Francois Grobler, Chief of Decision Analytics, Experian Africa.

COVID-19 accelerated digital adoption which forced many companies to think about their own digital strategies. The customer experience journey has become more and more digitally driven, making the ‘time to decision’ more important than ever, especially in the lending environment.

In an increasingly competitive and cost sensitive market, businesses are under pressure to accelerate the credit application process to drive revenue growth whilst reducing technology costs in support of a stronger profitability focus. At the same time, they need to carefully manage risk, ensure responsible lending, and provide customers with a personalised and affordable product.

Consumers have high expectations for fast, convenient, and personalised digital experiences. In fact, 63% of consumers have abandoned an application for credit at least once in the past 12 months, according to a study conducted by Forrester Consulting on behalf of Experian in August 2021.

If banks or lenders ask for too much information or do not use the data, they already have at their disposal to populate application forms, it makes the process complex and frustrating for consumers. They need to be able to ask the consumer as little as possible to ensure the application process is simple with a good user experience.

This isn’t possible without advanced software processing the data, making quick decisions, and coming back to the consumer with a credit decision in minutes. To do this, businesses need an automated way to understand each applicants’ risk profile and onboard the right customers quickly whilst using data and analytics to improve the accuracy of decisions, reduce manual effort and lower cost.

Increased automation of decisions

To improve the ‘time to decision’, businesses need to embrace automation. In doing so they can reduce the volume of manual reviews and operational cost.

The key to unlocking automation is the intelligent use of data and analytics to more accurately predict creditworthiness and affordability to deliver decisions within seconds. For example, in customer onboarding, businesses can eliminate unnecessary manual steps and automate routine tasks, such as verification and decisioning, so that customers can either submit documents digitally with autonomous review and approval or consent to an automated “pull” of the respective documents.

According to the Forrester study, 33% of businesses highlight ‘Lack of automation in our tools’ as the biggest challenge prohibiting them from achieving their top initiatives. However, the availability of new data sources combined with analytical techniques and smart workflow orchestration provides greater confidence to automate decisions.

Moving decisioning to the cloud

This may seem like an obvious move, but many businesses are yet to commit to a Software-as-a-Service (SaaS) approach to decisioning.

There are several reasons for this. Some may be waiting for alignment on broader organisational transformation projects, whilst others are carefully considering cloud deployment options e.g., private vs public. That said, it’s clear that the market is moving to SaaS to take advantage of the lower operating costs and increased agility that cloud deployment provides.

From a decisioning perspective, businesses moving to a SaaS model will not only see total cost of ownership come down by removing many internal costs, but they will benefit from faster updates on software releases that are delivered with less overheads, meaning service quality and time to value will be greatly enhanced.

SaaS solutions are also more flexible and scalable compared to on-premise models. Auto-scaling means that volumes can be easily adjusted up or down, meaning clients won’t be tied to a fixed price should volumes change but equally they won’t have to think about additional server capacity when scaling up as this will be provided by the SaaS vendor.

Affordable world class technology available to ALL

Previously credit bureaus sold decisioning tools, customer management systems and credit origination systems, etc., to each customer separately. All of this is now in the cloud which means banks and lenders can use these tools without having to pay a sizeable upfront amount with on-premise and maintenance costs. We now host these tools for them which they can access on a pay-per-transaction basis.

This means lower costs for the larger credit lenders, but more importantly it means that smaller lenders, consumer finance businesses and even some retailers that didn’t have the funds to implement these high-tech solutions into their environment, now have access to the same functionality and benefits that larger businesses have.

Decisioning has entered a new era

With the rise of alternative data, there are more data sources available than ever before, with new analytical techniques creating opportunity to extract greater value from this data to deliver significant performance gains for businesses. Machine Learning is transforming the way analysts develop scores and strategies, and there is a race to make better use of automation to drive faster, more accurate decisions.

For example, a machine learning scorecard adjusts as the data changes – as the economy, consumer behaviour and payments patterns change – which means the scorecard is now much better at predicting future risks. This can drive more revenue, lower bad debts, and improve profitability for lenders.

Businesses require software that is ready to support this rapidly changing environment. They need tools that drive more accurate decisions with incremental improvements that lead to better commercial performance. Every percentage improvement can make a difference and there are several marginal gains that businesses can implement to improve performance and get the most from their decisioning software.