Assessing financial risk based on historical credit performance, is no longer the leading factor to underpin lending decision-making, with our modern and inclusive economy.
I know that traditional banking and finance professionals may not agree with that statement, especially if they have been accustomed to many years of using the management of credit as an indicator of a business or founders ability to repay loans.
But the reality is that the role of credit risk, as part of internal financial risk processes, has been severely disrupted. From the pandemic, to geo-political challenges, to the cost of living crisis, external impacts have affected the validity of historical data points and sources that make up credit risk models.
This now makes it more difficult for lenders to predict what type of businesses are the best propositions to lend or invest into, or divest from when using credit risk as the lead indicator – this, in turn, increases portfolio risk and the operational costs of servicing loan debt.
Furthermore, adapting to forced change can also increase unconscious bias within their own lending practices, making it even more difficult for underserved businesses to access the finance that they need to grow.
With the current UK SME funding gap estimated to be £22 billion, it is evident that something is not working.
I believe that there are three key challenges for banks and other non-banking finance providers to consider when it comes to the importance of credit risk based models:
Integrate data-driven ESG-based compliance practices
Like it or not, the proliferation of ESG is increasing at a steady pace. According to a recent report by Grant Thornton, 93% of lenders expected ESG related lending in the mid-market to increase in the next few years. This will trickle down the food chain to smaller SME businesses too.
The pressure for lenders to evidence their own contribution and commitment to society will require deeper insight and information, on how the deployment of finance to their customers is having an impact in a more sustainable way.
But there are many advantages for lenders to make ESG a natural part of their financial lending processes such as increased consumer trust and loyalty, enriched relationships with their respective stakeholders and enhanced reputational and brand benefits.
Having access to better intelligence to benchmark against ESG compliance within credit risk measures, can help finance providers improve the pace and accuracy of meeting their own and their customers’ ESG objectives.
Embrace diverse intelligence to make more informed lending decisions
Growing a business is hard and mistakes will be made, and this is not always taken into consideration within strict credit-risk based lending models. Coupled with the fact that every lender has their own lending models to suit their best-fit client profiles, it makes it harder for a business to know what individual lenders are looking for and therefore, who is worth applying to for support.
If financial lenders choose to integrate alternative data within risk management processes, and combine this with their own risk appetite models, they can access a much more richer and holistic view of the environment that the customer operates within (i.e. real-time insight on wider market and sector trends that may affect the business or unlocking new predictive insight on business performance) to make much more informed lending and business growth decisions.
Use external technologies focused on reducing overall bad debt risk
The use of alternative data technologies can help finance providers feel more confident about the type of interventions and support that they can put in place for their customers, before bad debt incidents occur. Such technologies can make internal processes more efficient and create a smoother customer experience.
Reducing future risk and costs is essential for lenders to protect their already thin net interest margins on loans repaid. Using technologies to source powerful insight on the ongoing health of a business, internal portfolio trends, global economic trends, peer to peer benchmarking are just a few examples of ways in which lenders can leverage technologies to identify new data patterns, which can give predictive clues towards identifying future lending issues.
But let us not forget.
The financial crisis of 2008-2009, the financial impact of the 2020-2021 pandemic and the collapse and restructuring of various banking and financial institutions over the past couple of years should have taught us that, when it comes to the world of finance, some things may never change.
We have also seen how additional finance backing from the government can support those who they cannot afford to see fail. You may have your own opinion on that, but history does tell us that this pattern is often repeated because the current financial system still adopts a traditional approach at a systemic level, which may not always reflect the reality of businesses on the ground.
The sad irony of this is that whilst many organisations will continue to survive and even grow from this support, the cold reality is that the sheer scale of need is ever-growing and not every business that seeks access to finance will turn out to be a good lending decision.
Commercial success for lenders is just as important as social impact, and they must always feel that this is in balance.
Banks and other lenders will still have to manage the financial risk and ongoing operational costs of servicing this need, in perpetuity.
The ability to manage these needs through the power of data intelligence, sits at the heart of what we offer lenders at the GFA Exchange.
I truly believe that digital disruption gives finance providers the golden opportunity to manage the impact of these changing needs and evolve their own organisations, so they can loosen the shackles of the past and evolve themselves to not only improve their own needs, but more importantly, unlock the latent potential within diverse businesses and society that we are all here to serve.