Financial institutions and fintech apps often receive raw Open Banking transaction feeds that contain noisy, unstructured data, making it difficult to accurately label spending, assess credit risk, and extract actionable insights in real time.

Noggin applies AI models to clean and enrich transaction data, delivering structured categorisation, bespoke spending enrichments, and a real‑time credit risk score derived from behavioural patterns. The platform ingests data from Open Banking APIs, PDF statements, or partner feeds and returns enriched outputs via low‑latency APIs or a dashboard for manual review. Its categorisation taxonomy is tuned for lending, including sub‑categories such as BNPL and debt collection, and improves labelling accuracy by roughly 23% versus market leaders. The credit scoring model predicts default likelihood and identifies additional good borrowers compared with traditional bureau scores. Enrichments highlight recent borrowing, rent payments, income changes, and spending trends, providing a 30‑day recency view unavailable from credit bureaus.

AI‑driven transaction categorisation with a lender‑focused taxonomy and ~23% higher accuracy than competitors

Real‑time credit scoring that predicts default risk using behavioural patterns in transaction data

Bespoke enrichments that surface new loans, rent, income, and spending trend changes from the last 30 days

High‑throughput, low‑latency APIs designed for price‑comparison and underwriting use cases

Real‑time correction loop allowing models to learn from user adjustments and improve continuously

Dashboard for manual review and trend analysis alongside API access for automated decisioning

Primary customers are fintech lenders, credit unions, credit‑card and mortgage providers, as well as financial‑health apps and telco companies that need reliable transaction‑level insights for underwriting and risk management.