New research outlines the “risky business” and the greatest credit risk analysis challenges, opportunities, and trends fintech decision makers see in 2022
Parsippany, NJ — March 7, 2022 — Only 18 percent of fintechs and financial services organizations believe their credit risk models are accurate at least 75 percent of the time. The finding is revealed in new research outlining the greatest credit risk analysis 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 use of alternative data as the means to improve credit risk decisioning and support the key imperatives of fraud detection/prevention and financial inclusion.
The study, sponsored by Provenir, a global leader in AI-powered risk decisioning software for the fintech industry, surveyed 400 decision makers in fintech and financial services organizations across 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, yet many financial services organizations are still employing legacy approaches to credit risk decisioning. The net result is that organizations today have a substantial level of uncertainty in the accuracy of their risk models which results in less inclusive credit, fewer approvals, and reduced opportunity for business growth,” said Larry Smith, CEO and Founder of Provenir.
This “risky business” uncertainty in credit risk modelling accuracy may be why real-time credit risk decisioning was survey respondents’ No. 1 planned investment area in 2022. Additionally, the survey shows organizations are recognizing the value of AI and machine learning, alternative data, and data integration in credit risk decisioning approaches.
AI-enabled risk decisioning is seen as key to usher in improvements in many areas, including fraud prevention (78%), automating decisions across the credit lifecycle (58%), improving cost savings and efficiency (57%), more competitive pricing (51%) and improving accuracy of credit risk profiles (47%).
The survey also gauged how organizations want to use alternative data in credit risk analysis; improving fraud detection and serving the underbanked/unbanked were the top main objectives cited. Sixty-five percent of decision makers polled recognize the importance of alternative data in credit risk analysis for improved fraud detection. Additionally, 51 percent recognize its importance in supporting financial inclusion, 43 percent see its value in expanding target markets, and 40 percent say its use results in more accurate credit scoring.
Despite strong recognition of the value of alternative data, many organizations struggle with operationalizing alternative data within their credit risk models. Data integration was cited as the biggest impediment to the use of alternative data by 7 out of 10 respondents.
According to the study, organizations are also looking to lean into the latest technology advancements in their automated credit risk decisioning platform selection:
- AI – 55% of respondents who plan to invest in an automated credit risk decisioning system consider AI to be one of the most important features.
- Low-code/no code approach – 80% of respondents consider a low/no code user interface critical.
- Model language interoperability – 42% cited model language interoperability as key.
- Utilization of alternative data sources – Nearly half (48.5%) of those planning to invest in automated credit risk decisioning systems this year say improved utilization of alternative data sources is an important feature.
Report Methodology
The study, sponsored by Provenir and conducted by Pulse, surveyed 400 decision makers in fintechs and financial services organizations across North America, Latin America, Asia Pacific, Europe and the Middle East. The survey responses were gathered between Oct. 13 and Dec. 21, 2021. Respondents were Managers, Directors, VPs and C-Suite executives at small-to-mid-sized organizations with less than 1,000 employees, in North America, Europe, Asia, and Latin America.
