As the world faces its biggest economic crisis since 2008, financial organizations are being tested for their ability to help customers manage their debts. On one hand, consumers affected by the massive economic disruption find their ability to make payments jeopardized, while on the other, contact centres face under-resourcing as a result of the pandemic. The challenges for call centres have also been further magnified by a rapid spike in call volumes, high stress levels for both employees and customers, and the lack of digital channels in place to better manage customer communication.
The Covid-19 crisis has exposed the lack of digital capability and automation in organizations, with many – in haste – trying to change inbound Interactive Voice Responses (IVR) to alleviate demand, stopping all outbound collections activity, and handling responses to government relief measures. In Singapore, for example, UOB received more than 2,000 applications to defer mortgage repayments in the span of just five days after the Monetary Authority of Singapore announced its relief measures.
This confluence of challenges has prompted an urgent question for any organization that deals with receivables management: how can banks build more resiliency into collections and recovery operations?
Prior to Covid-19, many banks were already looking into predictive analytics technology like artificial intelligence to improve their collections yield and help increase the bottom-line in an effort to refine their collections strategy. According to a FICO survey, two-thirds of banks that employed automated collections to contact customers have indicated that it leads to faster bill payment. SPDB Credit Card Centre, for example, was also able to reduce its collection business staff headcount by 30% after deploying analytics technology, significantly cutting operating, management and risk costs.
This trend will only continue to accelerate. Shifting away from the traditional way of agents calling customers for collections, the use of “machine calling” and analytics technologies can enable better management of collections, improve collection performance, and increase customer satisfaction even in a post-Covid-19 world. Banks and financial institutions that are ready to expand their use of predictive and prescriptive analytics for their collection functions should consider the following four reasons:
Lower the cost of collections. Predictive models can literally forecast which cases are most likely to pay, making cases assigned to collections staff more productive. Where the models predict the cases will be less productive, they can be sent to a collection agency or given alternative, lower-cost treatments. At the same time, you can stop your collectors from calling on people who will self-cure.
Enhanced customer service. Predictive models can also help enhance customer service in several ways. They allow banks to give lower-risk customers more time to self-cure. Phone calls and letters to customers can also be less insistent, which will result in fewer complaints. Studies have shown that people who feel that they got better customer service are more likely to be compliant in the future. For commercial clients, happy customers typically continue to shop with the same company. By de-prioritizing these low-risk cases, collections staff can focus on cases that truly require their attention, optimizing resources and lowering the cost of collection.
Improve operations continually. Analytics can help improve operations by measuring the effect of individual changes to the collection approach. Using a feedback technique called “test and learn”, teams can experiment and implement a customized approach that works best. For example, you can put 80% of cases on existing collection strategies, 10% of cases on one alternate strategy and 10% of cases on another alternate strategy, then measure the impact of the changes from each strategy. This approach can also be applied to compare different timings for contacts, letter messaging, call campaigns, payment agreements, etc. Virtually any change in strategy can be compared accurately with data, rather than through mere hypothesis. This will allow continual improvement of operations and implementation of hybrid approaches where different debtors receive customized treatments that produce maximum results for each debtor pool.
Optimize overall performance. Mathematical optimization, also known as prescriptive analytics, takes predictive analytics a step further by looking across an entire business process to find the strategy that will result in the highest level of overall performance. The goals can be simple (maximize dollars collected) or complex (maximize dollars, while over-performing on a specific workload, and minimizing specific actions). The optimization algorithms can also account for staff, budget, legal and other constraints.
Prescriptive analytics also allow for the balancing of staff for each workload. The analytics could show opportunities if staff were moved between specific workloads, or specific activities on cases. It could show when to stop working a case, or where there are too many or too few resources working specific workloads. Prescriptive analytics, like predictive analytics, can also utilize a “learning loop” where the results of models are fed back into the model, allowing for continual automated tuning of models.
It is important to note that these analytics do not replace the expertise of collections staff, but rather, enhance the overall performance of banks and financial institutions. Data analytics can provide an objective, mathematical framework for making better decisions, helping businesses achieve a higher level of performance in the current period of uncertainty and beyond.
Delivering a truly seamless customer experience for those experiencing financial difficulties is the panacea for most collections’ operations. Now is the time for collections operations to rise above the noise and strike. There is unlikely to be a better opportunity to make a grab for investments in your collections operation. Pulling together a clear and compelling vision for collections will help teams accelerate their digital transitions during the remainder of 2020 and onwards.
Dan McConaghy is president of Asia-Pacific at FICO
