The hidden function and its importance in the digital era.
8 Nov, 2018
With growth in the number of people connected online and the…
Much has been written about data-driven strategies and decision-making based on scientific strategies, research and analysis, rather than on opinions or anecdotal evidence.
Whilst big leaps in data-driven strategic planning have been made in many areas such as marketing and IT, Credit Management is still lagging. This is especially so in businesses outside of larger banking, finance and utility sectors.
Larger credit providers, specifically those where lending money is their business – such as banks and financiers – approach this scientifically, with complex credit policies including business rules and multiple scorecards covering things such as behaviour, fraud, risk and pricing.
All these strategies revolve around data, with some serious number crunching going on. In simple terms, whichever statistical technique is used, it is critical that there is a large sample of previous customers with their application details, behavioural information and patterns and outcome performance information.
Scorecards are built to replace subjective human judgement with objective statistical models, swapping out unmeasured and tested anecdotal human decision making with data-driven, consistent decisions. What they do is essentially no different from what a credit assessor would do, they just do it in a more objective, consistent and repeatable way. Scorecards generally predict an outcome, be that the probability of default or probability of attrition/retention.
For businesses that currently don’t utilise these techniques, there are often a few reasons why but some of the unavoidable ones often include; having no or not enough data to build a customised scorecard, a lack of the data analysis skills needed, and not having the right technology to utilise and get the benefits from scoring and automation.
Many bureaux both locally and internationally offer population or generic scorecards which consider all the anecdotal rules of most credit controllers whilst also including other predictive data elements and correctly correlating and weighting them to risk, providing a scientific approach to risk mitigation immediately. Bureaux are also generally happy to provide the accuracy and the score to risk relationship of their scores to allow finance and risk personnel to work out and set your risk cut-off score.
Utilising this approach, and a credit decisioning software solution such as Credisense, has been shown to reduce bad debt by on average, between 15-20% (Decision Intellect, 2009), meaning there are significant benefits to organisations that expand their data driven decision-making strategies to all components of their businesses. Having a digital credit decisioning and credit management solution also means organisations can collect all the needed data in a centralised system for building their own scorecards in the future. The Credisense solution also offers a simple build and testing scorecard functionality allowing organisations to implement cutting edge credit management.
Collecting, utilising and analysing data, and following a data driven credit decisioning strategy enables a multitude of new and profitable strategies, all measurable and able to be analysed further for improvement. After all, it’s all about data, dummy!
For further content and unique insights around productivity, data and risk, follow us on Linkedin and Facebook.