Resource KT
Related Articles
Business credit continues to be strongly linked to vintage and past credit history. In a world disrupted by the pandemic, this is no longer enough since past models are no longer predictive of future performance. In fact, what matters most is behavior now.
Before Covid (BC) and After Covid (AC)
Commercial loans have been relationship-driven since the early days. Regulatory mandates on information sharing as part of due diligence have indeed brought order to decision making. However, as with interpersonal relationships, it takes time and effort to build trust and access unregulated information that is also crucial for lenders to make more precise decisions about who to lend and how much. Although credit bureaus have helped fill the information gap over the past two decades, they have only increased the reliance of lending decisions on the particular slice of information arising from the history. credit.
Unfortunately, for borrowing from the mutual fund industry, past credit performance is not predictive of future credit performance. Add a pandemic of such epic proportions that the passage of time is itself split into the “Before Covid” (BC) and “After Covid” (AC) eras (witness to Kotak Mahindra Bank’s annual report FY20). What we get is the scenario where financial institutions end up with credit decision models that have little value in the AC era since all of their accumulated knowledge is from the BC era. Plus, given that the AC era has only emerged recently, there isn’t much proper credit history to build on again.
New models
As businesses begin to move away from the more traditional principles and practices of collecting information on customer behavior. In this post-pandemic AC era, the need of the hour is for new credit decision and risk assessment models that incorporate an understanding of a client’s business that goes beyond their use of facilities. previous credit.
Stressors have varied enormously from sector to sector for a multitude of reasons. For example, the travel and logistics sector is hammered due to a plummeting customer base. The MSME sector as a whole has suffered from declining spending and poor customer recovery from the British Columbia era.
Business is no longer “business as usual” as expansion plans have been curtailed, reducing the associated need for new credit and postponement of investment projects. The emphasis placed on cost containment highlighted actions relating to the rationalization of the use of working capital. The extent of such a change also varies from industry to industry. Such a business climate favors certain types of credit products which result in an accumulation of specific risks.
Supply chains have been disrupted due not only to the pandemic, but also to global trade policies and regional geopolitical developments. Business entities that benefit greatly from cross-border trade, whether for the import of raw materials or the export of finished products / services, are strongly impacted. For example, beauty and wellness salons use a number of Chinese equipment and are primarily dependent on footfall, both of which were heavily impacted last year.
Behavioral intelligence
It is becoming increasingly relevant for companies to use advanced tools capable of providing more intuitive and tangible information about customers. The Need of the Hour is holistic, present-day business information that supports decision-making throughout the credit lifecycle. In fact, this information should encompass the various aspects of the business which, taken together, reflect the health of the operation of the business. Just as an individual’s behavior can be evaluated by physicians for symptoms of underlying discomfort, trading behavior provides crucial information.
The information generated from behaviors is called behavioral intelligence. Companies behave differently in different forums, but present useful models for risk assessment. For example, a business that has frequent disputes with the law, as evidenced by the number of completed or pending cases, may be viewed as a riskier proposition than a business that has had a low number of cases.
Compliance behaviors provide a number of fairly precise indicators of risk. Documents filed by companies with MCA and ROC reveal structural information that is needed in such behavioral models, but unfortunately this information is not diligently digitized in an easily machine-readable manner. Artificial intelligence (AI) techniques can help extract usable information even from notoriously difficult sources.
Mandatory declarations to tax authorities (eg GST) and trade unions (eg Employee Provident Fund) provide useful information about the health of a business. For example, frequent delays in TDS deposits could lead to liquidity issues, relative to prompt and timely payments.
Importantly, lending institutions such as banks that also provide commercial operating income are sitting on a treasure trove of behavioral data. Basic banking systems have been recording transactions for many years. By tapping into such transactions, modern AI algorithms can uncover behavioral insights capable of systematically building a true picture of a customer’s business. By then comparing activity between the BC and AC eras, new stressors can be quickly highlighted, resulting in both timely risk alerts and emerging opportunities for new business.
In summary, lenders should look beyond the credit history when making credit decisions. Business behaviors need to be understood holistically, especially in this post-pandemic era. The good news is that lenders already have the data they need internally, with external data increasing wealth when needed. Technologies have also rapidly evolved to accelerate this new era of decision-making models. It remains for the leaders of credit institutions to change their mindset and adopt such an innovation.
The author is Founder and CEO, TheDataTeam
