How Financial Services Can Invest in the Future Using Predictive Analytics

Adam Mayer, Senior Executive at Qlik, discusses the benefits predictive analytics can bring to financial services

Predictive analytics can be an essential tool for the financial services industry.

The financial services industry is inundated with data. Of all the industries, it is the one that captures the most information about its customers. It is also one of the sectors undergoing huge changes, as the accelerated pace of technological development demands new business models and new skills that drive the evolution of the services and products provided to customers.

With all this data at hand, it’s well positioned to tackle this challenge head-on, isn’t it? 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 is making effective use of data to increase its competitive advantage. But what’s stopping financial services organizations from embracing the latest innovations in data and analytics? A lack of confidence and regulatory risks.

The lack of trust stems from both customers and IT managers in financial services organizations. Customer confidence in banks is low, to the point that only 14% of consumers have sought help from their bank when they have experienced a life event with financial impact in the past five years. To restore trust, financial services organizations need to prove to consumers that they can make the most accurate and consistent decisions every time. This can be difficult when introducing more advanced analytical solutions that take humans away from the decision-making process, such as predictive analytics and machine learning.

These are concerns that Richard Speigal, BI Center of Excellence Manager at Nationwide Building Society, acknowledges: “If you can’t explain how models are built and how they work, there will always be a question of trust.

Working in a highly regulated industry also brings additional complexities, so much so that 46% of financial services IT managers believe the regulatory burden of predictive analytics outweighs the benefits.

The challenges of trust and regulation are understandable. Yet if financial organizations are to move forward in making the best use of the data they hold, they will need to find a way to overcome it. It starts with making sure these solutions aren’t left behind. Its production must ultimately be managed by a human counterpart, who can question and determine what is the best approach based on the information and their experience.

But how to marry this machine and human intelligence so that it does not overwhelm employees by adding more steps to their decision-making process? One solution is to integrate predictive analytics into existing business intelligence (BI) platforms that are used by employees at all levels in almost all financial services organizations. This will help democratize access to its powerful analytical results, as well as governance, ensuring stable control over every decision made. Decisions worthy of the confidence of the employee, his management and, above all, his client.
Of course, I am simplifying. The reality of achieving this integration is a bit more complicated. So what’s the secret to getting it right and harnessing the potential it offers? Well, there are two key factors to consider:

1. Start with your analytical data pipeline

If you want to improve the output and bottom line of your analyzes, building high-performance analytical data pipelines that deliver real-time data should be your first port of call. Consider the end goal of analysis in an organization; empower employees to act with full knowledge of the facts. What if you could enable action in the moment, based on proactive analytics and alerts fueled by hyper-contextual data in real time? If you can achieve it, this is when you can go from operating in a passive mode of consuming data with your business intelligence and moving into a state of active intelligence. However, this is only achievable if the pipeline is robust. How else can you trust – back to this key concern – that action is taken on the correct data.

This is where many businesses take off. They struggle to get the data into the pipeline and then deliver it in a state that is reliable enough to fuel their predictive analytics programs. This raises concerns about its quality, privacy concerns and the speed of the onboarding process.

As Nick Blewden, Lloyds of London, said: “The data itself is not the most valuable part; that’s what you do with it ”. It is therefore essential to invest in the entire process that will help transform raw data into reliable, ready-to-use information.

Climb to new heights using real-time data analysis

Chris Harris, Vice President, Field Engineering at Couchbase, explains how businesses can move forward using real-time data analytics. Read here

2. Empower your employees

We naturally feel more confident in using something if we understand it. So it is perhaps not surprising that the second consideration is data mastery.

Predictive analytics enables users to make better decisions based on what has happened and what is likely to happen based on available data. And these decisions can only be made if employees understand what they are working with.

They need good data literacy skills to understand, challenge, and take action based on the information, with greater abilities to realize boundaries and question the results of predictive analytics. After all, the accuracy of a forecast depends on the data that feeds it, so its performance could be affected during an abnormal event or through intrinsic bias in the data set.

Employees need to be confident in their understanding of the data to question their output. This is especially true where decisions can have a direct impact on the lives of clients, particularly the influential impact of those made in the financial industry – from the acceptance of an overdraft and the payment through to the approval of an overdraft. ” apply for a mortgage on time. And when communicating potentially emotionally charged decisions made using predictive analytics, it’s also essential that they feel comfortable explaining to customers and other stakeholders how those decisions came about. been taken.

Speigal once again summed it up perfectly: “being able to understand the workings behind the decision, to have this data literacy to make sure the right decision is made, is essential”.

Invest in a predictive future

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

With a robust data pipeline and a data-savvy workforce, predictive analytics is nothing to fear; rather, it is a tool that will help financial services organizations regain the trust of their customers and employees as they grow by enabling ever more informed decision-making.

Written by Adam Mayer, Senior Executive at Qlik

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