Published by Jessica Weisman-Pitts
Posted on April 6, 2022
5 min readLast updated: February 8, 2026
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Global survey findings reveal a great state of uncertainty in credit risk modelling,
Global survey findings reveal a great state of uncertainty in credit risk modelling,
underscoring the need for AI, machine learning, and alternative data
By Kim Minor, Senior Vice President, Global Marketing at Provenir
With all the disruption stemming from COVID-19 over the past two years, how sound are credit risk models? This was one of the questions we sought to find the answer to with a global research study that surveyed 400 decision makers in the industry. The results were more than a little unsettling – only 18 percent of fintechs and financial services organizations believe their credit risk models are accurate at least 75 percent of the time.
That’s astonishing – especially given the fact that the rest of the respondents – indeed the vast majority of those surveyed – indicated they believed their credit risk models were accurate less than 75 percent of the time. Especially as credit risk modelling is at the heart of every fintech and financial services company.
This state of great uncertainty in credit risk modelling is exposing the shortcomings of legacy approaches for credit risk decisioning that leverage limited data, workflow and automation – often in separate systems. To really level-up decisioning, organizations need more data, more automation, more sophisticated processes, more forward-looking predictions and greater speed-to-decisioning. And to this end, they need AI, machine learning, and alternative data.
While AI, machine learning and alternative data may have been on the credit risk decisioning “nice to have” list a few years ago, fintechs and financial services organizations are quickly realizing the alternative – legacy technology and approaches – are simply are not up to today’s task of credit risk decisioning.
Our survey underscored 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. Real-time credit risk decisioning was respondents’ No. 1 planned investment area in 2022, as organization’s work to resolve today’s “financial fault line” in credit risk decisioning.
Financial services executives see AI-enabled risk decisioning as the cornerstone to 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%).
However, many companies struggle with mounting the resources needed to support their AI initiatives; it can take long time to develop and implement AI, and it can be prohibitively expensive. Only 21% of financial services organizations begin to see a return on investment from AI initiatives within 120 days.
Fraud continues to grow for financial services and lending firms, both before and during the pandemic, with identity fraud losses hitting $56 billion in 2020. And while great strides have been made in financial inclusion, there’s still a long ways to go as about 1.7 billion adults globally remain unbanked.
Sixty-five percent of decision makers in our survey indicated they recognize the importance of alternative data in credit risk analysis for improved fraud detection. Additionally, 51 percent recognize its importance in supporting financial inclusion.
Alternative data is a more varied way for lenders to detect fraud before it happens and evaluate those individuals with a thin (or no) credit file by putting together a more holistic, comprehensive view of an individual’s risk
For unbanked and underbanked consumers, AI gives organizations the opportunity to support those consumers’ financial journeys. Financial services organizations typically struggle to support these consumers because they don’t come with a history of data that is understandable by traditional decisioning methods. However, because AI can identify patterns in a wide variety of alternative, traditional, linear, and non-linear data, it can power highly accurate decisioning, even for no-file or thin-file consumers. This vastly benefits those who can’t be easily scored via traditional methods, while also benefitting financial institutions, by expanding their total addressable market.
By deploying AI and machine learning technologies, and embracing alternative data, organizations are on their way to improved agility and confidence in credit risk modelling. In doing so, they will be more prepared to react to changes moving forward, while also supporting critical industry imperatives such as fraud prevention and inclusive finance.
But we need to bring down the barriers to entry and shrink the cost, complexity, resource requirements and time-to-market of AI and machine learning and make alternative data more accessible.
The past two years have seen great disruption in credit markets, spurring the need for new thinking and doing in credit risk decisioning. Fortunately, the industry is responding with unified risk decisioning solutions that combine data, AI and decisioning with pre-built models tailored and trained for risk decisioning across the customer lifecycle, and leaning in on low-code, no-code approaches.
The era of AI is here – just in time for organizations to come to terms with and resolve the financial fault line in credit risk decisioning.
About the Author:
Kim Minor is Senior Vice President, Global Marketing at Provenir, which helps fintechs and financial services providers make smarter decisions faster with its AI-Powered Risk Decisioning Platform. Provenir works with disruptive financial services organizations in more than 50 countries and processes more than 3 billion transactions annually.
Credit risk modelling is the process of assessing the likelihood that a borrower will default on their financial obligations. It uses historical data and statistical methods to predict future behavior.
AI in finance refers to the use of artificial intelligence technologies to analyze data, automate processes, and enhance decision-making in financial services, including credit risk assessment.
Alternative data refers to non-traditional data sources used in financial analysis, such as social media activity, online behavior, and transaction data, to gain insights into consumer behavior and creditworthiness.
Financial inclusion is the effort to provide access to financial services for all individuals, particularly those who are underserved or unbanked, to promote economic participation and stability.
Machine learning is a subset of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention, often used in predictive analytics in finance.
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