New

TrustFlow 2.0 is live — ML pipeline management with zero-downtime model swap. Learn more →

Deployment Scenarios

Composite scenarios. Real workflow patterns.
Trust, delivered.

These scenarios are composite reference stories built from recurring implementation patterns across lending, e-money, wealth management, treasury, and embedded finance. They are labelled as scenarios until public customer references are approved.

6Composite deployment scenarios
5Core platform modules represented
1Shared operating model
6Recurring rollout patterns surfaced
PL

Paysona Ltd

E-Money Institution 50 employees · £1.2bn monthly volume

The challenge

Three separate tools — a categorisation API, a reconciliation SaaS, and a manual AML process — generating three fragmented audit trails. When the FCA opened a supervisory visit, Paysona's compliance team spent 11 days assembling documentation from three sources. The FCA found gaps.

The solution

Trustinera AI replaced all three tools with a unified platform. TrustFlow handles live ingest from Barclays and Starling feeds. Categorise processes 4.5M transactions per month at 97% accuracy. Sentrise handles AML screening and case management. OpsIQ monitors pipeline health 24/7.

Timeline

6 weeks from contract to full production deployment, including data migration and team training.

"Our FCA supervisory visit went smoothly because we could export a complete audit pack in under four hours — transaction lineage, categorisation decisions, reconciliation completion, and model version. BlackLine couldn't do that in four days."

Leila A., CFO — Paysona Ltd

Results

< 4 hrs Next FCA audit pack (down from 11 days)
97% Categorisation accuracy (was 71% manual)
£2.1M Annual tooling savings (3 tools replaced)
3→1 Platforms replaced by Trustinera AI
Get similar results →

MW

Meridian Wealth

Discretionary Fund Manager 120 employees · £4.2bn AUM

The challenge

Meridian's compliance team of four was spending 18 hours per week on manual AML monitoring — pulling transaction reports, running spreadsheet-based watchlist checks, and writing SAR narratives from scratch. With AUM growing 40% year-on-year, the team knew the manual approach wouldn't survive the next year.

The solution

Sentrise replaced the manual AML workflow with real-time transaction monitoring, automated watchlist screening (OFAC, HM Treasury, PEP), and a case management system with auto-drafted SAR narratives. TrustFlow connected to Meridian's three custodian data feeds via API.

Timeline

4 weeks from signed contract to live AML monitoring in production.

"Sentrise gave our compliance team real confidence in our AML controls for the first time. The watchlist screening runs in under 100ms and the SAR generation alone has saved us three days of manual work per quarter."

Priya M., Head of Compliance — Meridian Wealth

Results

18→2 hrs Weekly AML monitoring time per analyst
70% Reduction in false positive alerts
3 days Saved per SAR per quarter
100% Watchlist coverage (up from estimated 82%)
Get similar results →

CF

Clearway Finance

Consumer Lending Platform 35 employees · £180M originations/year

The challenge

Clearway's affordability assessments relied on manual bank statement review — categorising income and expenditure by hand for each applicant. With 800 applications per week, the process took three underwriters three days each week. Categorisation inconsistency was flagged in an internal audit.

The solution

Trustinera AI's Categorise module processes uploaded bank statements and returns structured income/expenditure categories with confidence scores. High-confidence items flow straight to the affordability model. Low-confidence items queue for an underwriter review — reducing manual review from 100% to under 8% of applications.

Timeline

3 weeks integration via Python SDK, including underwriter training and threshold calibration.

"The categorisation accuracy is genuinely impressive — 97% out of the box on our transaction set. The false positive rate dropped from 18% to under 4% in the first month. Our underwriting team went from being overwhelmed to having time for complex cases."

James O., CTO — Clearway Finance

Results

97% Categorisation accuracy on lending transaction sets
92% Applications processed without manual review
3→0.5 day Underwriter time per 100 applications
18%→4% False positive rate (income misclassification)
Get similar results →

ND

Nortex Digital

Accounting SaaS (Embedded Finance) 28 employees · 12,000 SMB customers

The challenge

Nortex wanted to add AI transaction categorisation to their bookkeeping product but didn't have the ML engineering capacity to build it. Two previous vendors had poor accuracy on UK merchant data and required months of professional services to integrate.

The solution

Trustinera AI's Categorise API was integrated via the TypeScript SDK in 11 days by two engineers. The API's OpenAPI 3.1 spec and sandbox environment made integration testing straightforward. Nortex users now see AI-categorised transactions in their bookkeeping timeline. User corrections are fed back via the corrections API, improving accuracy for the whole Nortex customer base.

Timeline

11 days from API key to production feature launch.

"We embedded Trustinera AI's categorisation API in under two weeks using the TypeScript SDK. The OpenAPI spec was clean, the sandbox was production-accurate, and the error messages actually told us what went wrong."

Tom R., Engineering Lead — Nortex Digital

Results

11 days Integration to production feature launch
96% Categorisation accuracy on Nortex's SMB dataset
4.6/5 User satisfaction score for new feature
31% Reduction in customer-reported categorisation errors (vs manual)
Get similar results →

AC

Ashfield Corporate

Corporate Treasury (Mid-Market) 380 employees · £620M annual turnover

The challenge

Ashfield's finance team of eight spent five working days on every period close — manually downloading bank statements from four banks, reconciling against Sage 200 entries, and emailing exception lists back and forth. The process was error-prone and delayed board-level financial reporting by a week every month.

The solution

TrustFlow connected to Ashfield's four bank feeds (NatWest, HSBC, Barclays, Starling). Categorise processes and enriches all transactions. Reconcile matches against Sage 200 automatically, surfacing only genuine exceptions. Period-close reports are generated on demand and delivered directly to the Finance Director dashboard.

Timeline

8 weeks including bank feed onboarding, Sage 200 integration configuration, and team changeover.

"The Xero and Sage connectors mean our accountant sees categorised, reconciled data in their familiar tools without any manual export. Period close went from five days to one."

Marcus B., Finance Director — Ashfield Corporate

Results

5→1 day Period close duration
40 hrs Saved per month by finance team
99.9% Auto-match rate before human review
1 week Earlier board financial reporting
Get similar results →

CP

Corda Payments

Payment Institution 65 employees · 2.8M transactions/month

The challenge

Corda's finance ops team was processing 2.8M transactions monthly across seven payment rail types, with a manual categorisation process that took three people 40 hours a week. Their false positive rate in fraud detection was 18% — generating more noise than signal for the risk team.

The solution

Trustinera AI processes all 2.8M monthly transactions via TrustFlow's webhook ingestion. Categorise handles all seven payment types with a custom taxonomy configured for Corda's chart of accounts. Sentrise reduced false positives via configurable risk scoring thresholds tuned against Corda's historic transaction patterns.

Timeline

5 weeks from first API call to full production volume.

"We replaced our entire month-end spreadsheet process with Trustinera AI. Our ops team saves 40 hours a week and our false positive rate dropped from 18% to under 4%."

Sarah K., Head of Financial Operations — Corda Payments

Results

40→8 hrs Weekly categorisation team time
18%→3.8% Fraud false positive rate
£640k Annual team cost saving
2.8M Monthly transactions — zero downtime since launch
Get similar results →

Your story, next.

Book a 30-minute demo. We'll show you the results you can expect — with your data.