While some functions in financial institutions may get through the pandemic and return to something approaching normal, this cannot happen in debt collection and the mortgage sector in particular is going to face some long-term challenges.
Recently released data from UK Finance forecasts that UK mortgage arrears and repossessions will surge after the payment holiday ends on 1 April 2021.
UK Finance forecasts suggest the UK could see home repossessions rise from 2,900 last year to 22,300 by 2022. The industry body also believes mortgage arrears will rise to 142,200 in 2021 from 81,300 in 2020.
Considering all this, I firmly believe debt collection needs a serious overhaul. It lags behind other functions in terms of both investment in new technology and improved practices, making the current crisis nearly unmanageable for many institutions.
However, this is not a question of having to come up with new ideas. The current industry leaders employ four best practices that could be adopted by all organisations and implemented as standard practice. And these techniques will support institutions as they find a way through the COVID pandemic.
- Identify the most vulnerable customers
It is essential to dig deep to capture the vital data that separates economic victims from “regular” collections customers from vulnerable customers.
This data should describe:
- How reliant on credit the customer is — card utilisation, renewal of UPLs
- A customer’s financial morality — transactor not revolver, direct debit payer, early settlements of UPLs, takes advantage of interest-free offers, low LTV, lower than average balloon payment in terms, number of credit lines, changes in card transaction spend type and velocity
- Credit stress — number of new applications for credit internally and at bureau, frequency and degree of overdraft facility
- Financial stability — industry, occupation, earning potential, stability (number of address & telephone number changes)
This data is all available – be it from a call, collections or communications systems – and informs how to treat customers.
- Assess Affordability
It is important to dig even deeper into household balance sheets and cash flow. Where possible, use Open Banking data to determine accurate affordability assessments.
Credit bureau data and other behavioural indicators will signpost trouble with current debt, as will the use of forward-looking analytics to detect the effect of incremental debt on default risk.
- Move to Omnichannel, Responsive Communications
The best customer engagement strategies are led by the customers – so the question becomes: how open can you make your systems to your customers?
Research we conducted between lockdowns in 2020 found that nearly a quarter of mortgage customers found it difficult or not easy to make contact with their lender at the start of the pandemic.
Being able to talk to their lender when facing financial pressures is fundamental to a good customer relationship and can certainly play a crucial role in mitigating some of the long-term challenges in collections.
The ideal approach is omnichannel. Organisations need to be able to change their contact strategies quickly to enable customers to auto-resolve on omnichannel platforms.
These include auto voice, iSMS/WhatsApp, digital direct API, email, mobile app, online or through edocs. It only becomes true omnichannel when customers can leverage all and any channels to self-serve or engage with their creditor 24/7/365.
- Add Science to Segmentation
Segmentation is a vital dynamic process, and too much of it is done “by hand” today, using very few criteria that create large and crudely defined segments.
Decision science teams need to provide regular insight as to what they are seeing and how these learnings should influence customer treatment decisions.
The example segmentation below shows that, as the phases progress, the segments are based less on risk than on identifying those borrowers who can handle their debt, those who need assistance or active advice and attention, and those that need to be monitored periodically.
Developing a complex strategy like this requires prescriptive analytics or mathematical optimisation.
Mathematical optimisation has been available to debt collectors for a long time but rarely used. It is used much more frequently in lending and customer management.
This is the most sophisticated analytics available to determine treatment strategies and customer-specific actions that will best meet portfolio objectives given all the data available and the constraints.
Optimisation in collections can be applied across the different stages of the C&R lifecycle.
In early-stage collections, it is about optimising which channel, channel timing and channel messaging should be most suitable to which account to achieve the objectives
In late-stage collections, the focus is more on the identification of customers that require related restructure / settlement
Post charge-off, the objective is to maximize recoveries by determining which accounts to work, which to place and which to sell
In third-party placement, optimisation can be used to determine which accounts to best place to which agency in order to maximize recoveries, based on their performance with different types of debt.
Optimisation can also determine which accounts are best to be worked internally vs accounts that should be placed or sold.
The saying goes – “necessity is the mother of invention”. The difference, however, for collections and recovery is that inventions have been around for some time and are proven in various markets.
For those working in collections in the mortgage sector, necessity is the mother of adoption.
Bruce Curry is a vice president for collections and recovery consulting and sales at FICO
