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Understanding Financial Data

Coinscrap’s categorization engine is able to detect and analyze:

Financial Categories

17 categories and 120 sub-categories. The engine can detect custom categories.

Financial Scoring

Automated ratios about user’s financial standing and creditworthiness.

Insurance Coverage

Auto-detection of user’s insurance products: Type of insurance, amount, due date, frequency.

Non Financial Assets

Auto-detection of non-financial assets that are relevant for financial planning and financial score.

Partnering With
The University

A research article about the results of the categorization of Coinscrap data was published in the Computer Science Q1 research journal IEEE ACCESS. This journal has an impact factor of 4.098 and an article influence score of 0.835 (per 2018 JCR). Oscar Barba, co-founder and CTO of Coinscrap was one of the co-author of the paper.

Universidade de Vigo

The engine is Highly Scalable

The system combines Natural Language Processing techniques with Machine Learning algorithms to classify banking transaction descriptions for personal finance management, financial scoring and lead discovering.

API Interface

APIs run on cloud therefore the solution is highly scalable.

Parallel analysis  and stateless requests allow the engine to respond from any node and increase the scalability through a load balancer.


Non-relational databases allow the engine to speed up response and provide high volume of data processing.

Replica Set and Sharding enables increased availability and provide a fully scalable solution.

Multi Language

The engine works in three languages: Spanish, English & Portuguese.

The system could be trained to almost every language and the training process takes around to 4 weeks.

Delivery on premise:
Use on demand

On Premise

Scalable architecture is able to be installed within the client’s own infrastructure. Prepared for containers deployment in a orchestrator like kubernetes (Openshift). The engine meets the highest security standards.

On Cloud

The system is ready to be consumed via API on demand or setting a defined batch process through API interface or  files interchanging.

Categorization Engine Ratios

Coinscrap’s categorization engine is able to detect and analyze:

0 M
of processed transactions

The engine is periodically trained to improve the efficiency.

0 %
of accuracy

The accuracy ratio is a comparison between machine and human results.

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of categorization

17 standard categories and more than 120 specific subcategories are provided.

0 x
Enrichment phases

In each stage the system provides more data than the conventional categorization.

Partnering with the university:
Highly Scalable

GTI-Atlanttic is an investigation group from Vigo University specialized in natural language processing and semantic analysis. They have won several prizes because of their research:

Vodafone Foundation

Winners of the XI Edition of Vodafone Connecting for Good innovation in telecommunications.

Fundación Universia

Winners of the II Edition of Ayudas a Proyectos de Investigación de Tecnologías Accesibles Indra + Universia

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