Case Study
An FCA-regulated UK fintech startup integrated 7 disconnected systems (KYC provider, banking-as-a-service partner, accounting, CRM, support, analytics, and proprietary risk engine) into a unified data layer via custom API work, cutting customer onboarding time from 3 days to under 30 minutes and freeing approximately 32 engineering hours per week from manual data reconciliation.
1 min read By Deen Dayal Yadav, Founder & AI Automation Director
UK FinTech startup, FCA-regulated EMI licence, Series A funded, 24 staff
An FCA-regulated UK fintech startup integrated 7 disconnected systems (KYC provider, banking-as-a-service partner, accounting, CRM, support, analytics, and proprietary risk engine) into a unified data layer via custom API work, cutting customer onboarding time from 3 days to under 30 minutes and freeing approximately 32 engineering hours per week from manual data reconciliation.
UK FinTech startup, FCA-regulated EMI licence, Series A funded, 24 staff
An FCA-regulated UK fintech startup integrated 7 disconnected systems (KYC provider, banking-as-a-service partner, accounting, CRM, support, analytics, and proprietary risk engine) into a unified data layer via custom API work, cutting customer onboarding time from 3 days to under 30 minutes and freeing approximately 32 engineering hours per week from manual data reconciliation.
A UK-headquartered fintech startup operating under an FCA-issued Electronic Money Institution (EMI) licence had reached the operational scaling point where its rapidly assembled software stack was becoming the constraint on growth. The business provided a payment-and-expense product to UK small businesses, had closed a Series A funding round eighteen months prior, and had grown to 24 staff including 9 engineers, 6 customer operations, 3 risk-and-compliance, and the senior leadership team. With approximately 11,400 active business customers, monthly transaction volume was approaching ?42M and the company was growing at roughly 18% month-on-month.
The technology stack reflected the startup pattern of choosing best-of-breed point solutions and stitching them together: a third-party KYC provider for identity verification, a banking-as-a-service partner providing the underlying account infrastructure, Xero for the business's own accounting, HubSpot for sales and CRM, Intercom for customer support, Mixpanel for product analytics, and a proprietary risk-and-fraud engine the company had built in-house. Each tool was excellent at its job. The connections between them were the problem.
The most visible symptom was customer onboarding. A new business customer signing up for an account triggered a sequence of system events that the engineering team had originally wired together through a combination of webhooks, Zapier flows, and scheduled batch syncs. The flow worked most of the time, but failure modes were common: KYC results not posting back to the risk engine because a webhook timed out, customers reaching the dashboard before their account was actually provisioned because the banking-as-a-service API call had failed silently, support tickets in Intercom not linking to customer records in HubSpot because of mismatched identifier formats. The customer-operations team spent significant time manually reconciling these failures across three or four systems per case.
Customer onboarding time, measured from signup form submission to dashboard access with a funded account, was averaging 3 days. The technical theoretical floor was around 5 minutes. The gap was almost entirely the failure-and-retry workflow: when something failed silently in the chain, the customer waited until someone in operations noticed and manually unstuck it. The chief operating officer had calculated that roughly 14% of new customer signups required at least one manual intervention before reaching live status, and approximately 4% required three or more.
The reconciliation workload was the second pain point. The engineering team's most senior backend engineer had measured, over a two-week sample period, that he was spending around 32 hours per week of his own time on data-reconciliation tasks across the seven systems. Issues included customers in HubSpot without matching records in the risk engine, transaction records in the BaaS partner without corresponding ledger entries in Xero, and support conversations in Intercom that could not be linked to the relevant customer in the risk engine because of identifier-format mismatches. The work was not technically complex; it was just relentless and consumed the senior engineer's bandwidth.
The brief to Softomate was specific. Build a unified data layer that consolidated identifiers across all seven systems, automated the failure-and-retry workflows that operations was currently doing by hand, and gave the engineering team a single source of truth for customer-state. The build had to respect the FCA's outsourcing and data-residency obligations (all customer data had to remain within UK and EU jurisdictions), and the entire integration had to be auditable so the compliance team could demonstrate to the regulator how data flowed between sub-processors.
Softomate's discovery phase produced a complete data-flow diagram across all seven systems, identifying every point where a customer identifier crossed a system boundary, every webhook in current use, and every batch sync running on a schedule. The diagram revealed 23 distinct integration points across the stack, of which 11 were stable and 12 had known failure modes that the operations team had documented manually over the previous year.
The solution was a custom integration middleware (built in Python with FastAPI and deployed on the company's existing AWS UK region) that sat between the seven systems as a unified API and data layer. The middleware held the authoritative mapping between identifiers across all systems, intercepted every webhook event, applied retry logic with exponential back-off where appropriate, and wrote structured event logs that were queryable for both engineering debugging and compliance audit purposes.
The KYC provider integration was redesigned first because it was the most failure-prone in the existing setup. The previous flow had the KYC webhook posting directly to the risk engine, with a 14% silent failure rate. The new flow had the webhook posting to the middleware, which validated the payload, enriched it with the relevant HubSpot record, wrote it to the risk engine, and confirmed acknowledgement back from each downstream system before marking the event complete. Failed events were retried up to three times with increasing back-off, and final failures were posted to a Slack channel monitored by the operations team for manual resolution.
The banking-as-a-service integration was rebuilt to use the partner's webhook API rather than the previous polling approach. The middleware subscribed to every relevant event type (account creation, deposit confirmation, transaction posted, KYC status change) and translated them into the internal data model. The integration also included a daily reconciliation job that compared the middleware's view of the customer base against the BaaS partner's authoritative ledger, flagging any drift for engineering review.
The customer identifier consolidation was the most operationally important piece. The middleware introduced an internal customer ID that was generated at signup and mapped to the customer's record ID in each downstream system. All inter-system events used the internal ID as the canonical identifier, and the middleware handled translation to the system-specific ID at the point of API call. This eliminated the entire class of identifier-mismatch bugs that had been the source of much of the reconciliation work.
The compliance audit layer wrote every cross-system event into an immutable log stored in a write-once S3 bucket with retention configured to meet the FCA's record-keeping requirements. The compliance team gained, for the first time, the ability to produce a complete data-flow audit for any individual customer or any time window, which had previously required multi-system manual gathering. The audit layer also included structured tags for personal-data flows to support GDPR data subject access request handling.
The build was delivered in 9 weeks from kickoff. The first three weeks were discovery, design, and middleware foundation. Weeks four through seven were the seven integrations themselves, built in priority order: KYC, banking-as-a-service, risk engine, HubSpot, Intercom, Xero, Mixpanel. Weeks eight and nine were parallel-running with the legacy webhook setup, comparison testing, and the cutover itself.
Customer onboarding time fell from a 3-day average to under 30 minutes for the 96% of customers whose KYC outcome was a clear pass. The remaining 4% (where KYC required additional manual review by the risk team) still required human time, but the review queue was now visible in real time rather than discovered through silent failures. The 14% manual-intervention rate in the previous flow fell to under 1% post-deployment.
The senior backend engineer's 32 hours per week of reconciliation work was effectively eliminated. He reported, in the post-launch review, recovering roughly 28 of those hours into product engineering work, with the remaining 4 hours per week being on-call response for the genuinely novel failure modes the middleware exposed for the first time. The CTO described the change as equivalent to having hired a second senior engineer, without the cost.
The customer-operations team's manual reconciliation work fell by approximately 78% in the first quarter post-launch, allowing the team to refocus on actual customer support rather than data fixing. Two of the six operations team members were redeployed into a new customer-success function focused on the company's top 100 highest-revenue customers, work the company had wanted to do for over a year but had never had the capacity for.
The compliance team's quarterly FCA reporting workflow was substantially streamlined. Producing the data-flow evidence for the firm's annual FCA outsourcing review previously took the compliance officer approximately 16 hours of cross-system data gathering; with the new audit layer, the same evidence package could be produced in under 90 minutes. The firm's external compliance consultancy specifically commented on the improvement in audit-trail quality during the most recent regulatory health-check.
The reliability metrics changed materially. The customer-facing dashboard's reliability (measured as the percentage of new customers reaching a functional dashboard within 30 minutes of signup) rose from 86% to 99.4% over the first 90 days. Support tickets related to broken signup flows fell by approximately 71% over the same period.
The unexpected outcome was the speed at which the product team could now build new features that crossed system boundaries. Previously, any feature requiring data from more than one system involved engineering work in the system-specific integration code; now, the same features could be built against the unified middleware data layer. The product team shipped four features in the quarter following the middleware launch that had previously been blocked on integration complexity.
Total Softomate engagement cost was recovered within 6 months of go-live, calculated against the avoided cost of additional engineering and operations hires plus the value of the reduced customer-support load. The fintech has since commissioned Softomate to build a second middleware layer focused on its supplier-payment workflow, where similar cross-system reconciliation pressures are emerging as transaction volume scales.
Related service:API Development & System Integration London. Further reading:FinTech Startup Software Stack UK, FinTech Software Development UK Guide and Cyber Security for Financial Services UK.
Anonymised client engagement. Identifying details modified for confidentiality. Outcome ranges reflect typical results from similar projects.
Names withheld to preserve confidentiality.
Names withheld to preserve confidentiality.
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Softomate Solutions Limited
Deen Dayal Yadav
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