About Noggin

Noggin provides AI-powered enrichment of Open Banking transaction data, delivering highly accurate categorisation, bespoke spending insights, and real-time credit risk scores via low‑latency APIs and a dashboard. Its lender‑focused taxonomy improves labeling accuracy by about 23% and its credit scoring model predicts default risk using behavioural patterns, helping fintech lenders and financial institutions make faster, more reliable underwriting decisions.

<problem>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.</problem> <solution>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.</solution> <features> - 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 </features> <target_audience>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.</target_audience>

What does Noggin do?

Where is Noggin located?

Noggin is based in Newcastle upon Tyne, United Kingdom.

When was Noggin founded?

Noggin was founded in 2021.

How much funding has Noggin raised?

Noggin has raised $940.0K.

Location

Newcastle upon Tyne, United Kingdom

Founded

2021

Funding

$940.0K

Employees

7 employees

Investors

Oxcp

Noggin

Newcastle upon Tyne, United KingdomFounded 202172K+ followers8/10 TractionRelative Traction Score based on online presence metrics compared to companies in the same age group.

Updated 2 months ago

Funding

$940.0K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

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.

Solution

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.

Target Audience

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.

Features

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