Financial services (FS) organisations increasingly rely on “realtime” information – about their customers, products, services, market, regulation and even competition – as part of their ongoing efforts to become more connected, digitally enabled and information-led businesses. And, achieving this is underpinned by unlocking the power of their data.

By Yolanda Smit, regional director (Gauteng) at PBT Group

While data has always been fundamental to business success, how it is gathered, stored, accessed, and analysed is evolving thanks to continuous digital advances. From the proliferation of Internet access, mobile devices and applications, to cloud computing, the Internet of Things (IoT), social networking and more traditional contact centres, as just some examples – organisations are finding themselves inundated with a wealth of data at their disposal.

Therefore, today’s multi-platform data sources require organisations to embrace a different outlook and use a myriad of analytical solutions – to make sense of the vast amounts of structured and unstructured data and – to assist them in analysing it faster and better than before.

With this in mind, below I’ve outlined some notable data-driven trends we are seeing players in the financial services sector – particularly short-term insurers – adopting and leveraging on, with considerable gains to their business.


Single view (of data)

The availability of multiple technical platforms to cater for different types of data have made this process more efficient – and cost-effective.

For example, when insurers settle a claim they can use the same platform to access the claim itself, customer information, geo-spatial data on when an accident happened, information on the car tracking device, and even social media about what the customer posted prior to the accident.

This provides the insurance company with a single view of – not only the customer but of – the transaction in the form of the claim, which empowers the organisation to manage its system more efficiently through event-based insights. Having such an integrated approach will also enable the insurance company to manage their customers’ experiences through more streamlined engagements – which could be a key differentiator in a competitive market.


Predictive modelling

As the business can now be managed based on events, organisations can start predicting points of engagement or even engineer it in such a way to happen according to pre-determined scenarios.

For example, a car insurance company would be able to predict with a high probability if a customer is more likely to be involved in a collision sometime in the future because they regularly post updates and photos to their social media platforms whilst driving – and therefore aren’t focused on the road or other drivers around them. In this instance, using multiple sources for combined predictive modelling can provide a car insurance company with stronger probability data that can be used to offer their customers individualised packages and premiums.

Such an approach to predictive modelling also gears the organisation to maximise on engagements when they occur. Ultimately, these (and other) data-enhanced engagements are resulting in FS organisations becoming better at what they do.



Organisations across the financial services sector are actively looking at practical approaches to introducing article intelligence (AI), driven by machine-learning (ML) and cognitive computing (CC) models, into their environments. Whether it is used to drive enhanced customer experiences, to promote new potential revenue streams for new products or services, or cost reductions for improving operational efficiencies – automation is expected to become more integrated into organisational processes and systems.

The rapidly growing uptake of automation is also directly linked to the next trend…


Data science to sustain

Considering how much structured and unstructured data is being pushed through more channels into the organisational back-end systems, data science capabilities is turning into a business priority if sense is to be made of all this information.

It should be noted that data science is not AI itself. Rather, it is the data scientists that “teaches the artificial engine to become intelligent”, through statistical descriptive, predictive and prescriptive modelling. Essentially, effective data science is a critical success factor to sustain any of the other trends above.

Currently, very few organisations succeed at deploying “sustained” data science. Getting data science right requires the right data scientist skills, solid governance, data architecture and data engineering capabilities. Yes, sustained value of data science lies in the complete end-to-end life cycle wrapped in sound data management capabilities.

Core to this is good data science governance, guiding the data science process, business and technical architecture, model management, model performance monitoring, systems monitoring, business continuity strategy, all leading to achieving one main objective: trust, as in “trusting the AI to make decisions and act autonomously”.

Of course, this digital shake-up is not a case of blindly following all the latest trends. It is pointless to implement ‘shiny new toys’ when there is no business case for it or it does not add significant value in alignment with the business strategy. However, to remain relevant in the new digital era and competitive in Industry 4.0, all FS organisations must examine how best they can sweat their data assets, aimed at extracting the most benefits in information and to develop bespoke solutions tailored to the stakeholders of the organisation.

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