AI-powered decisioning is crucial to maximise customer lifetime value

Discussions of risk decisioning platforms often focus on onboarding and loan origination. However, the investment in the start of the customer journey is only one piece of the puzzle; an organisation’s growth depends not only on attracting new customers, but also on retaining and maximising the value of its existing customers.

AI-powered decisioning is crucial to maximise customer lifetime value

This is especially true as the rapid increase in financial services competition has led to financial institutions needing to look beyond onboarding to maximise the lifetime value of their customer base. Research has shown that upselling increases revenue by 10-30% on average, and the probability of selling to an existing customer is 60-70% – provided that customer is happy with the support and service.

While sophisticated, automated onboarding/origination solutions are key, it’s important to also focus on the tools companies use to support the myriad other decisions across the life of that customer. There is immense value in having a unified end-to-end solution for decisions throughout the customer journey.

By eliminating siloed systems and supporting ease in sharing of customer intelligence, the right decisioning platform can enable more accurate, faster decisions across the customer lifecycle, mitigating an organisation’s risk and maximising its growth.

Risk decisioning can support key company initiatives, including:

• Upsell/cross-sell opportunities

Target your customers effectively with AI-powered intelligence to enable the right offers at the right time, increasing the likelihood of acceptance.

• Strategic risk mitigation

Test and deploy additional data sets and AI models for proactive credit line management and to regularly evaluate risk exposure and predict portfolio performance.

• Pre-collections and collections strategies

Accurately predict potential defaults before they happen and identify the best treatment strategies and most effective communication channels for those customers that do get into trouble. 

Risk decisioning across the customer lifecycle: barriers abound 

Data and analytics are the key to smarter risk decisions across customer portfolios, but ensuring the right people have access to the right data at the right time can be challenging. If risk teams, fraud analysts, and data scientists all have access to different data sets, it’s difficult, if not impossible, to make cohesive, accurate decisions. Whether it’s fraud prevention, identifying upsell opportunities, meeting compliance requirements, or determining collections strategies, the ability to access and integrate both real-time and historical data is crucial.

If integrating additional data sources, expanding to new regions, launching iterative products, responding to fraud threats, or making changes to workflows is difficult and time-consuming, then it’s not sustainable. Having to rely on the internal IT teams or a software vendor for any of the above slows down the time to market, limiting an organisation’s ability to respond to evolving consumer needs and changing market conditions.

Additionally, it’s critical to know how current risk models are performing, if a loan scorecard is still accurate, how reliable the fraud scores are, or how many customers are defaulting on payments and heading towards collections. Having instant access to insights has a huge impact on risk tolerances, revenues, and future growth. Without an on-demand, integrated view into how decisioning models are performing across the lifecycle, it’s challenging to change course, alter processes, and power profitable product innovation.

In terms of technology, easy machine learning model deployment is essential to enable an organisation’s ability to accurately respond to market threats or opportunities before the competition can. It’s important to note that getting the right risk models in place is not a “one and done” proposition; consumer behavior continues to change and risk models need to keep pace. 

Decisioning for customer journey success

Attracting and acquiring new customers is only one key to increasing an organisation’s revenue and growing its business. In times of economic uncertainty, it becomes even more critical to proactively manage and mitigate risk to improve customer retention, reduce defaults, and maximise revenue opportunities.

The right decisioning platform that is all-encompassing – supporting everything from credit, fraud, compliance and product decisions – is key to long-term success, growth, and profitability.

AI-powered decisioning complete with case management, data, and insights provides the foundation for more accurate, automated risk decisions across the entire customer lifecycle – enabling a superior customer experience and allowing financial institutions to maximise customer lifetime value.

About the author

Kathy Stares is executive vice president of North America for Provenir, a global leader in AI-powered risk decisioning software, processing more than four billion transactions annually for disruptive financial services organisations in more than 50 countries worldwide.

As a member of Provenir’s executive team, she is introducing creative account management approaches to support the company’s aggressive growth strategy. With more than 20 years of experience and accomplishments in financial services technology, she brings deep knowledge and curiosity about risk decisioning innovation.

Sponsored by Provenir, the winner of the FinTech of the Future – Data & Insights Award at the Banking Tech Awards USA 2023