Alternative data: what is it?

Data is a valuable commodity – frequently referred to as the ‘new oil’. Unlike oil, however, data is pervasive, ever evolving, and relates to the personal interest of every individual.

Sources of data are evolving too. Traditional data sources are slowly falling out of favour to be replaced by new and different sources, also known as ‘alternative data’.

What is alternative data?

Alternative data is financial and non-financial data that is secured from non-traditional sources. Raw data is scraped from various different and far-reaching avenues, including drones, phone usage, geolocational information, and social media information, to enable financial organisations to make faster, more accurate decisions.

In the past, for instance, banks may have looked at a person’s bank statements or work history when determining whether they were a suitable candidate for onboarding. However, with alternative data, banks are now able to make onboarding and Know Your Customer (KYC) decisions from alternative sources that can determine a persons’ sentiment. For instance, what do they search for online? And could this impact the products they care about or the investment vehicles that they’d be interested in?

Our report, ‘Data: Poison or Cure?’, in association with Burnmark, found that financial services have harnessed alternative data points across the ambit of their businesses. We identified 14 new alternative data sources, ranging from social media data harnesses for credit scoring to drones used for commercial loans and insurance and behavioural biometrics to determine customer risk.

How does alternative data benefit financial services?

Alternative data goes hand in hand with the digital transformation of financial services. Myriad new and evolving data sources, if managed correctly, can prove hugely advantageous for financial services. Automated systems, driven by artificial intelligence and machine learning, can quickly identify patterns within a series of unlinked data sets to make intelligent inferences about market activity or customer behaviour. Notable examples include:

• Dataminr, a company that runs analytics on data from Twitter, uncovered preliminary reports on the Volkswagen emission scandal three days before the market reacted.
  
  
• Satellites have been used to track the activities of industrial facilities from afar, to generate reports on manufacturing activities.
  
  
• By harnessing alternative data sources and natural language processing, digital public data can be instantly analysed to reveal sentiment and context, so banks can quickly identify sentiment towards investment factors, such as ESG.

What are the drawbacks?

As with all data types – traditional and alternative – if it is properly managed and understood then it can reap innumerable rewards. However, if managed poorly, financial services can very quickly find themselves with too much data in a non-standardised format, which may offer more challenges than it solves.

Data in its purest form, also known as ‘unstructured data’, must be cleaned, analysed, and prepared before it can provide any insight or add value. Internal processes for this collecting, cleaning, and storing process can often be badly built or laborious. However, handing data management over to third parties can prove equally challenging if watertight processes, policies and agreements are not put in place.

Bad data, bad systems, or a combination of the two could mean that banks receive little customer insight, but are on the sharp end of regulatory requirements, from storage to privacy to consent. The question is, are the risks worth the reward?

Find out how CUBE solves for data privacy and records.