Study: AI can improve credit risk models in financial services

Most financial services firms and fintechs doubt their models' accuracy.

March 29, 2022

2 Min Read

New study finds fintechs are turning to AI and alternative data for accurate credit risk models.

A new study finds that most financial service firms and fintechs doubt the accuracy of their credit risk models. Only 18% of the organizations think their credit risk analysis are credible at least 75% of the time.

Decision-makers are starting to rely on data integration, alternative data, AI predictive analytics and machine learning. They’re finding these technologies can bolster efforts in several areas, including fraud prevention and detection, financial inclusion and credit risk decisioning.

“Consumer credit markets have changed dramatically over the past two years. 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.

The AI company, which sponsored the study, provides risk decisioning software for the fintech sector.

This year, the biggest investment priority for these organizations is real-time credit risk decisioning to improve the accuracy of their modeling. Companies are recognizing how AI and machine learning, data integration, and alternative data can play an important role in refining credit risk analysis.  

Fraud prevention is the area that 78% of respondents believe will benefit the most from AI-enabled risk decisioning. Automating decisions across the credit lifecycle, competitive pricing, and cost efficiency are other areas that AI-enabled risk decisioning could support.

Around 65% of the respondents thought that alternative data could increase fraud detection and 51% believed it could boost financial inclusion. Other benefits include accurate credit scoring and expanding markets.

In spite of the advantages, 70% of fintechs and financial services organizations found it challenging to incorporate alternative data into their credit risk models. Data integration was cited as a hurdle.

Most companies want to use the latest technology into their automated credit risk decisioning software. Around 55% plan on prioritizing AI as a crucial feature in future investments. Almost half of the organizations want to include alternative data sources.  

About 80% believe a low-code/no code user interface is critical, which can optimize AI for rapid risk decisioning and remove barriers for entry.

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