Industry

Fintech

Building the future of finance

I help fintech companies build secure, compliant, and scalable financial systems. From payment processing to investment platforms, I understand the unique technical and regulatory challenges of the financial sector.

At a glance
Regulations
6 frameworks
KPIs tracked
6 core metrics
Reference stacks
5 patterns
Services
4 engagements
Case studies
1 published
Perspective

How I think about fintech

The architecture, the trade-offs, and where I push back on conventional wisdom.

Fintech is the industry where every architectural decision compounds into either resilience or regulatory risk. After a decade designing systems that move money, I've learned that the hardest engineering problems aren't the ones that show up in interviews - they're the slow, boring ones around idempotency, reconciliation, and audit. A payment that succeeds twice is worse than one that fails once. Building systems that get this right is what separates fintech teams that scale from those that get stuck firefighting refunds.

My approach starts with the ledger. Before we talk about UI, mobile, or even APIs, I want to know how you model money. Double-entry accounting on top of an append-only event log is the only model I've seen survive contact with auditors, accountants, and angry customers simultaneously. From there, domain-driven service boundaries emerge naturally: payments, KYC, ledger, treasury, reporting. Each service owns its truth, and a robust event bus gives you the eventual consistency you actually need without the distributed-transactions nightmare.

Compliance is not a feature you bolt on at Series B. PCI-DSS scope reduction starts on day one - most teams should never touch a raw PAN, and tokenization through providers like Stripe, Adyen, or Marqeta should be the default. SOC 2 Type II becomes painful only if you wait. I help teams embed compliance into the platform layer so engineers can ship fast without breaking controls. KMS-backed key management, deterministic encryption for searchable PII, and structured audit logs are foundations, not retrofits.

Real-time risk is where AI starts to earn its keep in fintech. Rule engines still catch the obvious patterns, but transformer-based scoring on transaction sequences picks up the subtler ones - synthetic identity, mule networks, account takeover. I've shipped fraud models that run inline at authorization with sub-50ms p99 latency, using a mix of feature stores, vector retrieval, and traditional ML. The trick is building the human-in-the-loop tooling alongside it: analysts need explainable signals, not opaque scores.

If you're early stage, I act as a fractional technical partner - helping you make foundational decisions you can't easily reverse. If you're scaling, my work spans architecture reviews, modernization, and team coaching. Either way, the bar is the same: ship safely, sleep well, and keep the regulators happy. See how I think about fintech architecture or book a call if you want to dig in.

Challenges

What teams struggle with

The recurring problems I see on fintech engagements.

  • 1Strict regulatory compliance requirements (PCI-DSS, SOC 2, PSD2)
  • 2Real-time transaction processing at scale
  • 3Security and fraud prevention
  • 4Legacy system integration
  • 5Multi-currency and international operations
How I help

Capabilities I bring

Concrete engineering work that resolves the challenges on the left.

  • Payment system architecture and integration
  • Compliance-ready infrastructure
  • Real-time fraud detection systems
  • Banking API integrations (Plaid, Stripe, etc.)
  • Crypto and DeFi platform development
Metrics

What teams measure

The KPIs leadership obsesses over in this sector. Most tie back to performance and architecture decisions made years before the dashboard was built.

01

Authorization rate

Percentage of payment attempts that successfully authorize - every basis point lost is real revenue.

02

Fraud loss rate (bps)

Net fraud losses as basis points of total payment volume; benchmark 5-15 bps depending on vertical.

03

Time-to-clear / settlement latency

How quickly funds move from initiation to final settlement - drives working capital and customer trust.

04

Chargeback ratio

Disputed transactions as a share of volume; >1% triggers card-network monitoring programs.

05

p99 transaction latency

Tail latency of the auth path; SLO targets typically sit at 200-500ms.

06

KYC pass-through rate

Share of applicants that complete onboarding without manual review - directly tied to CAC.

Reference stacks

Stacks I see most often

Patterns I reach for first when scoping a fintechengagement. I don't pick technologies for novelty - read more about how I choose.

1

Postgres + Kafka + Go services for ledger, fronted by Next.js dashboards on Vercel

2

Stripe Issuing/Connect or Marqeta for card programs, Plaid or Finicity for bank data

3

Temporal or AWS Step Functions for long-running money movement workflows

4

Snowflake or BigQuery as the analytical store, dbt for transformations, Looker for finance ops

5

AWS KMS + HashiCorp Vault for key management; Datadog + PagerDuty for SRE

Technologies

Tools of the trade

The platforms and frameworks I lean on for fintech work.

Building for Fintech?

Let's discuss your specific challenges and how technology can help you ship safely, sleep well, and keep regulators happy.

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