data-analytics

In the field of credit risk management, few would challenge data’s role in financial forecasting, lender analysis, credit-modelling and risk aversion. In short, credit risk managers are no strangers to data. But it could be argued that the value and volume of data they have access to largely determine the quality of their decision making. The shift towards more technology and data-centric business models has created new opportunities for those in the credit risk landscape to play more collaborative roles and engage other business divisions to produce positive outcomes in shorter time-spans.

With this said, let’s take a look at how data analytics brings credit risk management practices into sharper focus, resulting in more profitable decisions, reduced risk and predictable outcomes.

In a world of risk, data is king

Seasoned credit risk managers do well to err on the side of caution if the metrics don’t support the hypothesis. However, an overly conservative culture can lead to missed opportunities for new revenue streams. This is why data analytics is key to giving deeper insights into customers’ risk profiles and support a number of key decisions along the credit lifecycle (origination, account management, collections and recoveries). When data insights are applied to key decisions at each phase of the credit lifecycle, such as credit approvals, loan amounts, interest rates, propensities to roll and pay, risk managers are able to make more profitable decisions that enhance returns while minimising risk.

Credit risk managers should leverage data democratisation for better decision-making

Data centralisation has spawned a culture of information-sharing across departments, resulting in fewer silos within organisations. New collaborations give credit risk managers the benefit of data democratisation to empower their decision-making processes even further. Often drawing from sales, legal, marketing and other sources, credit departments historically found themselves going from pillar to post to build some context around their customers. Thanks to convergence and the augmentation of internal data sources with external credit, transactional, income and other data, credit risk practises are gaining deeper visibility into who their customers (both performing and non-performing) are and the likely credit risk outcomes emanating from discrete risk profiles. For example, data analytics reveal much about payment behaviour, income fluctuation and various lifecycle stages that people - or businesses – experience, thus allowing credit risk practitioners to mitigate risk for customer and credit provider alike.

Data analytics redefines what credit risk managers perceive as credit risk

Data analytics eliminates the “gut feeling / anecdotal” approach to credit risk practices and replaces it with actionable metrics that allow credit risk practitioners to spot credit risks and opportunities wherever it may exist. This visibility results in greater context that in turn improves the decision-making process - making it easier for companies to segment their customers on a more granular level. With 51% of South African adults holding an account at a financial institution, the opportunities to enhance insights across originations, account management, collections and recoveries environment have never been greater. It is, however, a question of finding the most suitable credit risk enhancement and mitigation techniques. And that is the role of data analytics: creating new credit risk insights and balancing effort and outcome to optimise portfolio yields.

Image credit: http://ar-management.tmcnet.com/