Software implementation for asset financers: how to get it right

Asset finance companies play an essential role in greasing the wheels of commerce. Despite economic headwinds – high energy costs are driving inflation, a serious concern for firms’ balance sheets – the UK’s Finance and Leasing Association (FLA) reports asset finance new business grew 2% year-on-year in July 2022. Construction equipment, heavy commercial vehicles and used commercial vehicles all reported double digit growth. Finance to SMEs increased by 13% over the same period.

Software underpins this success. It enables asset financers to hone in on the right opportunities for their clients. But it comes with risks. Powerful new platforms may boost efficiency and cut costs, but getting them online means grappling with legacy systems and disrupting data flows. Moreover, integrations between systems and the data they process must be futureproof; rebuilds should be done with tomorrow’s asset finance landscape in mind. Otherwise, why take the trouble?

Navigating this tricky task necessitates buy-in from multiple teams and a streamlined workflow. Failure to tick these boxes means every integration necessary to keep the finance flowing could become a potential stumbling block. Asset finance software provider Alfa tackles this topic in their latest whitepaper.

So, you’ve decided to upgrade your asset finance software system. What are the key things to bear in mind?

Different approaches have different costs and benefits. Comprehensive software replacement means uprooting legions of legacy systems all at once, risking repetition of work and data losses during the rush to get them back online again. A phased approach – where multiple systems run simultaneously while a new system gradually takes over from the old – takes greater consideration to implement, but permits more opportunity for paving over pitfalls.

Whatever approach they choose, project managers must prioritise. Confronted with myriad software integrations, putting the most valuable and scalable at the top of the re-implementation list is essential. Some may be especially complex; others may be more important for individual products and regions. A cost-benefit analysis of each integration from the outset can help. In a truly agile system transformation, those most fundamental for getting the entire integration landscape up and running will receive the most emphasis. Not everything can – or should – be done at once.

The advantage of an integration-by-integration transformation is that it allows project managers to work out what value can be added in each project phase. For example, in the midst of a multi-country rollout, focussing on one country to begin with allows the most profitable integrations to be trialled and tested while others are deferred. But adding immediate value is not the only consideration. If existing available functionality provides an interim solution before wider transformation takes place, this can keep customers happy – smoothing over software transitions, minimising downtime and guaranteeing every facet of the end-solution fits together neatly.

Some decision makers balk at such a solution, known as the minimum viable product (MVP) approach. Interim fixes can be frustrating for senior managers who want to get the show back on the road. Providing they utilise a tight scope, MVP solutions need not hinder this goal. They re-establish fundamental features for a handful of customers while further functionality is refined. By the time a wider portfolio rollout takes place, business teams are confident of the software they are using and tech teams are experienced in maintaining it.

Interweaving business and technical teams is another crucial ingredient in transformations. Although a technical undertaking, every software transformation involves transfers of business data. Marrying up priorities – disciplined timelines and objectives from strategists, alongside innovative and streamlined coding from tech specialists – draws out the best of both, leading to the most efficient rollout in the long-run.

Above all, teams must get to grips with the data they are dealing with. Understanding legacy data – where it comes from, how it is processed and the way it is used – is a prerequisite for any project. Doing so will make designing the new data journey significantly more straightforward.