Early warning systems enables lenders to proactively identify those customers who have a predicted high risk of becoming seriously delinquent in the very near future. Given this insight, the organization is then able to engage with the customer and agree appropriate actions aimed at preventing the situation worsening.
Several factors are behind this recent increase in interest. Given the current economic uncertainty, credit providers are facing entirely new set of risks. Customers transitioning out of government led payment moratoria need to be closely monitored. What level of future payment certainty can be assumed? How stable is their income? Have they been in receipt of income subsidies paid by the state? Is this is masking the risk that the customer will not actually have a job to return to?
All these factors have an impact on the potential for future delinquency. Post-moratoria defaults will undoubtedly rise but organisation’s are struggling to identify which existing customers are most at risk. Foresight is more important than ever. However, predictive models that are based solely on historic credit data can no longer be relied upon and need to be recalibrated, re-tested and re-validated.
All of this is taking place against a backdrop of intense competitive pressure. Organisation’s need to ensure that they move before their competitors in bringing customer accounts up to date as the further down the queue, they find themselves, the greater the risk of default.
The need for new risk models
Our analytics teams are working with a number of organization’s in developing new credit and risk models including specific post-moratoria models. They are created using machine learning algorithms, or through traditional techniques – depending on the client’s requirements. The models take account of multiple variables including probability of default, indebtedness, affordability, the current and future value and, equally importantly, the strength of relationship with the customer. Models can then be used to design proactive customer management strategies covering variety of actions such as the amendment of credit limits, the offer and creation of payment plans or forbearance terms, or the initiation of proactive collections activity.
Streamlined model deployment
PowerCurve users can use Strategy Manager to accelerate the new model deployment by using the ACE framework. It enables analytical models to be directed imported into the decisioning environment. From there they support the associated process flows that apply across multiple channels including customer portal, AI Virtual Assistant (“AIVA”) and the organization’s own in-house execution channels. The value the ACE framework helps deliver in terms of speed, agility and reduced costs, is a further strength of PowerCurve Strategy Manager, and once again, this can be used to accelerate deployment of analytical models across the entire customer lifecycle.