Data & Analytics Capgemini: Business Analytics (UK)

Analytical risking in a digital world


Alastair Brown

June 24, 2017

With every day that passes, a greater number of products, services and customer interactions are being delivered digitally.

Society’s ‘digital transformation’ has been responsible for improvements in customer experience, reduced costs to serve for organisations and an exponential growth in data and customer insight, affecting all sectors across the economy. From banking, retail, utilities to Government, it is hard to think of an organisation that is not interacting digitally with their customers.

Yet the need to serve customers in a quick, digital fashion frequently conflicts with the need of an organisation to check and risk assess customers and their requests. Many organisations are struggling to balance the need to embrace digital with the traditional requirement to ensure fraud and error is minimised.

However, recent leaps in data and analytical technology mean that organisations can now deliver ‘real-time’ analytical digital risking capability to have the best of both worlds; digital customer experiences that are risk assessed and secure.

In its simplest form, business rules and risk assessments can be quickly developed to interact with customers during their digital journey. This can include:

  • Risk rules to check customers are submitting expected inputs within their digital submission
  • Prompting a ‘nudge’ of a customer when an unexpected input is entered
  • Risk rules to identify customers who require further investigation based on their inputs within their digital submission

But this is just the start.

The opportunity to do so much more is now possible thanks to our ability to store and exploit society’s seemingly infinite levels of customer data. This data can be cross-checked in ‘real-time’ during a digital transaction, or be used as part of a complex calculation that interacts with new information being submitted digitally by the customer.

For example, a customer might provide an estimate of their income as part of an online application. This customer estimate can now be cross-checked in ‘real-time’ with the terabytes of financial and customer-related data already held by the organisation.

Alternatively, predictive modelling can be run, again in ‘real-time’, to forecast the likely outcomes of agreeing to and proceeding with the customer’s request.  

Beyond this, decision tables and risk profiles can be developed to provide a macro risk assessment of a customer across all of their inputs as they move through their digital journey.

Figure 1: Analytical digital risking theoretical customer journey

This capability can now be delivered inexpensively and at pace. A wide range of analytical risk management software options are available, spanning traditional licensed options to new, open-source alternatives.

A final consideration is to ensure this new data and analytical technology is supported by a well considered analytical operating model. This will ensure that an organisation’s risk experts are given the required access to these technologies, putting them at the heart of an organisation’s digital risking function.

The Capgemini Consulting Customer Experience and Analytics team has recently delivered ‘real-time’ risking capability with a client, covering a number of their digital services.

Upon completion, our performance testing showed that our risk decisions, while calling upon approximately 80 million rows of customer data, were being delivered in less than 400 milliseconds.  To put this into context, it takes you 400 milliseconds to blink! This digital risking solution is forecast to deliver significant savings by reducing fraud and error and eradicating unnecessary call handler interactions.

So it is possible to have the best of both worlds: digital customer experiences that are protected and risk assessed in ‘real-time’. These solutions are relevant to all sectors and represent the future of analytical risking in an increasingly digital world.

This article was written by Alastair Brown from Capgemini: Business Analytics (UK) and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to

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