Posted: 08 Oct. 2020 05 min. read

Supporting small business customers in an uncertain world

The COVID-19 pandemic has extended from a health crisis into one of the most severe economic crises in Australia’s history. With the ongoing lockdown of international travel and the second-wave in Victoria, we have seen this crisis drive significant financial stress and uncertainty for small businesses in particular. For organisations that support small businesses with financial products, understanding the current and future financial needs and capabilities of these customers is more difficult than ever.

In stable or predictable times, organisations such as credit lenders rely on a range of data-sets to build models and derive measures, metrics and views that support decision making. However, when decisions are made on the premise that the past is a reasonable representation of future trends, even the most comprehensive stress testing regime will be challenged. Organisations can revert to in-depth banker conversations with customers with a focus on future business planning – though these take time and still rely on a banker’s ability to predict the future.

So in an environment where customers aren’t confident of their own financial future, what can you as a lender do to maximise your understanding of customers to appropriately support their needs? How can you understand your portfolios with sufficient depth to make sensible portfolio-level decisions and effectively manage the shape of your business for the future? 

The following four dimensions can be useful in responding to these challenges:

  1. Ask different questions

    Challenge yourselves on the questions you’re asking and ensure the most important ones are prioritised for managing customers now and moving forward. There will be many perspectives on what’s important, so regular prioritisation with key stakeholders based on key objectives (immediate and long term) will be imperative.

    In understanding small business customers, there may be value in exploring external dimensions that influence your customers’ businesses. If data on a customer is limited, how can you fill in the gaps?  For example, in predicting revenue recovery, you could consider the recovery of demand for the services that your customers provide, and the forecasted health of your customer’s suppliers as overlays.

    Recovering and thriving in the current environment is demonstrated by great examples of resilience and creativity by small businesses to pivot their services. In addition to predicting the metrics of risk, revenue and capacity to manage debt, should business ingenuity, business flexibility and strength of management also be considered in identifying appropriate support options for customers?

  2. Look at different data

    Review the data that you have available, the data that is currently being used effectively and whether more value-add information can be sourced. Are there blind spots in your understanding of your customer base? Is there information within your organisation that you haven’t considered before to support the questions you now have? How could macro-economic observations and predictions supplement the data you use?

    Are there opportunities to extend the use of bureau data and shared data from other lenders and aggregators, to facilitate a broadening of your view of the customers’ financial position? Increasing the regularity of data sourcing drives a more dynamic view of customers and enables you to be responsive to change.

    Being able to easily capture, store and use what you learn directly from your customers will provide inputs as well as valuable feedback mechanisms for analytical developments. Social media data may also provide an additional dimension to predicting future financial resilience. What can web presence, on-line reviews and social media engagement and sentiment tell you about your customers?

    With the emergence of new technologies, data is becoming a lot more accessible. Are you taking advantage of this? 

  3. Look at data differently

    Identify if there is an opportunity to challenge not just the data you use, but also the way you use it. Be creative in what you predict, in how you combine and test your predictions and in how you establish a rhythm of continually learning and optimising decisions.

    In the current environment and as we move through the establishment of a new “normal”, evaluate the basis on which individual decisions are made. This may include adjusting definitions and thresholds that drive different treatments on a regular basis, to reflect the change in “normal”.

    For the questions identified as the highest priority, consider new ways of combining information to find answers. Create new features, explore different outcome definitions and use different techniques to support the types of data you’re using and the analytical objectives.

  4. Offer different solutions

    Challenge your organisation on the types of support you can provide small business customers. Whilst a payment deferral, further credit or restructuring debt will remain appropriate options in some cases, are there other products (existing or new) that make sense for the future of the business? Are there additional concessions or alternative payment arrangements which will provide customers with the flexibility they need while not unreasonably increasing the risk to the organisation? Non-financial support such as business advice or business coaching may be an appropriate alternative support offer also.
     

In providing support to small businesses in the current uncertain environment, creativity and agility across analytics and decision making, and supporting this with dynamic monitoring to respond rapidly to change will be key moving forward.

 

More about our Author

Liz Buzzard

Liz Buzzard

Director, Risk Advisory

Liz has worked in the credit risk and analytics industry for over 20 years, supporting clients in Australia and across Asia Pacific. She is experienced working with organisations to help solve their risk management challenges and implement smart strategies supported by data and experience – with particular focus on data driven strategy design, analytical modelling, decision and process automation and creating monitoring and strategy optimization frameworks. Her key motivation is to provide valuable risk management insight to lenders who seek to effectively balance the needs of their customers with those of their business.