This course is of interest for finance professionals in banking, leasing, factoring, consumer and small business lending, fintech, telecoms and health care. The content is particularly relevant for credit managers, risk analysts, business process engineers, IT specialists, auditors, consultants and many others interested in predictive statistical modelling.
Today, big data and scoring are driving real-time business decisions everywhere, not only in finance. The sample data sets and practical case studies in this course will focus on credit risk assessments in granular consumer and micro/small business portfolios. The immediate objective is to build better application and behavioral credit scoring models that can support or automate credit granting decisions. However, once you start analyzing credit data sets and working through the scoring process, the versatility of the methodology becomes immediately apparent: instead of predicting default behavior, one can just as easily score the probability of fraud, predict the client’s next purchase, target low risk drivers for special car insurance offers etc. The only pre-requisite is a university-level analytical perspective and the willingness to engage with fundamental statistical concepts. Knowledge of R, SPSS, or other statistics packages is helpful, but not required. We will demonstrate all calculations using standard Excel functionality and inexpensive add-ins such as XLStat.com or open source tools such as Jamovi.org.
Everything we offer in our range of open seminars can be packaged and delivered as tailormade in-house training programmes for companies and organisations. We will be happy to advise you and create an individual offer on request.