How Is Data Analytics Used In Financial services?

How Is Data Analytics Used In Financial services?

Data analytics in financial services can be used to inform decision making.

Within financial services, data analytics is applied to a broad range of information. From revenue KPIs and predictions, to fraud detection and social listening.

Careful analysis of data allows banks and financial organisations to better understand their customers, thereby delivering the best service possible.

3 Ways Data Analytics Is Used In Financial Services

In this article we’re drilling into 3 ways data analytics can be used in the financial services industry.  We acknowledge that there are more than 3 ways data analytics is used in financial services, particularly given the huge amount of data captured and the variation of sources. 

That said, 3 of the most obvious ways data analytics is used in financial services are:

Customer insights and personalisation → 

Data unlocks insights about your audience, and in the financial services industry this means creating a 360 degree view of a customer’s profile and behaviour. 

Customers can be segmented into different personas that identify common needs, demographics, and behavioural patterns. The likely action, or preferred product, can then be predicted using AI and ML techniques. 

The better a business understands their customer, the more tailored they can make their products offering,  therefore the more personalised the marketing approach and communications can be. 

Targeted analytics and insights allows you to build efficient strategies to improve customer experience, and ultimately attract and retain customers by providing the right message, with the right services, at the right time.  

Fraud detection and compliance → 

Detecting fraudulent transactions and remaining compliant with the use of sensitive data is one of the biggest challenges faced by financial institutions.  

With recent data breaches occurring in large Australian businesses, there is much discussion around how much sensitive data should be stored, but that is a story for another day!

Customers and product base can be segmented into varying degrees of perceived riskiness, and then strategically apply appropriate levels of controls to flag fraud or potential compliance breaches.

Data analytics also allows financial organisations to build a picture of what’s ‘normal’ for an individual in terms of their spending habits. Then, if a transaction occurs that doesn’t fit the mould, it can be immediately identified.

Such prevention and detection models that identify outlier or unexpected behaviour are invaluable in ensuring positive customer outcomes and preventing future remediations. 

Improving operational efficiency → 

In times of economic uncertainty, a company’s ability to operate efficiently will ultimately help to keep costs under control and invest their resources in value-adding areas.

Data analytics enable companies to take stock of the past and current results, and  generate insights which will highlight focus areas in the organisation’s performance metrics.

This involves the identification of areas for improvement – such as processes that are inefficient  and costly. It also involves highlighting areas that are best practices – and enabling learnings from these benchmark areas to uplift other parts of the business.

By performing in-depth analysis of data from multiple angles in a timely manner, patterns can be identified quickly, and insights can be translated into actionable outcomes. 

Case Study: Financial Institution

InfoCentric has a close working relationship with a major Australian financial institution. 

Our work in data analytics has covered multiple areas of business, particularly within the marketing teams, around customer segmentation and communication strategy recommendations. 

We harnessed end-to-end customer data across customer demographics, product holdings over time, channel origination, and digital behaviour to provide a profile of customers and their likelihood to take up a new product. 

  • What are the likely geographic location, age range, demographics of the customer 
  • What is the product path the customer have taken up prior 
  • What are the common customer journeys across the each channel, digital and offline 
  • Performance history of prior marketing initiatives 

By analysing the integrated customer data we were able to provide comprehensive analysis of how to best serve each category of customer. 

The recommendation included an A/B testing experimentation approach on one of the marketing strategies, which later proved the analytics driven approach provided acquisition uplift and cost efficiency improvement.  

Ready to use data analytics in financial services or other industries?

Whether you work in the financial services industry or not, your data will unlock insight and aid decision making if managed in the right way.

At InfoCentric, we’re experts in data management and analysis and can help you make the most of the data you have.  

We are also experts in the Financial Services and can provide deep insights into best practices for the use of data analytics within the industry.

Ready to get started? Book a consultation today.