CREDIT RISK ASSESSMENT MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2023 - 2030)

Market Challenges And Opportunities

Credit Risk Assessment Market Drivers

• Increased application of big data and analytics: The growing adoption of big data analytics across lending and credit evaluation is a key factor supplementing the adoption of credit risk assessment solutions. Integration of alternative data from sources such as payments, social media, and e-commerce along with traditional credit data enables a deeper evaluation of creditworthiness. Furthermore, technologies like AI, ML, and NLP allow to analyze massive, complex data to build predictive models that can derive actionable insights to make informed lending decisions.
• As reported by the World Bank, in 2022, inclusion of alternative data led to 20-30% increase in approval rates for the credit invisible in Mexico during 2020-2021.
• High volumes of credit lending: The continuous growth in volumes of credit lending across consumer financing, small business loans, and mortgages is creating the need for banks and other lending institutions to deploy robust, scalable risk assessment frameworks. This is driving the demand for on-demand, flexible cloud-based solutions that allow to efficiently handle and analyze large, dynamic volumes of credit data for accurate evaluation of risk profiles. The deployment of such solutions helps achieve faster processing and underwriting of loans while preventing losses and maintaining portfolio quality.
• According to a report by the World Bank in 2022, overall debt levels of households globally had risen to nearly 34% of GDP, demonstrating huge growth in personal loans over the past 5 years alone.

Credit Risk Assessment Market Opportunities

• Cloud-based delivery models: The demand for on-demand, scalable cloud-based credit risk assessment solutions is rising rapidly. Cloud models allow faster deployment, flexible scaling, and regular upgrades and help improve operational efficiency. Financial institutions of all sizes are migrating risk management systems to the cloud to achieve enhanced performance and accessibility and expand credit access by reducing latency in decision-making. This provides significant expansion opportunities for cloud-native solutions. According to IDC, in 2022, the cloud services experienced a robust 22.9% year-over-year growth, with an even stronger revenue expansion of 28.8% observed in the foundational cloud services that underpin digital-first strategies.

• Expansion in developing economies: Developing economies offer untapped opportunities for growth owing to the large unbanked populations and underserved MSME sector with massive demand for formal credit. Financial inclusion initiatives in these economies are driving the need for agile, robust credit risk management platforms to serve consumers at scale. Furthermore, factors like the digitalization of lending and evolving regulatory policies are mandating improved risk control measures – creating substantial opportunities for the adoption of advanced credit assessment solutions. According to the Directorate General of Commercial Intelligence and Statistics, The proportion of MSME-specified products in India's total exports for the years 2020-21, 2021-22, and 2022-23 stood at 49.4%, 45.0%, and 43.6%, respectively.

Credit Risk Assessment Market Restraints

• Data privacy and security concerns: Growing instances of data breaches, cyberattacks and lack of control over consumer data in open banking create data privacy and security apprehensions that could restrain adoption. Financial institutions are cautious in deploying cloud-based solutions. Strict data regulations also limit the quality of data available for risk analysis. This requires significant investments in data security by providers.
• Counterbalance: In a credit risk assessment market facing data privacy and security concerns, implement data encryption techniques to protect sensitive data during transmission and storage. Conduct regular audits of your data security measures to identify potential vulnerabilities and address them promptly.
• High dependence on historical data: A major proportion of credit risk assessment still relies heavily on analysis of historical repayment behavior. This backward-looking approach has limitations in accurately evaluating the risk associated with new credit applicants with no prior credit history. Over-dependence on historical data also restricts the more predictive capabilities of advanced analytics.
• Counterbalance: In a credit risk assessment market with high dependence on historical data, there are several strategies, Diversification of Data Sources: Don't rely solely on historical data. Incorporate real-time data, predictive analytics, and forward-looking indicators into your credit risk assessment models.
• Integration complexities: Integrating AI/ML-based third-party credit risk solutions with existing legacy systems requires significant upfront investments. Seamless data transfer between systems while ensuring data integrity poses challenges. Lack of technical expertise in managing complex integrations often deters financial institutions from deploying advanced assessment solutions.
• Counterbalance: Standardize your data formats and processes to simplify integration. This can help to reduce errors and improve efficiency. Invest in integration technologies such as middleware, APIs, or integration platforms. These technologies can help to automate and streamline the integration process.