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Discovery and compliance scoping
We map the regulatory frameworks your product touches before writing architecture, not after. This step is what makes the rest of the timeline predictable instead of a source of late-stage rework.
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Fintech software development services for payments, lending, and wealth management platforms
Financial products carry a different kind of engineering risk than most software. A bug in a retail app is an inconvenience. A bug in a payments flow, a lending decision engine, or a custody ledger is a regulatory incident. AI Engineering by Grid Dynamics builds fintech software with that difference built into the process from day one, not bolted on before an audit.
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Why AI Engineering by Grid Dynamics for fintech software development?
Most software partners treat compliance as a checklist at the end of a build. We treat it as an architecture decision at the start, because in financial software, retrofitting compliance is usually more expensive than the feature it's attached to.
Our Risk & Compliance Platform is built on bitemporal data infrastructure, so every record carries both when something happened and when the system knew about it. That distinction is what makes audit trails, regulatory remediation, and automated reporting actually defensible rather than reconstructed after the fact.
What does better fintech engineering actually change for your business?
Five things change in practice: faster time to market, lower compliance and audit cost, fewer post-launch integration failures, lower fraud exposure, and a platform that grows into new markets without a rebuild.
Faster time to market
A microservices, API-first architecture means adding a new payment rail or product line is an addition to the system, not a rebuild of it. That's the difference between shipping a market entry this quarter and missing the window by a year.
Lower compliance and audit cost
Grid Dynamics' own research on fintech legacy modernization found that retrofitting an existing codebase averages around $1.5 million and 16 months of work, and compliance requirements are one of the most common triggers for that retrofit. Designing for compliance up front is what avoids paying that cost later.
Fewer post-launch integration failures
Most fintech projects that stall after launch trace back to an integration with a bureau, processor, or core banking system that wasn't scoped properly. Scoping integrations during discovery means paying for that work once, not twice.
Lower fraud exposure
Real-time risk scoring built into the transaction flow catches fraud at the point of the transaction, not in a monthly reconciliation report after the funds have already moved.
Room to grow without a rebuild
A platform built on MACH principles adds new markets, currencies, or payment methods as configuration and new services, not a multi-quarter re-architecture project.
Each of these turns into a real cost or a real deadline somewhere on your roadmap. Getting the engineering right up front decides which side of that ledger you end up on.
What fintech products do we build?
We build across thirteen categories spanning core financial infrastructure (payments, banking, lending, wealth management), risk and compliance systems (KYC, AML, transaction monitoring), and the surrounding product layer (integrations, BI and reporting, recommendations, customer support, mobile apps). Each answers to a different regulator or a different user expectation, even when they share a tech stack
Payments and money movement platforms
Payment flows that handle authorization, settlement, currency conversion, and reconciliation across multiple rails and providers.
This includes merchant-facing payment gateways, cross-border transfer systems, and the reconciliation logic that keeps ledgers accurate when money moves across systems that don't naturally agree with each other.
Digital banking and core ledger systems
Account origination, balance and transaction ledgers, and the core banking logic behind checking, savings, or embedded deposit products.
Built to handle real-time balance updates and the two-way data flow that modern banking APIs require, rather than the batch-based logic of legacy cores.
Lending and credit decisioning platforms
Origination workflows, credit decisioning engines, and loan servicing systems.
This is where explainability matters most: a credit decision has to be reconstructable months later, which is a data architecture requirement as much as a modeling one.
Wealth management and robo-advisory platforms
Portfolio construction, rebalancing logic, and AI investment suitability assistants that match recommendations to a client's actual risk profile and regulatory suitability requirements, not just their stated preferences.
Embedded finance and banking-as-a-service
Payment, lending, or account infrastructure embedded into a non-financial product, exposed through APIs your own product team can build on. This is one of the fastest-growing categories in fintech, and one of the least forgiving of weak API design.
Risk, compliance, and regulatory reporting systems
Structured products platforms, agentic AI workflows for trade and portfolio risk, and the automated reporting infrastructure that turns a regulatory filing from a quarterly scramble into a system that already knows the answer.
KYC and AML verification systems
Identity verification, sanctions and watchlist screening, and ongoing transaction monitoring for anti-money laundering compliance.
Built to reduce false positives in the screening queue without weakening the checks regulators actually test for, since an alert backlog nobody can work through is its own kind of compliance failure.
Transaction monitoring and processing systems
Real-time transaction processing, monitoring, and reconciliation across the systems a transaction touches on its way to settlement.
This is where the real-time risk scoring and fraud detection described above plug directly into the transaction flow, rather than reviewing what already happened after the fact.
Integration and middleware layers
The connective layer between your platform and the processors, bureaus, and core systems it depends on.
Covered in more depth in the integrations section below, since getting this layer right is usually what separates a fintech product that works from one that also survives contact with the rest of your technology estate.
BI, analytics, and reporting platforms
Dashboards and reporting infrastructure that turn transaction, customer, and risk data into something operations, finance, and compliance teams can actually use, including the automated regulatory reporting a compliance-grade data architecture makes possible.
Recommendation engines
Personalized product, portfolio, or credit offer recommendations built on a customer's actual data and behavior, not a generic segment.
In financial services, this has to account for suitability and regulatory limits on what can be recommended to whom, which is a compliance problem as much as a modeling one.
Customer support and AI-powered customer experience platforms
AI assistants and support tooling that handle account questions, transaction disputes, and routine service requests, with a clear handoff to a human agent when a request needs judgment a model shouldn't be making alone in a regulated product.
Fintech mobile apps
Native and cross-platform mobile apps for banking, lending, investing, and payments, built on the same API layer as the web and backend systems rather than as a separate product that drifts out of sync with them.
Compliance by product and jurisdiction
Every fintech category above answers to a different set of regulatory frameworks, often more than one at once.
We design systems to meet these frameworks; certification scope and legal ownership of compliance sign-off sit with your legal and compliance teams, working alongside our engineering delivery.
| Product area | Frameworks and standards commonly in scope |
|---|---|
| Payments and money movement | PCI DSS, PSD2 (EU), NACHA (US), ISO 20022 messaging |
| Digital banking and core ledgers | GLBA, Basel III / CRD capital and reporting rules, FFIEC guidance (US) |
| Lending and credit decisioning | Truth in Lending Act / Regulation Z, ECOA and fair lending rules (US), Consumer Credit Directive (EU) |
| Wealth management and robo-advisory | MiFID II (EU), SEC and FINRA suitability rules (US) |
| Cross-cutting, all products | GDPR / CCPA data privacy, KYC/AML (BSA, 5AMLD), SOC 2 and ISO 27001 as architecture design targets |
The integrations a fintech product really needs
A fintech platform is rarely a standalone system. It's a coordination layer between providers, regulators, and internal systems that all speak different formats.
Payment rails and processors
card networks, ACH, real-time payment rails, cross-border transfer networks
Market and pricing data feeds
for wealth management, trading, and structured products platforms
Core banking ledgers and legacy systems
the systems of record most fintech platforms have to work with, not replace, at least initially
Credit bureaus and alternative data sources
for lending and underwriting decisions
KYC/AML and identity verification providers
identity document checks, sanctions screening, ongoing monitoring feeds
Accounting, ERP, and general ledger systems
so financial data reconciles automatically instead of through a monthly export
CRM and customer communication systems
so compliance-required disclosures and customer data stay in sync
Getting this integration layer wrong is the most common reason fintech projects stall after launch: the product works, but it doesn't talk to the five other systems the business actually runs on.
Advanced technologies we use
so individual services can change, scale, or get replaced without taking down the platform around them
The technology partners behind our fintech platforms
We build on partnerships across three layers most financial platforms already run on: cloud infrastructure, data and AI platforms, and workflow orchestration.
Cloud infrastructure
Data and AI platforms
Workflow orchestration
Most of these are probably already running somewhere inside your organization, which is why we build directly on them instead of asking you to adopt something new just to work with us.
Our process, and how long it actually takes
02
Architecture and design
The data model, integration points, and compliance controls get designed together, since in fintech, the compliance requirement usually is the architecture requirement.
03
AI-accelerated build
Development runs with AI embedded across planning, coding, and testing, which compresses the distance between a designed feature and a tested one without skipping the testing.
04
Compliance validation and launch
Security review, regulatory sign-off support, and a phased rollout rather than a single cutover, particularly for anything touching money movement or core ledgers.
05
Ongoing delivery
Most fintech platforms don't stop needing engineering at launch; new payment rails, new markets, and new regulatory requirements keep arriving.
Timelines vary by regulatory scope more than by pure technical complexity. As a general guide: a focused MVP, such as a single lending product or an embedded payments feature, typically runs 3 to 4 months. A fuller platform, such as a core banking module or a multi-jurisdiction payments system, typically runs 6 to 12 months depending on how many frameworks from the table above are in scope. These are general ranges, not a quote; your actual timeline depends on your specific compliance footprint.
Use cases
Five scenarios come up repeatedly in fintech engineering work: modernizing a payments stack under a compliance deadline, building credit decisioning into a new lending product, extending a core banking system without a full replacement, embedding finance into a non-financial product, and adding real-time fraud detection to an existing platform.
Modernizing a payments platform ahead of a compliance deadline
SITUATION:
A payments provider is running on an aging stack that a new PSD2 or PCI DSS requirement is about to make non-compliant.
WHAT THIS TYPICALLY INVOLVES:
scoping the specific requirement against the existing architecture, then modernizing the affected services (often authorization, settlement, or reconciliation) without a full platform rebuild. Built to hit the compliance deadline without freezing feature work elsewhere in the platform.
Building credit decisioning into a new lending product
SITUATION:
A lender is launching a new credit product and needs an origination and decisioning engine that's explainable enough to survive a regulatory review.
WHAT THIS TYPICALLY INVOLVES:
building the decisioning logic and the underlying data architecture together, so every decision is reconstructable months later, not just accurate at the moment it's made. Built so the lending team can adjust risk criteria without an engineering release cycle for every change.
Extending a core banking system without a rip-and-replace
SITUATION:
A bank or neobank needs new functionality (a new account type, a new API-driven feature) but the core ledger is a legacy system too risky to replace outright.
WHAT THIS TYPICALLY INVOLVES:
wrapping the legacy core in a modern API layer and building new functionality as services around it, rather than a multi-year core replacement project. Built to ship the new capability in months, not the years a full core migration would take.
Embedding finance into a non-financial product
SITUATION:
A software company wants to add payments, lending, or account features directly into its own product, without becoming a licensed financial institution itself.
WHAT THIS TYPICALLY INVOLVES:
integrating with a banking-as-a-service or payments provider and building the product-facing experience and compliance controls on top of it. Built to add a new revenue line without the company taking on a banking license's full regulatory weight.
Adding real-time fraud detection to an existing platform
SITUATION:
A platform is catching fraud in batch reconciliation, after the money has already moved, and wants to catch it at the point of transaction instead.
WHAT THIS TYPICALLY INVOLVES:
adding real-time risk scoring into the transaction flow itself, using the platform's existing transaction data rather than a bolted-on third-party black box. Built to stop fraudulent transactions before settlement, not just flag them afterward.
Custom software development vs staff augmentation: which path fits you
Choose custom development if you're building a new product and want us to own delivery against milestones. Choose staff augmentation if you already have a roadmap and need dedicated engineers to execute it under your direction. Both run through the same engineering discipline; what changes is who owns the roadmap.
| Custom software development | Staff augmentation | |
|---|---|---|
| Best for | ||
| Best for | Building a new product or platform from the ground up | Extending an existing team and roadmap you already own |
| Who owns the roadmap | ||
| Who owns the roadmap | We plan and deliver against agreed milestones | Your team directs the work day to day |
| Typical fit | ||
| Typical fit | New fintech product, major platform rebuild, compliance-driven overhaul | Scaling delivery capacity, filling a specific skill gap, covering a hiring gap |
| Engagement shape | ||
| Engagement shape | Fixed scope or milestone-based | Dedicated engineers or a full POD embedded in your team |
Custom development isn't the right call in every case: skip it if you haven't validated the product yet, if mature off-the-shelf software already covers the requirement, if you just need more hands on an existing roadmap rather than a new product, or if your timeline doesn't actually allow for the compliance scoping and testing a regulated build needs.
How to choose a software development partner
Ask how they handle compliance in the architecture, not just the audit.
A partner who only thinks about compliance at the review stage will cost you rework.
Ask for specifics on financial services experience
“We’ve built fintech before” is not the same as having built lending decisioning engines or payment reconciliation systems specifically.
Check how they handle integration, not just the product itself
A fintech platform is only as good as its weakest integration to a bureau, a processor, or a core banking system.
Understand the engagement model before you sign anything
Know whether you’re buying a fixed outcome or a team, since the two require different oversight from your side.
Ask what happens after launch
Fintech platforms need continued engineering for new regulations and new payment rails. A partner without a post-launch model is planning to hand you a maintenance problem.
Watch for the reverse of these signals too: a partner who can’t name specific regulations without prompting, treats AI as a feature to bolt on rather than a way to accelerate delivery, has no clear answer on data residency or audit trail design, or is vague about what happens to your codebase if the engagement ends.
FAQs
A focused MVP, such as a single lending product or an embedded payments feature, typically takes 3 to 4 months. A fuller platform, such as a core banking module or a multi-jurisdiction payments system, typically takes 6 to 12 months. The main driver is regulatory scope, not raw technical complexity
Ready to talk about your fintech platform?
Book a strategic call with our team. We'll talk through your product, your regulatory footprint, and what a realistic build actually looks like, not a generic sales pitch.