Credit Risk Assessment Market Size and Trends

The credit risk assessment market size is expected to reach US$ 18.43 billion by 2030, from US$ 7.31 billion in 2023, at a CAGR of 14.1% during the forecast period.

Credit risk assessment involves analyzing credit data and financial statements to determine the level of risk associated with lending money to a particular entity. It helps lenders and financial institutions analyze creditworthiness, predict probabilities of default, and make informed lending decisions.

The key growth drivers of the market include the rising need for making real-time business decisions, increasing complexities in business processes, growing data and security breaches, and stringent industry regulations.

The credit risk assessment market is segmented by component, deployment model, organization size, vertical, technology, and region. By component, the market is segmented into software and services. The software segment accounts for the largest market share as AI and ML-enabled software solutions are gaining rapid traction for credit risk analysis. Software solutions enhance risk management capabilities and provide predictive insights

Figure 1. Global Credit Risk Assessment Market Share (%), by Region, 2023

Credit Risk Assessment Market Trends

Integration with automated decisioning platforms: Financial institutions are increasingly integrating credit risk assessment capabilities with automated decisioning platforms that allow straight-through processing for accelerated credit underwriting. This enables dynamic risk analysis and real-time decisions on credit approvals, pre-approvals, limit settings, etc. Solutions providers are partnering with decisioning platforms and leveraging technologies like AI/ML and APIs to deliver seamless integration. According to OECD, In 2022, A leading financial institution integrated an automated decisioning platform into their loan approval process. This resulted in a 50% reduction in decision time and a 20% increase in loan approvals.
Hybrid and ensemble modeling: Driven by the need for greater predictive accuracy, the adoption of a hybrid modelling approach combining statistical techniques, machine learning, and deep learning is rising. Ensemble modeling utilizing the output of multiple models is also gaining prominence as it enhances predictive power and minimizes bias or overfitting compared to individual models. Providers are incorporating hybrid and ensemble modeling to strengthen risk assessment capabilities. According to the World Bank, In 2022, the adoption of alternative data and advanced analytics pushed financial inclusion to near universal levels in Brazil between 2011-2017, with the number of borrowers growing by over 50% during this period.
Specialized solutions for evolving segments: Rising alternative lending segments like Buy Now Pay Later (BNPL) and supply chain finance are demanding tailored risk assessment capabilities specific to their needs. Solutions catering to specialized segments and unconventional data sources are gaining prominence. Customized platforms for small business lending and commercial credit evaluation are also witnessing increasing adoption. According to a 2022 report by the World Bank, the use of new alternative data and more dynamic modeling helped increase unique credit approvals in developing markets by 15% compared to traditional methods that relied mostly on credit scores and payment histories.
Integration of non-traditional data: Analyzing non-financial alternative data derived from sources like social media, web, IoT, supply chains, etc. is gaining traction as it provides unique behavioral insights into creditworthiness uncaptured by traditional data. Advanced analytics applied to these new datasets allow a more holistic evaluation of risk. Solutions enabling the integration of non-traditional data see rapid adoption across lenders focused on financial inclusion.