Engineering for financial services
where every action lands in a log.
Core systems modernization, risk and compliance tooling, accounts-payable and reconciliation automation, and AI applications for the front and back office. Built to satisfy the examiner who'll arrive next year.
What financial-services software looks like in practice.
Financial-services operators carry decades of accumulated systems: a core that may have been picked in the 1990s, a layer of front-office applications added later, and a tangle of point tools for everything the core doesn't cover. Most of the data the business actually runs on is fragmented across these systems, with the reconciliation work happening in spreadsheets and the regulatory reporting stitched together at the end of each period.
The pressure isn't to replace the core. Core conversions are multi-year programs, and most operators don't have the appetite for that risk. The pressure is on the surrounding work: the lending workflows that take too long, the AML alerts that pile up faster than analysts can clear them, the AP queue that still routes by email, the customer-facing experiences that haven't kept pace with what fintechs ship. AI is starting to land in real ways here, but the audit trail has to come with it from day one.
Apollo builds the modernization, data, and AI work that sits around the core. Examiner-aware by default, audit-recorded everywhere it matters, and integrated with the core, ledger, and downstream reporting systems you already run.
SOX, GLBA, BSA/AML, FFIEC handbooks, and PCI DSS are the floor for most operators. Lenders add fair-lending and TILA/RESPA. State regulators add their own. The audit trail and access-controls posture have to satisfy all of it simultaneously.
The integration between the lending origination system and the core that was written when the bank was half the size, that still runs but that nobody on the current team is comfortable touching. The reconciliation spreadsheet maintained by one person. The AML rules engine whose logic lives in a vendor product nobody is allowed to modify.
AML alert triage with explicit reasoning trails. Customer-facing AI assistants where every response is logged and reviewable. Document extraction for loan packages, KYC files, and AP invoices. Risk-narrative drafting where a human approves before anything goes out the door.
Four engagement patterns in financial services.
Most financial-services engagements land in one of these shapes. Each gets built audit-recorded by default, integrated with the core and ledger systems you already run, and ready for the examiner who will eventually ask about it.
Core Systems Modernization
The applications and data layers that sit alongside the core, replacing the workflow tools and integrations that have aged out. Modernized in place, with the audit fields and reconciliation hooks the existing controls already depend on. The core itself stays. The brittle surrounds get rebuilt.
Risk, Compliance & AML Tooling
AML alert triage, KYC document workflows, fair-lending monitoring, and the regulatory-reporting pipelines that feed Call Reports, HMDA, BSA, and state filings. AI for the unstructured parts (narrative review, document classification) with the model versioning and reasoning trails examiners will request.
AP/AR & Reconciliation Automation
Invoice intake and three-way match, accounts-receivable application, and the reconciliation work that consumes back-office capacity at every operator above a certain size. Confidence scoring routes the easy cases through and pushes the rest into reviewer queues built for the work.
Customer-Facing AI with Audit Trail
The conversational and decision-support AI that lives in customer-facing channels and lender-facing tools. Every response logged, every decision attributable to a model version, every escalation path designed in. Built to satisfy fair-lending and consumer-protection review the same day it gets reviewed.
Where our work sits.
Financial-services operators run a small set of large systems with years of integrations between them. Apollo builds the modernization, data, and AI work that sits around this landscape, not on top of it. The map below is simplified, but representative of what we walk into.
Compliance, designed in.
The frameworks that shape what we build, and what we ship.
Financial services lives under more overlapping regulatory regimes than almost any other sector. The right ones for any engagement depend on the operator's charter, the products in scope, the data it touches, and the geographies it serves.
Apollo treats these as design inputs, not as a checklist applied at the end. Access controls, audit trails, encryption boundaries, model documentation, and reconciliation hooks get specified before the first line of code and reviewed against the relevant frameworks at every iteration.
The panel on the right is the working set we encounter most often. Any given project picks a subset, and the proposal will be explicit about which ones apply and what we will and won't certify directly.
Federal & statutory
Examiner guidance & supervisory
Industry frameworks & certifications
Four phases. Built around the examiner.
Apollo's standard methodology, applied to financial services. Audit posture, access controls, and reconciliation design get specified before the build starts, so examiner-readiness is a property of the system rather than a phase at the end.
Map the work. Map the regulations.
The current process, the systems involved, the volumes, and the reconciliation breakpoints your team already knows about. The compliance frameworks in scope and the controls posture they require. Where AI helps, where rules suffice, and where a reviewer still has to look.
Architecture, audit plan, controls plan.
System architecture and integration design against the core and front-office landscape. Access-control and segregation-of-duties posture. Audit trail design that satisfies SOX, SOC 2, and examiner expectations. Reviewer UI wireframes where humans stay in the loop.
Platform. Integrations. Reviewer UIs.
Platform deployed inside the security boundary, core and ledger integrations connected, AI extraction wired up with model versioning, reviewer UIs functional. Two-week iterations. Each shipped capability arrives with its tests, its monitoring, and its audit fields.
Measure. Tune. Expand.
Throughput, error rates, AI accuracy, and false-positive rates in production dashboards. Threshold tuning based on what the data shows. Gradual rollout to adjacent product lines once the first workflow is steady. Knowledge transfer to your team along the way.
Tell us what you're trying to build.
Send a paragraph about the project: the systems involved, the volumes if you have them, the regulatory frameworks in scope, and the parts that aren't working today. We'll reply within one business day, either with a 30-minute call or with an honest "this is not the right fit; here's who you should call instead."