Packed with advanced data analytics, machine learning and AI, predictive analytics is a game changer in the financial services sector. The technology allows computers to mine organisations’ data vaults to identify trends, improve operational efficiency, manage risk, gather actionable audience insights, tailor customer support and drive growth.

Considering these multiple benefits, it’s no surprise that one report[1] projects the global market for predictive analytics in banking to surge from $3.63 billion in 2024 to $19.61 billion by 2033.

The advantages of predictive analytics, such as reduced costs and improved resource allocation, will appeal to procurement decision-makers.  Predictive technology can also help marketers and customer experience professionals meet personalisation demands. Balancing these benefits with a member-centric approach gives building societies a unique edge over other finance companies.

How does predictive analytics work?
 

Building societies can use predictive analytics to draw on historical, real-time and even synthetic data to improve insights, recognise potential issues and anticipate the needs and preferences of their members.

There are many practical applications of predictive analytics for building societies, but mainly they help to:

• Identify trends in savings behaviour
• Predict mortgage and other loan needs
• Assess risk and spot potentially fraudulent activity
• Enhance product recommendations

Balancing technology with a human touch
 

Building societies have a reputation for supporting local communities and treating members as individuals, which other financial companies lack. The human touch is an increasingly vital differentiator across all businesses. A Salesforce report on customer expectations found that 61% of customers feel they are just another number to companies.[2]

Innovative predictive analytics and a member-centric approach mean customer service teams can respond to each member’s needs with the personalised, empathetic support and guidance they expect from a building society.

From AI prompts to help service agents resolve issues immediately, to giving them access to member data and empowering them to address member concerns independently, the result is a frictionless and modern member journey.

Benefits of predictive analytics for building societies
 

Enhanced personalisation: Customised member experiences should be part of marketers’ strategies to meet the growing demand for personalisation, especially among the 18-34 age group. A Moneyhub building societies white paper reveals that 64% of respondents in the younger bracket expected personalisation versus 47% in the total population.[3]

Improved operational efficiency: Efficiencies, including lower costs, improved service and stronger supplier relationships, are another benefit of predictive analytics. Procurement can use it to identify patterns and recommend ways to streamline purchasing, optimise inventories, analyse supplier data for potential disruptions, and identify opportunities to optimise contracts.

Competitive edge: BSA figures on the mortgage market[4] show that building societies outperform banks across all customer service measures and community sentiment through retained high street branches. Innovative predictive analytics help building societies maintain their competitive edge by providing insights that others in the financial services sector do not have.

However, you should bear in mind regulatory considerations, such as the UK’s transition from General Data Protection Regulation (GDPR) to the proposed Data Usage Act (DUA) and Data (Use and Access) Bill (DUAB). Some of the limitations these rules impose on using predictive analytics include requiring explicit content for its use[5], as well as customers’ right to object to profiling and automated processing of their data for marketing.[6]

Steps to get started
 

Once you have identified your business needs, a simple roadmap to help building societies start implementing predictive analytics starts with the following steps:

• Assessing current data capabilities, such as quality and completeness
• Selecting the correct tools, technologies and techniques, from data storage tools to cloud platforms
• Deploying validated models aligned with objectives into your systems
• Training teams to interpret and act on insights effectively

Afterwards, you must also track performance continuously and add new data to maintain the quality of your predictive analytics.

The potential of predictive analytics for building societies is transformative. I’ve listed a few benefits: personalisation, enhanced operational efficiency and competitive advantage. However, I encourage marketing, member experience, procurement and data and technology professionals to consider how data-driven strategies can drive growth and member satisfaction in your building society.

Read our insights on Paragon’s website or connect with Peter Toole at peter.toole@paragon-cc.co.uk.[AH1]