Home Finance How to digitalise the future of financial services with predictive analytics

How to digitalise the future of financial services with predictive analytics

by jcp

By: Adam Mayer, Senior Manager, Qlik

Data is the silent engine that drives the financial services industry. It is what empowers companies to leverage detail-rich information about their customers. Everything from opening a bank account to applying for a mortgage is buttressed by the notion of data.

As such, it is surprising that the industry has hesitated in implementing innovative data technologies. In fact, research from Qlikfound that just 55% of UK employees working in financial services believe their company uses data effectively.

Thereluctance to adopt leading-edge data technologies can be partly explained by the industry’s strict regulatory standards, with digital innovation complicated by the need for stringent compliance. The financial services industry is a heavily regulated arena, which also adds an extra layer of complexities. With $270 billion per year spent on compliance and regulation, it is no surprise that 46% of IT leaders in financial services feel that the regulatory burden of predictive analytics outweighs its benefits.

The regulatory burden is compounded with an overall lack of trust around the use of more advanced data applications, such as the use of predictive analytics.

Just half of IT leaders trust predictive analytics to be always accurate (45%) or without bias (50%), highlighting a real concern that unchecked applications could result in unintended consequences further down the line.As a result, some IT leaders fear that their customers won’t trust the decisions made by predictive analytics solutions.

Richard Speigal, BI Centre of Excellence Leader at Nationwide Building Society highlighted this issue, stating “if you can’t explain how the models are built and can’t explain how they’re working, there’s always going to be a question of trust.”

Overcoming trust and compliance for data innovation

The transformative potential for data in financial services organisations means that finding new waystoovercome the challenges around trust and regulation is of critical importance. Research conducted by IDC on behalf of Qlik found that three-quarters of financial services firms reported an increase in profit and revenue as a result of their investments in data management and analytics –an average increase of 18% and 17% respectively.

The promise of Active Intelligence – where trust, real-time information proactively triggers and compels individuals to take informed action at the most important moment – to further transform the use of data across all industries also presents a risk of further disruption for financial services firms that can’t overcome these regulatory and trust challenges today.

Maintaining the human element in decision-making, even when prompted and informed by predictive analytics and machine learning algorithms, will be key to helping financial services firms adopt more advanced solutions without fear. Here is where integrating predictive analytics into existing business intelligence (BI) platforms becomes extremely helpful. This approach not only democratises access to predictive forecasts across the organisation, but ensures that every decision is compliant and can be trusted.

When integrating predictive analytics into BI platforms, there are two factors that are key to building trust in the insights and the decisions they inform:

  1. Begin with a data pipeline

If companies want to double down on the value of the data they already have, they must consider the journey of their data from beginning to end. This is why a key success factor when implementing predictive analytics is the data pipeline that underpins it. Data pipelines are the pathway that transforms raw data into analytical-ready information, making it continuously available to the rest of the business at scale, while maintaining security and governance. As such, flaws in an organisation’s data pipeline mean for a shaky foundation when implementing predictive analytics and the decisions these insights inform.

  1. Upskill to improve trust in insights

Integrating predictive analytics into BI platforms allows employees at all levels to make more accurate decisions. However,in order to unleash this potential, it is crucial that staff receive data literacy training that enables them to not onlyunderstand and communicatethe insights, but also question and challenge them to provide the checks and balance that are so critical to trust.

It would be impossible for employees to be confident that the decisions they are making are accurate if they do not understand the data informing them. And this is critical given so many of the decisions a financial services organisation makes – from opening a bank account to approving a mortgage – can have a major impact on a customer’s life.

This sentiment is shared by Nationwide’s Richard Speigal, who stated “Being able to understand the workings behind the decision, to have that data literacy to ensure the right decision is being made, is critical”.

Welcoming a predictive future

While we may not have the power to look into the future, predictive analytics can bring us one step closer to that reality. And using data to look forward, predict and prompt us to make decisions in the business moment will be critical to achieving Active Intelligence.

With a strong data pipeline and data literateworkforce, there is no reason why financial leaders should be apprehensive about integrating predictive analytics into their BI platforms. And by marrying the best of human and machine intelligence in this way, financial services organisations will be able to make smarter and more trusted decisions with their data to better serve their customers.

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