Data analytics

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

• Provide actionable insights from analytics leading to predictive sales and service
• Data analytics to extract hidden value from existing data sets

Enrichment

• Data enrichment for financial transactions that enables better data analytics
• Cleans data sets by integrating with other geo-locator databases and completing incomplete fields

External data extraction

• Analyses social media of clients to identify triggers relating to banking
• Searches audio and video for the spoken word to extract data from conversations

Platforms

• User-friendly software platform for building and deploying predictive models
• Self service analytics platform accessing data to all enterprise data for non-data experts

Credit

• Alternative credit scoring system based on non-financial data like social
• Predictive data analytics for lending with algorithmic credit score generation

Specific solutions

• Customer lifetime value calculator using cloud based survey and financial data
• Predictive analytics & customer affinity matching with presence in a branch

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