If you want to seek out the newest ideas in credit scoring — a field that advances more rapidly than many people may suspect — the best place is the annual Edinburgh Credit Scoring and Control conference (well, next to our own FICO World). At this year’s conference, I started thinking about what has changed in the world of credit scoring since my first visit to the conference almost 20 years ago. My phone has certainly gotten smarter in that time – so what is smarter within credit scoring?
With around 70 presentations, the key questions remain the same:
- What data is available and useful?
- How do I best gain intelligence from the data?
- How do I best action the intelligence?
- How do I comply with the ever-increasing regulations?
In terms of turning this data into intelligence, artificial intelligence (AI) and specifically machine learning algorithms are being investigated and used on an increased scale. With ever greater volume and variety of data coupled with vastly increased computer processing power, machine learning approaches to drive the value from the data are proving more and more useful.
This same processing power for the development of machine learning models is also helping with the ease of deployment of these types of models, which has been troublesome in the past in terms of both speed of deployment and speed of execution.
The evolution from predictive analytics (models that order by a single outcome of interest) to prescriptive analytics (also referred to as decision optimization, identifying the best action to take considering multiple outcome metrics or dimensions) is vastly improving the business outcomes of decisions across the credit lifecycle, from origination through to collection and recovery. Prescriptive analytics provides the ability to make better, more informed decisions by taking account of multiple (often conflicting) objectives — for example, increasing accept rates whilst controlling losses.
Since the economic crisis there is also greater focus on modelling stressed situations, and how these stresses impact both the likely performance of individual consumers as well as total portfolios. Predictive models help lenders comply with regulations such as Basel and IFRS 9. As compliance is gained and maintained, we are seeing these same models being used to drive business value through better insights and understanding of portfolios, acting as key inputs to both what-if scenario analysis and decision optimisation capabilities.
FICO data scientists and experts, all of whom blog here, presented no fewer than five sessions at Edinburgh this year on hot topics related to these areas I have described.
- Gerald Fahner
- David Binder
- Scott Zoldi (two sessions) -
- Richard Cowley and Bruce Curry
- Neel Williams and Ken Robinson (from Tesco Bank)
- Demand Modelling and Price Sensitivities - a Case Study (this session is not on the event website, but you can refer to the series of blog posts from Neel on this topic)