Only 18% of fintechs and financial services organizations think their credit risk models are very accurate


PARSIPPANY, NJ–(BUSINESS WIRE)–Only 18% of fintech and financial services organizations believe their credit risk models are accurate at least 75% of the time. The finding is revealed in new research outlining the biggest credit risk analytics challenges, opportunities and trends fintech decision makers see in 2022.

The study also shows the growing appetite for AI predictive analytics and machine learning, data integration and the use of alternative data as a way to improve risk decision making. credit and supporting the key imperatives of fraud detection/prevention and financial inclusion.

The study, sponsored by Provenir, a global leader in AI-based risk management software for the fintech industry, surveyed 400 decision makers from fintech and financial services organizations in North America, Latin America, Asia-Pacific, Europe and the Middle East.

A copy of the full report is available online.

“Consumer credit markets have changed dramatically over the past two years, but many financial services organizations still use legacy approaches to credit risk decision making. The net result is that organizations today have a substantial level of uncertainty about the accuracy of their risk models, resulting in less inclusive credit, fewer approvals, and reduced opportunities for business growth. said Larry Smith, CEO and Founder of Provenir.

This “risky business” uncertainty about the accuracy of credit risk modeling may explain why real-time credit risk decision-making was the number one area of ​​investment planned by survey respondents in 2022. Additionally, the survey shows that organizations are recognizing the value of AI and machine learning, data alternatives, and data integration in credit risk decision-making approaches.

AI-powered risk decision-making is seen as key to driving improvements in many areas, including fraud prevention (78%), automating decisions across the product lifecycle credit (58%), improved cost savings and efficiency (57%), more competitive pricing (51%) and improved accuracy of credit risk profiles (47%).

The survey also assessed how organizations want to use alternative data in credit risk analysis; improving fraud detection and serving the underbanked/unbanked were the main goals cited. Sixty-five percent of decision makers surveyed recognize the importance of alternative data in credit risk analysis for better fraud detection. Additionally, 51% recognize its importance in supporting financial inclusion, 43% see its value in expanding target markets, and 40% say its use results in more accurate credit scoring.

Despite strong recognition of the value of alternative data, many organizations struggle to operationalize alternative data into their credit risk models. Data integration was cited as the biggest barrier to using alternative data by 7 out of 10 respondents.

According to the study, organizations are also looking to leverage the latest technological advancements in their selection of automated credit risk decision-making platform:

  • AI – 55% of respondents considering investing in an automated credit risk decision-making system consider AI to be one of the most important features.
  • Low-code/no-code approach – 80% of respondents consider a user interface with little or no code to be essential.
  • Model Language Interoperability – 42% cited model language interoperability as key.
  • Use of alternative data sources – Nearly half (48.5%) of those considering investing in automated credit risk decision-making systems this year say better use of alternative data sources is an important feature.

Report methodology

The study, sponsored by Provenir and conducted by Pulse, surveyed 400 decision makers from fintechs and financial services organizations in North America, Latin America, Asia-Pacific, Europe and the Middle East. Survey responses were collected between October 13 and December 21, 2021. Respondents were managers, directors, vice presidents and senior executives of small and medium-sized businesses with fewer than 1,000 employees, in North America , Europe and Asia. , and Latin America.

About Origin

Proven helps fintechs and financial services providers make smarter decisions faster with our AI-powered risk decision platform.

Provenir brings together the three essential components needed – data, AI and decision making – in a unified risk decision-making solution to help organizations deliver world-class customer experiences. This unique offering empowers organizations to drive business intelligence innovation across the customer lifecycle, improving customer experience, access to financial services, business agility, and more.

Provenir works with disruptive financial services organizations in over 50 countries and processes over 3 billion transactions annually.


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