The Good Tech Companies - As Fintech Scales, Regulators Are Asking a Hard Question: Can the Systems Prove It?

Episode Date: January 19, 2026

This story was originally published on HackerNoon at: https://hackernoon.com/as-fintech-scales-regulators-are-asking-a-hard-question-can-the-systems-prove-it. As fintech scales, regulators want proof—not promises. Inside the engineering shift making ledgers auditable and defensible. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #fintech-ledger-auditability, #double-entry-fintech-ledger, #financial-event-replay, #payment-reconciliation-systems, #audit-ready-fintech-infra, #regulator-proof-accounting, #explainable-risk-systems, #good-company, and more. This story was written by: @sanya_kapoor. Learn more about this writer by checking @sanya_kapoor's about page, and for more stories, please visit hackernoon.com. Rapid fintech growth is exposing a new risk: systems that can’t prove financial accuracy under audit. This article examines how ledger design, transaction-state modeling, and replayable financial records are becoming essential as regulators demand reconstruction—not assertions—of what happened across complex payment and lending platforms.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. As fintech scales, regulators are asking a hard question. Can the systems prove it? By Sonia Kapoor. As regulators tighten expectations, engineering design is becoming central to auditability and transaction reconstruction. As fintech platforms expand into lending, payments, and embedded finance, regulators and auditors are applying a sharper lens to a foundational question. Can a company prove what happened in its financial system not just asserted, recent enforcement actions and audit findings across the industry have highlighted a recurring weakness. Many fast-growing fintech stacks were built for speed and customer experience first, with accounting integrity and traceability

added later. In practice, that can make it difficult to reconstruct financial events months or years after the fact, especially when transactions span multiple partners, payment rails, and asynchronous settlement systems. Financial correctness is not something you can bolt said a poor of Berthair, a technical architect with more than 14 years of experience building distribute Dan financial systems. Berthair is the founding engineer and head of engineering at a U.S.-based fintech developing credit products for underserved borrowers, including international students and consumers with limited credit history.

In today's environment, the engineering choices behind ledger design, transaction state management, and reconciliation workflows increasingly determine whether a fintech platform can withstand audit scrutiny. Ledger design moves from back office to frontline risk. Modern fintech transactions are rarely simple. A single customer action such a spaying a bill or making a purchase can generate multiple financial events. Authorizations, partial captures, refunds, reversals, chargebacks, delayed postings, and settlement adjustments. Each event can arrive out of sequence, be duplicated, or be amended by external processors. At scale, those realities can turn reconciliation, into a continuous operational risk.

Industry audits frequently cite problems such as fragmented ledgers, inconsistent state transitions, and reliance on manual corrections, particularly in systems stitched together from loosely connected microservices or third-party abstractions that were not designed for full event reconstruction. To address those challenges, Verthera led the development of an internal financial infrastructure layer designed around double-entry accounting and explicit transaction state modeling. The system records each financial event. ASA structured ledger entry intended to be replayable and independently verifiable, enabling

teams to trace funds movement across complex product flows. Financial systems should behave like accounting systems first, Berthair said. If you can't reconstruct where each dollar originated and where it moved, the platform can't reliably defend its records under audit. Engineering for payment networks that don't behave ideally. Payment systems introduce edge cases that simplified fintech ledgers often fail to model, incremental authorization. split captures, asynchronous reversals, delayed chargebacks, and settlement corrections that arrive long after a customer believes a transaction is complete. When software assumes ideal sequencing, operational teams may be forced to make manual adjustments creating downstream risk

in reporting, compliance, and customer dispute handling. Berthair's architecture was designed to preserve ledger consistency under those conditions. It uses ID impotency controls and transactional safeguards to prevent duplicate events from corrupting balances and to reduce reconciliation drift when upstream signals arrive later in unexpected order. The approach draws on patterns from high-scale distributed systems, where fault tolerance and recovery behavior must be engineered into the system from the beginning. From distributed infrastructure to financial accuracy, before his current fintech role, Berthair worked on high-through-put infrastructure supporting cloud services and speech recognition platforms, where correctness and latency can affect large user-populmonary.

He has also listed ASEAN inventor on multiple awarded U.S. patents spanning data scalability, speech recognition optimization, and data processing methods. He later worked on blockchain security and fraud analytics at a major U.S. digital asset platform, where detecting high-risk activity depends on dateline age and systems that can operate under adversarial conditions. Security work changes how you think about correctness, Berthair said. You stop assuming clean inputs, and you design systems that can recover from ambiguity without corrupting financial state. Risk systems built to explain decisions. Regulators

are also paying closer attention to how fintech lenders make credit decisions, including whether outcomes can be explained and audited. In many organizations, machine learning models and rule engines have been deployed faster than the governance frameworks required to document decision logic. Berthera led the design of a risk framework combining rule-based decisioning with machine learning enrichment, built to preserve decision context andrational. Each decision stores the inputs and logic used at the time, enabling retrospective review and auditability across multiple product types as policy requirements evolve.

Risk systems must be explainable by design, Berthair said. Otherwise, TeamSoray forced to reverse engineer decisions later and that rarely stands up under scrutiny. A shift in FinTech engineering priorities. As fintech matures, the industry's priorities are shifting. speed and growth remain important, but durability, auditability, and operational transparency are becoming core requirements, especially for platforms handling regulated financial activity. Industry experts increasingly view financial infrastructure not as an application layer but

as a long-term record system whose outputs may need to bed-offended years later. Technology moves quickly, Berthair said, but financial records have a long life. Engineering teams have to build for that timeline. This story was distributed as a release by Sonja Kapoor under Hackernoon Business Blogging Program. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.