployment can be complex, costly and time-consuming, and that the project does not provide a comprehensive production deployment guide. Its Docker images are not production-ready, and the README notes that organizations may pay a vendor for deployment and maintenance. If your team cannot staff on-call, Liquibase migrations, read and batch instance tuning and backup testing, a managed Mambu contract or a partner-run Fineract deployment may carry less risk. For a microfinance or inclusive lender that wants a ready front end, Mifos X may be the better starting point than raw Fineract.

Before committing, confirm three things: the loan and deposit products you need are covered by Fineract's configurable product model, the release you plan to run is still supported under the project's quarterly release and archive policy, and the operating model (who patches, who restores and who is paged) is funded and named. Ask Mambu for its own exit terms and data export options so the comparison is based on real costs, not on the assumption that migration will be cheap.

How DeployOpen scopes and runs a Mambu to Fineract migration

DeployOpen plans, implements, integrates, hardens, documents and supports self-hosted Apache Fineract inside your own cloud accounts, data centers or air-gapped networks. For a Mambu exit, the work is agreed in a written, fixed scope before any extraction starts, so product mapping, data movement, reconciliation and cutover are each defined and testable.

How we scope

Written scope lists which Mambu products, portfolios and client data move to Fineract
Product configuration mapping covers each loan and savings product, its fees, schedules and business rules
Scope names the data sources, API extraction approach and the reconciliation criteria that sign off each run

How we build

Fineract deployed with PostgreSQL, the database the project recommends, in your own environment
Extraction scripts pull clients, accounts and schedules from Mambu APIs and load them into Fineract tenants
Reconciliation runs compare balances, schedules and accruals between Mambu and Fineract before any cutover decision

How we hand over

Runbooks cover cutover steps, rollback criteria, batch and write instance operation, and Liquibase migration handling
Documentation records product mappings, reconciliation results and open exceptions for your own team and auditors
Knowledge transfer sessions prepare your operators to run Fineract after the engagement ends

How we support

Ongoing support, upgrades and continuous testing are available as a retainer after handover
Quarterly Fineract releases are reviewed and applied on a schedule agreed with your team
Compliance evidence is prepared for your independent auditors, which issue any attestation or certification

Migrate from Mambu to Apache Fineract