Data Analytics

Understanding AI Scoring

The term ‘scoring’ when applied to AI (Artificial Intelligence) is more commonly know as ‘data scoring’ and is used to quickly and effectively provide meaningful insights based on algorithms derived from large amounts of data.

AI data scoring is a way of applying an existing ‘reference’ model to new data: in basic form, comparing a list of criteria required to the new data to determine the score.

The role of AI, Big Data and Machine Learning

To make data scoring effective huge amounts of data are required to allow the Machine Learning element to effectively ‘learn’ what the data is telling it to develop algorithms to enable scoring to take place.

From here AI works to score the data whether making a decision (a ‘yes’ or ‘no’ to a financial loan applicant for example) or putting a numerical score value – for example, someone’s credit rating may be expressed as ‘out of ten’ or ‘out of 100’ or similar.

Harnessing the advantages of data scoring is certainly possible for businesses of many sizes – not just larger concerns – but requires sound data management and access not only to powerful AI and Machine Learning tools but also data analytics experts, such as Essex based professionals, Objective.

Examples of data scoring

Credit scoring – the term ‘credit scoring’ is familiar to many and uses historical data to apply a score value to determine someone’s credit worthiness and may be used by a company deciding whether to offer loan facilities, a credit card or other financial products.

Historical data such as previous credit given (or declined as the case may be), present financial commitments, whether they are on the electoral register and more are ‘totted up’ and a score given. The potential lender will decide whether or not to offer a product (or the decision itself may be automated).

Sales lead scoring – in sales ‘lead scoring’ will be undertaken by AI and Machine Learning to help qualify a sales to lead so it can be categorised. A higher score may categorise the lead as ‘warm’ or ‘hot’ or consider it as ‘lukewarm’ or ‘cold’.

Based on the scoring criteria, the lead may be escalated to immediate action if ‘hot’ or moved to the ‘bottom of the pile’ if scored as ‘cold’ (perhaps put into an email sequence for automated follow up to try and ‘warm’ the lead up or similar).

Medical diagnosis – examples would be when a patient is being diagnosed with a mental condition such as ADHD; a series of tests and perhaps questions and answers would be recorded and set against known criteria and, if enough are present and scored by relevance or importance by the tech, this could contribute to the diagnosis.

Voting tools – some websites may provide interactive facilities to help people judge what political party to vote for. The user would select or score certain aspects that would influence their voting decision, and the tech would apply their data input and scores with the established data applied to each party to show – on a pie chart for instance – which party mostly offers what the user wishes for.

Benefits of data scoring

A key advantage of data scoring is how information can be presented in a very user friendly way to enable fast decisions to be made.

For instance, when deciding if an applicant should be offered a financial product the findings can be presented in a basic ‘out of ten,’ or maybe a temperature gauge style graphic showing ‘low to high risk’. 

By setting new data (from a financial product applicant in this case) against established data with scores attached (for instance, if holding a current credit card with an account in good order is considered a high score metric) then a reliable decision is the result since the same criteria are being used as the ‘benchmark’ every time.

It could be worth talking to an established data analytics expert to see how data scoring can be put to use in your business.

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