Credit companies are facing an increasingly volatile global financial climate. A person has to look no further than the impact the unexpected Brexit results have had on the global market. And if that’s not enough, the highly accelerated pace of technological development means that companies need to always be prepared to update their processes and methodologies to accommodate ever-changing client needs and to mitigate risk.
With this in mind, here are three ways to manage credit risk governance in today’s highly unpredictable business climate:
1) Pay attention to both the economic and political climates
No organisation exists within a vacuum. This is especially the case for companies that have a loan scheme or credit options for their clients, as they are more vulnerable to market variables like currency strength, commodity prices and overall economic climate. Managing credit risk governance effectively, therefore means having a clear understanding of both local and international economic and political events and trends.
This understanding should ideally extend to a pragmatic conclusion, including:
- Defining possible scenarios and the associated likelihood of each scenario.
- The impact each scenario could have on your business and its credit performance metrics, cost metrics and growth and revenue metrics. The impact each scenario could have should be analytically identified and defined at the lowest possible performance indicator (e.g. the flow into collections or the impact on debt sale prices) rather than top-line metrics (net loss rates or provision coverage). The top-line metrics would be a modelled forecasting output based on overriding the performance metrics.
- After understanding the performance impacts by risk grade agree on the alignment of strategies to ensure ongoing compliance to the defined risk appetite and profit hurdles.
A tumultuous economy increases credit risk and the likelihood of defaults – being aware gives you time to prepare through measures like alignment of credit strategies and policies, careful cash flow management, diversity of funding and contingency plans.
2) Maintain effective internal policies and organisational structure
Another key way to manage credit risk governance in an unpredictable world is to have internal infrastructure in place that’s able to handle the unexpected and to weather suboptimal credit conditions.
The board should have clear risk appetites and profit hurdles that are effectively communicated and implemented, but that are also flexible enough to adapt should the need arise.
It is important to have these clearly defined not only for the overall portfolio, but also for new business vintages and the marginal accepts (highest risk accept population). These parameters should then be sufficiently conveyed through the company policy in a way that is not ambiguous or leaves room for erroneous interpretation. Clearly defined organisational roles and responsibilities make it far easier to manage credit risk governance while simultaneously reacting and adapting to an ever-changing business environment.
These governance foundations would include:
- A credit risk management committee with strong attendance by the wider business;
- A full suite of reporting across the credit lifecycle that goes beyond credit performance and includes complaints, reputational risk, extremes, overrides, exception reporting and operational performance;
- Regular reviews of a comprehensive credit policy document that is supported by a detailed procedure document;
- Daily and weekly operations and performance reporting with intra month outlooks avoiding month-end surprises;
- Scorecards monitoring across the credit lifecycle at least quarterly;
- Strategy performance that is tracked against a control, champion and other challenger strategies;
- A change and event log that is maintained and trusted;
- Provision calculation is understood by all rather than a selected few;
- Credit staff who understand their roles and responsibilities with measurable KPI allowing real-time performance feedback and are handsomely rewarded for top performance (cascaded from the overall business plan);
- Defined and monitored regulatory and reputational risk;
- Quality controls;
- Costs tracked to activity level; and
- Profit models that are standardised and that underpin all credit policy decisions.
3) Boost your credit risk governance response time with advance data analytics
Lastly, it’s important to make use of available data analytical tools and skills to boost your company’s durability and limit exposure. Machine learning is a great example, as it allows credit risk managers to use algorithms that take a vast number of variables into account to minimise risk exposure for a client, while continuously adapting to changing performance trends potentially caused by external variables like economic and political factors. Machine learning involves using pattern recognition, optimisation of variables and continuous learning to achieve the best results for a client - all while working within the limitations of your risk appetite and credit policy framework.
Machine learning and predictive analytics allow credit managers to maximise profitability while minimizing risk exposure. Furthermore, with the vast increase of mobile devices and client information available, an approach like machine learning has a far wider and deeper data pool to draw insights from – leading to a more accurate model and better results.
Contact Principa to find out how predictive analytics and machine learning can give better insights into your credit management strategies and mitigate risks in a highly volatile climate.
Image credit: http://www.valis-it.com/
About the Author, Edwin Cross
Prior to joining Principa in December 2013, Edwin had 13 years’ consumer credit (credit cards, revolving credit and personal loans) experience in Barclays Bank and Standard Bank SA with 10 years’ specialised in credit risk management across South Africa and Europe in particular United Kingdom, Spain, Germany and Italy of which over 3 years with Chief Risk Officer accountabilities. Edwin is considered an expert across the full customer life cycle and has lead and implemented a number of highly successful credit, marketing, collections, scoring and provisioning initiatives. Since joining Principa, Edwin continued to successfully deliver risk-based and profit-based initiatives to key clients (specialised lenders, large retail banks and retailers). He holds a M.Comm degree from Stellenbosch University.