How financial services can invest in the future with predictive analytics

The financial services industry is flooded in data. Out of all the industries, it is the one that captures the most information on its customers. It’s also one of the industries in line for huge change as the accelerating pace of technological development demands new business models and skills that drive the evolution of services and products delivered to customers.

With all of this data at its fingertips, it’s well-positioned to meet this challenge head-on, right? The reality is that the industry is struggling to make the best use of its data. According to our research, just over half of UK employees working in financial services (55%) believe their company uses data effectively to increase its competitive advantage. But what is holding financial services organisations back from adopting the latest innovations in data and analytics? A lack of trust and regulatory risks.

The lack of trust stems from both customers and IT leaders at financial services organisations. Customer trust in banks is low, to the point where just 14% of consumers sought help from their bank when experiencing a financially impactful life event in the past five years. To rebuild trust, financial services organisations need to prove to consumers that they are able to make the most accurate, consistent decisions every time. This can be challenging when introducing more advanced analytical solutions that move humans one step further from the decision-making process, such as predictive analytics and machine learning.

These are concerns that Richard Speigal, BI Centre of Excellence Leader at Nationwide Building Society, recognises, “If you can’t explain how the models are built and can’t explain how they’re working, they there’s always going to be a question of trust.”

Working within a highly regulated industry also brings additional complexities, so much so that 46% of IT leaders in financial services feel that the regulatory burden of predictive analytics outweighs the benefit.

The challenges around trust and regulation are understandable. Still, if financial organisations want to move forward using the data they hold to the greatest advantage, they will have to find a way to overcome them. That starts with ensuring that these solutions are not left to run amok. Its output must ultimately be managed by a human counterpart, who can question and determine what the best approach is based on the information and their experience.

But how do we marry this machine and human intelligence so that it doesn’t overwhelm employees by adding more steps to their decision-making process? One solution is to integrate predictive analytics into the existing business intelligence (BI) platforms which are used by employees at every level in nearly all financial services organisations. This will help to democratise access to its powerful analytical output, along with governance, ensuring a steady handle on every decision made. Decisions that can be trusted by the employee, their management and – most importantly – their customer.
Of course, I’m simplifying. The reality of carrying out this integration is a little more complicated. What’s the secret then to getting it right and harnessing the potential it offers? Well, there are two key factors to consider:

1. Start with your analytics data pipeline

If you want to improve the output and outcome of your analytics, building high performant analytic data pipelines that deliver real-time data, should be your first port of call. Let’s consider the end goal for analytics in an organisation; empowering employees to take informed action. What if you could enable action to be taken in the moment, based on analysis and proactive alerts that are fuelled by real-time, hyper-contextual data? If you can achive this, that’s when you can change from just operating in a passive mode of data consumption with your business intelligence and shift towards a state of Active Intelligence. However this is only achievable if the pipeline is robust. Otherwise, how can you trust – back to that key concern – that actions are being taken on the correct data.

This is where many companies are coming unstuck. They are struggling to integrate the data into the pipeline and then deliver it in a reliable enough state to feed their predictive analytics programmes. This is leading to fears over its quality, privacy issues and the speed of the integration process.

As Nick Blewden, Lloyds of London, said, “The data itself isn’t the most valuable part; it’s what you do with it”. So, it’s critical to invest in the entire process that will help transform raw data into business-ready, trusted insights.

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2. Empower your people

We naturally feel more confident about using something if we understand it. So, it’s perhaps unsurprising that the second consideration is data literacy.

Predictive analytics empowers users to make better decisions that consider what has happened and what is likely to happen based on the available data. And those decisions can only be made if employees understand what they’re working with.

They need good data literacy competencies to understand, challenge, and take actions based on the insights, with greater abilities to realise the limitations and question the output of predictive analytics. After all, a forecast’s accuracy depends on the data fuelling it, so its performance could be impacted during an abnormal event or by intrinsic bias in the dataset.

Employees must have confidence in their understanding of the data to question its output. This is particularly true when decisions could directly impact customers’ lives, particularly the influential impact of those made in the financial sector – from agreeing to an overdraft and making it to payday to approving a mortgage application in time. And when communicating potentially emotionally fraught decisions made using predictive analytics, it’s also key they feel comfortable explaining to customers and other stakeholders how those decisions were made.

Speigal again summarised this perfectly, “Being able to understand the workings behind the decision, to have that data literacy to ensure the right decision is being made, is critical”.

Investing in a predictive future

As Malcolm X said, “The future belongs to those who prepare for it today”. While we may not have the powers to see into the future, predictive analytics will help the financial services industry predict what it might look like and make decisions that will allow them to prepare – and prepare their customers – for that future.

With a robust data pipeline and data literate workforce, predictive analytics is not something to be afraid of; rather, it’s a tool that will help financial services organisations regain the trust of their customers and their people as they move forward by empowering evermore informed decision-making.

Written by Adam Mayer, senior manager at Qlik

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