Leveraging artificial intelligence and machine learning for next-generation credit risk assessment models

Credit risk assessment has traditionally relied on demographic, behavioral, and credit bureau data for scorecard development.Recent advancements in Artificial Intelligence and Machine Learning have diversified the available model development techniques and allowed the exploration of alternative data sources such as social media, data exhaust, mobile GPS tracking, and psychometric data to develop next-generation decision models. This paper reviews the traditional modeling techniques and discusses the emerging AI and ML techniques for risk modeling applications. Some key strategic considerations for transforming risk modeling practice are also discussed. These advances have made it possible to develop more personalized models that allow better measurement of risk differentials at a population and individual consumer level, which in turn allows funders to make better decisions on how to fund, when to fund, and how much to fund.As a result, it is possible to lower the overall cost of credit risk assessment while deepening both positive customer experiences and financial inclusion for previously neglected customer segments. In the end, next-generation risk models hold the potential to democratize mega datasets and responsible AI practices and create fairer systems for financial services consumers. As financial organizations look to upgrade their decision processes in orderto compete on faster response times and promotional offers that utilize customer-level risk differentials, a generational opportunity arises for tech-driven finance pioneers. Consumer-facing financial services players that make deeper investments into their lending technology stack stand to gain far greater first-mover advantages and may fundamentally reshape the consumer finance market through next-generation risk decisioning infrastructure. Next-generation risk decisioning has also been made possible through low-code and no-code tools that allow business decisionmakers direct access to AI-and ML-powered modeling techniques. These tools may power an exploratory analytics culture at financial companies