AVX-CS-002 / Fintech Risk SaaS
LedgerFlow Risk Copilot
A fintech risk intelligence SaaS that models transaction behavior, flags anomalous account movement, and gives compliance teams explainable review workflows.
Client
LedgerFlow
Year
2026
Build
11 weeks
Abstract / Executive Summary
Business impact, condensed.
LedgerFlow required a defensible intelligence layer for financial operations teams reviewing high-volume payment activity. Avrixo built a risk copilot that combined behavioral modeling, rules orchestration, and analyst feedback loops inside a single B2B SaaS experience.
The platform translated complex model outputs into operationally useful evidence packets, allowing reviewers to prioritize cases, explain decisions, and continuously improve detection quality without overburdening engineering teams.
The Core Problem
The bottleneck beneath the business goal.
The client depended on rigid rules that generated too many false positives during seasonal transaction spikes. Analysts spent hours validating low-risk activity, while higher-risk cases could be buried inside noisy review queues.
The business also needed explainability. Any AI-assisted alert had to show contributing signals, historic account context, and policy references so compliance leaders could defend decisions during internal or external audits.
The Technical Solution
Architecture designed for accuracy, scale, and trust.
Avrixo designed a hybrid risk engine that paired deterministic compliance rules with machine learning scores from transaction sequence features. The system normalized payment metadata, account behavior, velocity signals, and counterparty history into a feature store used by the scoring service.
A feedback loop captured analyst outcomes and routed them into retraining datasets, enabling the model to adapt as fraud patterns changed. The frontend exposed risk bands, feature attributions, and policy mappings in an auditable case-review interface.
To support enterprise rollout, the SaaS architecture included workspace isolation, granular permissions, configurable rulesets, and exportable evidence trails for governance teams.
Architecture Notes
System decisions that made the product viable.
Feature pipeline for transaction velocity, merchant patterns, and account drift.
Hybrid scoring that combines model probability with policy rule severity.
Analyst feedback capture for supervised retraining cycles.
Audit-first interface with source records and decision history.
Tech Stack Matrix
Infrastructure behind the outcome.
Application
Modeling
Backend
Governance
Metrics and Outcomes
reduction in false positives
Adaptive scoring helped analysts focus on cases with stronger behavioral evidence.
faster case review throughput
Evidence packets reduced the time spent collecting source data across tools.
continuous risk monitoring
Streaming checks and alert queues kept high-risk movement visible outside office hours.