Financial Services Data Modernization on AWS, Built to Survive the Examination
Every regulated data program carries two deadlines. The one your steering committee set, and the one your examiner set. Only the second one has consequences attached. Mactores is the agent-native AWS modernization firm, and financial services data modernization is the engagement we scope backwards from the examination. The evidence an examiner will ask for is generated while the system is being built, not reconstructed from memory eight months later.
The production date and the fee are both named in the statement of work before build starts. If Mactores causes the slip, Mactores carries the overage cost. That is a clause, not an assurance.
Start with your regulatory perimeter
Bring the estate, the frameworks in scope and the date you are working against.
A scoping conversation ends with a scope, a production date and a fixed fee, or a straight answer on what has to change first.
- 60–70%
- of engagement hours absorbed by agents rather than billed as analyst time
- 12 wks
- median time to production across Mactores engagements
- 21
- public case studies with named customers
- 200+
- AWS-certified engineers
- 2008
- building on AWS since; 18 years of production migrations
- AWS Services Partner
- Premier Tier
- AWS Specialization
- Agentic AI
- One of 7 AWS Competencies
- Migration & Modernization
- AWS Service Validations
- 17 Validations
Top Mactores Clients
What Stops a Core Banking Modernization Program From Reaching Production?
Anyone who has run one knows the sequence. A data program is scoped against a business case, staffed, and given an architecture. Nine months later the steering committee is reviewing its fifth target-state diagram, the audit function is asking questions nobody costed, and the vendor is still billing whether or not a single workload has moved. The program does not fail. It just never finishes, and the examination arrives anyway.
The scale of that pattern is documented. IBM’s Institute for Business Value surveyed 700 global business leaders whose banks were behind schedule on core platform modernization and published the finding as The 94% core banking problem: 94% of core banking modernization projects run past their planned timeline.
Separately, the Basel Committee’s January 2026 newsletter on risk data aggregation named data lineage and traceability as one of the areas where supervisors still see the least progress, more than a decade after BCBS 239 was issued, and noted that banks are only beginning to use agents for data curation and quality work.
And when the failure becomes a breach rather than a delay, IBM’s 2026 Cost of a Data Breach report puts financial services second only to healthcare at $6.29 million per incident, against a US average of $11.5 million that the report attributes in part to rising regulatory fines.
Read those three together and the shape of the problem is specific rather than general. The work that produces examination evidence, meaning lineage, traceability and reconciliation, is the work that gets deferred, because in a traditional delivery model it is the most expensive hour on the invoice and the easiest line to move to phase two. Mactores inverts that. The evidence is a byproduct of how the migration runs, which is only affordable because agents are doing the volume.
Where the program is now
If a data modernization attempt has already stalled once inside your institution, tell us where it stopped.
Knowing which gate it died at shapes the scope, and it usually shortens it.
Which Regulatory Frameworks Does Financial Services Data Modernization Have to Answer To?
These engagements are engineered against the rules that decide how a US financial institution is actually examined. One boundary is worth stating before the table: building to a framework and being certified against one are different things. The certificate is yours to hold and your assessor’s to grant, and nothing on this page is an attestation of anything.
What each framework asks for, and where the build produces it
| Framework | What it governs | How the build answers it |
|---|---|---|
| FFIEC IT Examination Handbook | Data governance, technology risk management and third-party risk for banks and credit unions under federal examination | Governance and access boundaries are set in the landing zone during gate 02, before any workload arrives, and the gate record is the third-party oversight artifact |
| SEC cybersecurity disclosure and governance rules | Governance transparency and incident-reporting obligations for public financial institutions | Decision logs and change records are produced continuously, so the disclosure timeline is supported by contemporaneous evidence rather than reconstruction |
| FINRA recordkeeping and supervision | Books-and-records retention and supervisory review where broker-dealer data is in scope | Retention and immutability requirements shape the target storage design rather than being retrofitted onto it |
| GLBA Safeguards Rule | How customer financial information is protected, including while it is in motion between systems | Encryption in transit and at rest, and least-privilege access, are configured before migration begins rather than remediated after |
| SOX internal controls | Controls over financial reporting systems and the data pipelines feeding them | Pipeline changes carry an approver, a diff and a test result, which is the control evidence a SOX walkthrough asks for |
| PCI-DSS | Any system storing, processing or transmitting payment card data | Cardholder scope boundaries are drawn during target architecture design, so the scope does not quietly expand during migration |
| SOC 2 | Engagement practices and system-level control reporting | Gate acceptance records and validation output feed directly into the control evidence set |
A framework named here tells you what the engineering was built to satisfy. It implies nothing about endorsement by that body, and it is not a certification. Where AI components sit inside the scope, the architecture also gets reviewed against the NIST Cybersecurity Framework and NIST IR 8596, the preliminary draft Cyber AI Profile issued 16 December 2025. That document is still in motion, so its status is confirmed again at scoping rather than assumed from this page.
Bring your last examination finding
We will show you which of the five gates produces the evidence for it.
The package is the same set your internal audit team would otherwise assemble by hand afterwards.
Is This Built for a Bank, a Broker-Dealer, a Payments Platform or an Insurer?
The architecture pattern holds across all four. What shifts is which risk gets sequenced first, and therefore what the reconciliation has to prove before anyone signs the window.
Banks and credit unions
Core and ancillary data platforms where the examination cycle sets the calendar. FFIEC expectations around data governance and third-party oversight are design inputs, and the evidence set is built to be handed to an examiner without assembly.
Capital markets and asset management
Pricing, risk and reporting platforms where the number has to be defensible at the moment it was produced. Lineage from raw signal through model output to the decision it drove, captured at runtime rather than reconstructed.
Payments and fintech platforms
High-transaction systems where cardholder scope and operational continuity carry equal weight. Reconciliation runs at production transaction volume, and scope boundaries are drawn in design rather than discovered during assessment.
Insurance and insurtech
Policy, claims and actuarial data platforms where the reporting chain feeds financial statements and the controls over it are audited annually.
What this engagement does not cover, stated plainly
Regulatory interpretation or legal advice on your obligations. We design against frameworks; your counsel and your assessor own the position. Certification or attestation of any kind. Model validation as a discipline, though we build the lineage that model validation depends on. Core banking package selection and vendor negotiation. Business process redesign around the modernized platform. Where the right answer is a specialist, we will name one rather than bill you while we learn their job.
Who this is not for
Institutions that want a large junior bench to direct. Programs where a Big 4 signature is the actual procurement requirement rather than the delivery. Engagements where the date is aspirational and everyone in the room knows it. We would rather say so on the first call than the third.
Beyond financial services
The same fixed-date, fixed-fee model runs across other regulated and complex environments.
The method holds wherever the timeline is fixed and the data is sensitive.
Five Gates, and What Your Team Signs at Each One
A gate is not a milestone. A milestone is something a project manager reports on; a gate is something that does not open until somebody in your organization puts their name to what came before it. There are five of them, they are the same five on every Mactores engagement, and in a regulated institution they double as the points where examination evidence gets produced.
-
01
Scope, date and price
Agents crawl the source estate and map how data actually moves through it, which is rarely how the documentation says it moves. Engineers turn that into a target architecture drawn against your regulatory perimeter, then commit. What leaves this gate is a signed statement of work carrying a production date and a fixed fee: a specific number for a specific date, which is what a CFO can act on and a range is not.
Exit
Scope, date and fee
-
02
Controls, before anything moves
Nothing migrates until the control set exists. Access boundaries, encryption posture, retention rules, logging and residency constraints are written down and mapped to the frameworks you answer to. This is deliberately early: your risk and compliance people get their say while changing the answer is still cheap, rather than in a review at the end when it is not.
Exit
The control mapping
-
03
Build on copies, not on production
Schema conversion, migration logic and lineage extraction all run first against non-production copies of your data. Everything the build emits, from diffs to access records to the transformation rules themselves, remains traceable back to a source, so the audit trail exists before the audit does.
Exit
The build, reviewed in your own environment
-
04
Prove it against live data
The reconciliation harness runs the new system beside the old one on real production traffic, comparing at record level rather than in aggregate. Throughput, latency and integrity are demonstrated, not asserted. Where a variance appears, an FDE explains it. No variance is closed by anyone who cannot say why it happened.
Exit
The reconciliation results
-
05
Cut over in stages, hand over the evidence
Traffic moves in waves, each accepted before the next begins, and the legacy platform retires on the schedule you approved. What you keep afterwards is the lineage, the control mapping and the reconciliation output that supports both: the same package your internal audit function would otherwise assemble by hand over a quarter, delivered here as a byproduct of the work.
Exit
Production acceptance
Delays caused by Mactores do not extend your fee. That overage is absorbed on our side under the standard clause in the statement of work. Where the cause sits on your side, that portion moves to the standard rate printed in the same document, so nothing about it is negotiated after the event.
The clause behind the date
The date, the fee and the overage terms are contract language.
Our commitment page sets out exactly what enters the statement of work at each of these five gates.
What Drives the Fixed Fee, and Where Does the Budget Come From?
The fee is fixed on every engagement. We will not name it before scoping, and the reason is not coyness. Quoting a regulated data migration without first knowing how many source systems exist, how much data sits in them, which regulators are in scope and what the target footprint looks like is guessing, and a guess dressed as a fixed fee is how programs end up back on time and materials in month four.
Five things move it, and each is settled somewhere specific
| What moves the fee | What pushes it up | Settled at |
|---|---|---|
| Source estate | More systems, more variety, acquisitions never properly integrated | The estate crawl at gate 01 |
| Volume and retention | Bigger datasets, longer statutory holds, immutability and archival duties | The estate crawl at gate 01 |
| Regulatory perimeter | More frameworks, more than one regulator, data that cannot cross a border | The control baseline at gate 02 |
| Reconciliation depth | Record-level rather than aggregate matching, more domains in parallel, tighter variance thresholds | Target architecture design |
| Your own coordination | Approvals that sit, access that arrives late, scope introduced after signature | Converts to time and materials at the rate already printed in the SOW |
Where the funding usually comes from
Most of this budget is already inside your run rate. BCG’s 2025 banking technology work, drawing on Expand Research benchmarking, puts more than 60% of bank technology spend in the “run-the-bank” category, which covers application maintenance including third-party licence costs, infrastructure and hosting, and IT overhead. That is the pool, and it is worth doing the arithmetic out loud.
Take an institution running a $200 million annual technology budget. On the BCG proportion, roughly $120 million of that is run-the-bank. Inside it sit the specific lines a data modernization retires or shrinks.
Already inside your run rate
- The legacy warehouse licence that renews annually and that no new workload has been built against in three years
- The integration tier whose maintenance contract largely exists to keep the integration tier running
- Extended support on a database engine past its end-of-life date
- Capacity provisioned for a peak that moved to a different system
- The analyst hours spent assembling examination evidence by hand, every cycle
Reallocated to the modernization
- Migration, schema conversion and the reconciliation harness
- Lineage captured at runtime instead of reconstructed each examination cycle
- The legacy line item retired, not run alongside the new one
Retire those and the program is a reallocation of spend you have already committed, not a new request to the board. That is a different conversation from asking for incremental investment, and it is the one that survives a budget committee. The same reallocation pattern runs across data platform modernization and application and database modernization.
The $200 million budget above is a worked example built on a published third-party benchmark. It is not drawn from a Mactores engagement and it is not a quote. Every cost figure, range and saving referred to on this page, the fixed-fee model included, is directional only. Your scope and your fee are established in a scoping call and recorded in a signed statement of work, and nothing here substitutes for that.
Run the arithmetic on your own ledger
Pull the renewal dates, the maintenance contracts and the capacity lines you are already funding.
Thirty minutes with the engineer who would actually scope the work turns the fixed fee from a concept into a number you can defend upward.
What Can You Verify Without Taking Our Word for It?
Up to this point the page has been Mactores making claims about Mactores. This section is the exception. Each credential below is granted only after AWS assesses the work itself, and every one of them is listed publicly, so your procurement function can confirm the whole set in the AWS Partner Solutions Finder before anyone takes a call.
The one that matters most to this page is the AWS Agentic AI Specialization. It is assessed against delivered agent-based work, which is a materially higher bar than the general consulting partner status carried by most firms bidding for a bank data migration. Around it sit 7 Consulting Competencies, 17 Service Validations, and AWS Premier Tier Services Partner standing.
- Specialization
- AWS Agentic AI
- Partner tier
- Premier
- Competencies
- 7, including Migration & Modernization
- Service validations
- 17, spanning data, ML and infrastructure
- Certified engineers
- 200+
- Building on AWS since
- 2008
Which validated services do the work on a regulated build
Data comes off the legacy estate through AWS Database Migration Service, with AWS Glue doing schema mapping and the transformation layer. The target sits on Amazon Redshift and Amazon RDS, or Amazon Aurora where the point of the exercise is ending a commercial engine licence. Real-time market and transaction feeds arrive through Amazon Kinesis and Amazon MSK. AWS Lambda automates around all of it. Two services matter disproportionately in a regulated context: AWS CloudFormation, because it declares the whole environment as one revertible unit, and AWS Control Tower with AWS Config, because together they govern the account structure and then demonstrate that the controls never drifted out of it.
Named in full, the competencies are Migration and Modernization, DevOps, Data and Analytics, Machine Learning, AI Services, Healthcare, and Manufacturing and Industrial Services. The Agentic AI Specialization is named in words because we hold the credential and not the badge artwork. Full detail is on the partners page