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Data analytics

Matchi Trend Report - Digital Account Origination

Fintechs can utilise cloud based processing power to harness the enormous amounts of information generated by banks and insurers and develop data analytics applications that involve extraction, combination, and analysis of data using sophisticated tools and techniques. This data can be collected, stored and analysed, all in real time, with cloud based software and at a fraction of the cost.

The field of data analytics has evolved from analysing raw data to draw conclusions, to a wider definition that encompasses extraction, combination, and analysis of data using sophisticated tools and techniques. Recent advances in technology meant that larger amounts of data can be collected, stored and analysed, all in real time, with cloud based software and at a fraction of the cost.

The field of data analytics has evolved from analysing raw data to draw conclusions, to a wider definition that encompasses extraction, combination, and analysis of data using sophisticated tools and techniques. Recent advances in technology meant that larger amounts of data can be collected, stored and analysed, all in real time, with cloud based software and at a fraction of the cost.

The ease of access to low cost technology has led to the development of multiple applications ranging from big data analytics to bespoke algorithms for very specific purposes.

Although data analytics is not exclusively fintech, per se, it has found application to industries that generate large amounts of data, of which financial services is a natural client.

The main areas of the broader field of fintech based data analytics are:

  1. Generic solutions – these are traditional data analytics companies that are able to process data and discover patterns and generate insights, usually along with predictive power
  2. Enrichment – these companies “clean” databases and enhance the data, usually by cross referencing with external data sets
  3. External data extraction – this refers to the extraction of data from unstructured data sets, e.g. voice conversations
  4. Platforms – these are usually platforms that allow users without specific statistical skills to be able to analyse data sets, i.e. “DIY analytics”
  5. Credit – these are risk based algorithms derived from data analytics for credit scoring purposes
  6. Specific solutions – these are niche applications of data analytics, e.g. customer value calculations

Some solutions

Generic solutions

Enrichment

External data extraction

Platforms

Credit

Specific solutions






More on Matchi: Matchi is a global fintech innovation match-making firm, since 2013. Matchi has worked with over 100 leading banks and insurance companies (FIs) around the world, and has a database of over 2,500 fintech firms. Matchi provides both a highly curated portal of fintech solutions, as well as bespoke projects for FI clients to source targeted fintech solutions aimed at the FIs focus areas / pain points. The global Matchi team has run Innovation Challenges and Market Scans for multiple FIs around the world, in markets as diverse as Canada, Japan and India, as well as searches in other geographies.

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