As COVID eases out in light of vaccinations, the societal and economic impact rages worldwide. Customer behavior has changed permanently and risk leaders are grappling with the implications for existing credit models. Since COVID was a unique black swan event, current credit models are often not as predictive as expected. Consequently, many financial organizations have either altered their existing models to fit the new reality or completely replaced them with something new in hope that it tackles all the challenges put up by the pandemic.
Fico scores still on the rise.
However, altering the existing model or replacing it completely to meet the new normal is not a feasible option because of historical data. Scenarios like COVID are different from typical recession cycles etc. Hence, you cannot apply cyclical trends to adjust the risk models or build a new one to capture the covid impact. In the absence of this, the simple exercise that banks often tend to do is reduce the risk appetite further and adopt a more conservative approach, which works from a risk management perspective but becomes counterproductive from a Growth perspective. If we recall the recent past where borrowers had the option to go for payment holiday; while credit bureau has directed banks not to report payment dues on customers underpayment holiday, it took some time for banks to cater to this requirement and hence bureau report for certain months were unreliable from credit decisioning perspective.
Furthermore, Risk managers typically chose the conservative approach. They mainly considered everyone who availed payment holiday as "Riskier" and hence wasn't comfortable extending benefits such as cross-selling another product, limit increase, etc. However, this is not always true for multiple reasons, therefore, hamper the possible growth opportunity.
Fewer consumers have subprime credit scores
Hence, with the advent of the COVID-19 pandemic, banks and financial institutions worldwide need to look for more innovative ways to reform their credit decision process. The process begins with building a statistical model and measuring the model's performance over time to assess its viability. Over the past few decades, analysts from top financial institutions developed logistic regression models. They have become the benchmark for the model creation of a robust credit scoring system. Recently, groundbreaking technologies like Artificial Intelligence and Machine Learning techniques have emerged as sophisticated solutions for creating credit scoring models. Let's see how AI-based credit scoring differs from traditional credit scoring methods and its vital benefits to the industry.
How AI tackles credit risks better than existing technologies?
Updating real-time data and allowing for early interventions to consumers.
With the correct data in place, AI models help them in reducing credit losses by detecting delinquencies as early as a year
AI models can ingest extensive external data such as Macroeconomic data produced by FRED etc. and hence allows the model to react as per the changes in the external environment.
AI also helps leverage customer data across the organizations that improve the model's overall prediction accuracy in real-time, easier detection of potential frauds, and reduction of false transaction positives.
Traditional Credit Scoring vs. AI-based Credit Scoring
AI-based credit scoring models offer various advantages when compared to traditional credit scoring models. The most significant advantage of an AI-based credit scoring system is that it can help organizations determine the hidden accords between different aspects of an individual's data that are not always visible to the traditional models that look at a single variable at a time.
Another main benefit of AI-based credit scoring is that it makes the analysis process more predictive than the traditional system that works on assumptions and historical data. Rather than this, AI-powered models analyze data, learn from changes in data and observed patterns, make improvements, and make predictions to a scale of detail that is practically impossible to observe in a traditional scoring model.
One of the main issues with standard or traditional scoring models is that they become obsolete with the change in data trends, and in that case, it needs to be updated by the experts. But with AI-based scoring models, redundant cost and time in making changes to the model are removed as the AI models are dynamic and update themselves and are easily retrained with changing data.
Issues With The Current Credit Scoring Systems
Logistic regression analysis and other traditional credit scoring models have helped out financial institutions for decades. However, these methods are becoming obsolete, and institutions are looking for more advanced and sophisticated ways to build a credit scoring model. The issues related to these credit scoring methodologies became more apparent during the Covid-19 pandemic. Here are some of the demerits of traditional credit decision models.
Although credit scoring models rank the riskiness of individual people but cannot assess the comparative risks of different borrowers.
Older methods don't always factor in the psychological aspects and spending habits of the borrower.
Another drawback of traditional credit scoring methods is that they aren't factorized with the changing economic conditions and do not adjust without a change in the borrowers' financial position.
Finally, these traditional credit scoring methods require much more time and effort for effective decision-making. It can lead to the degradation of the brand image of the lender and the borrower's creditworthiness.
How can AI help?
Historically applicants for credit score for various products, such as mortgages and auto loans, have used a lot of credit history. This approach based on a limited set of historical data limits the number of consumers eligible for credit. With AI, models are created to predict a consumer's credit potential by obtaining more comprehensive and complete information through generally more important unstructured data, such as current financial health, education, future employability, macroeconomic data, and projected earnings.
Conclusion
AI models are inherently predictive in nature vis-à-vis regression models. It enables multivariate analysis of data to give a better risk score than a univariate regression model. AI models would allow models to blend through the cycle and point in time, making predicting risks better.
AI models are no longer a black box and are fully explainable, compliant to FCRA & ECOA regulations. They are easy to deploy can the test-learn-deploy cycle is now down to few weeks. Lending institutions must pilot this technology in parallel to their existing process to ascertain the value proposition.