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By Todd O’Brien, head of sales, advanced analytics, Dell Software

Today’s Financial Services sector is characterised by a two important developments that have come about since Lehman Bros fell over: the need to provide a more targeted and personalised service in order to regain and retain customer trust; and the requirement to satisfy much tighter regulations. In the age of Big Data, the challenge for the industry is how best to use the plethora of available data – not only to appease the regulators but also to improve the customer experience.

Enter the world of advanced analytics, which offers Finance Services providers the opportunity to unlock insights within the data that will enable them to gain an advantage through better understanding customers, competitors and employees. The use of complex algorithms and sophisticated statistical models to analyse data and information is not a new phenomenon however. In fact, the industry’s leading predictive analytics software solutions have been around for decades. And yet, thanks to significant advancements in our ability to capture, store and integrate data, combined with businesses’ growing desire to keep up with – and ultimately predict – rapidly changing customer behaviours, interest in predictive analytics technology is at an all-time high.

A great example of Financial Services applying advance analytics to improve customer services is Danske Bank in Denmark. Where previously it created analytical models using business modellers before handing over to IT who hand-coded the model on the legacy systems, the bank now uses advanced analytics to both develop the models and put them into production. Now, when a customer applies for a loan, the bank’s answer is based on these models, which use a variety of data, including data related to the customer as well as external data, such as data from credit bureaus. The analytics find patterns in that data related to good and bad financial behaviour, predict whether a customer will default within a year and quickly determine credit worthiness. Crucially, Danske Bank has reduced the time it spends on models by up to 50 percent. The models enable the bank to make the right decisions and provide customers with the right prices and the right credit limits quickly.

As far as capabilities go, advanced analytics is a mature field that’s been around as long as computers themselves but there’s a significant skills gap leading to a worldwide shortage in the knowledge required to develop and deploy advanced statistical and analytical models. Although additional advancements in technology, specifically those that abstract complexity and make advanced analytics more attainable to business analysts and other end users, can help address this growing skills gap, that alone isn’t likely to be enough – and that’s where collective intelligence comes into play.

Collective intelligence, or crowdsourcing, has been around for a while but intersect it with advanced analytics and you have a combination of technological capability and shared know-how that is powerful enough to change the Financial Services world. Big data combined with crowdsourcing changes the way Financial Services providers do business because the real value in this type of intelligence lies in its ability to predict what is most likely to happen in the future. It is this insight into customer behaviour that needs to form the basis of how products and services are packaged and presented for sale. To this end, in 2011 a UK bank became one of the first to embrace crowd sourcing when it launched a dedicated site or ‘Lab’ to request crowdsourced feedback on four main subjects: website redesign concepts, QR codes, a mortgage app comparison tool and the idea of the Lab itself. Although now closed, the Lab gained a total of 4,158 comments and 1,345 ratings on its ideas in its first month alone. Since then, many other financial services providers have jumped onto the collective intelligence bandwagon.

In the modern, data-driven era in which we now live, advanced analytics combined with crowdsourcing has become a must-have capability for the success of Financial Services providers. It allows them to harvest the real value in ever growing amounts of data, advancing the use of customer insight in order to improve market interaction in ways that include service delivery, improving the customer experience and extending the lifetime value of the customer.

I’m confident this collective intelligence message will continue to resonate, especially as the analytics industry shares more examples of all the amazing things it can accomplish with distributed intelligence. After all, we’ve always known that “two heads are better than one,” so imagine what can be done when you amplify that with hundreds of thousands of people and interactive models.

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