Relying on innovation for agile and accurate credit risk modeling

In an environment of significant uncertainty, AI, machine learning and alternative data will optimize decision-making and maximize returns

The pandemic-induced disruption has shed light on credit risk patterns, raising pertinent questions about the quality of existing decision-making processes. Worryingly, our survey of 100 industry decision makers in Asia Pacific found that only 16% of fintech and financial services companies believe their credit risk models are accurate at least 76% of the time.

This startling figure should worry business leaders, particularly in India, and the great uncertainty surrounding credit risk modeling exposes the shortcomings of legacy approaches that leverage limited data, workflow and automation – often in separate systems. To truly improve decision-making, organizations need more data, more automation, more sophisticated processes, more forward-looking predictions, and faster decision-making. And to do that, they need AI, machine learning, and alternative data. Recognizing this, Indian financial services leaders are looking to leverage the potential of artificial intelligence (AI); in particular with regard to the decision and deployment of new credits, even as national regulations around the technology are relatively nascent.

Our survey highlighted the growing appetite for AI predictive analytics and machine learning, data integration and the use of alternative data as a way to improve credit risk decision making. . Real-time credit risk decision-making was the top area of ​​investment respondents planned for in 2022, as organizations work to address the current “financial fault line” in credit risk decision-making. credit risk.

Financial services executives view AI-powered risk decision-making as the cornerstone of improvements in many areas, including fraud prevention (91%), automation of decisions across the credit lifecycle (75%), improved cost savings and operational efficiency (68%) and greater competitiveness. price (60%).

However, many companies are struggling to mount the resources needed to support their AI initiatives; developing and implementing AI can be time-consuming, and it can be prohibitively expensive, with only 7% of financial services organizations beginning to see ROI from AI initiatives in the 120 days. PWC’s Uncovering the Ground Truth: AI in Indian Financial Services study reports that lack of integration poses a challenge to full adoption of AI. Financial institutions in India are still dependent on legacy systems, and due to the increase in data and variety, AI applications cannot be adapted to make the most of it.

Sixty-five percent of decision makers in our survey indicated that they recognize the importance of alternative data in credit risk analysis for better fraud detection. Additionally, 46% recognize its importance in supporting financial inclusion. Alternative data gives lenders a more varied way to detect fraud before it happens and allows them to assess people with thin (or non-existent) credit records by establishing a more holistic and comprehensive view of credit risk. an individual, which helps lenders expand their services. by improving access to credit.

For unbanked and underbanked consumers, AI gives organizations the ability to support the financial journeys of these consumers. In India, the growth of unbanked and underbanked customers has increased significantly. Financial services organizations typically struggle to support these consumers because they don’t have a data history understandable by traditional decision-making methods. However, because AI can identify patterns in a wide variety of alternative and traditional data, it can enable highly accurate decision-making even for fileless or file-light consumers. This greatly benefits those who cannot be easily assessed by traditional methods, while also benefiting financial institutions, by expanding their total addressable market.

By deploying artificial intelligence and machine learning technologies, as well as embracing alternative data, organizations are well on their way to improving agility and confidence in credit risk modeling. By doing so, they will be better prepared to respond to the changes ahead, while supporting critical industry imperatives such as fraud prevention and inclusive finance.

As organizations face steep inequities in credit risk, AI and machine learning offer the power to solve these challenges and deliver seamless experiences to internal and external stakeholders. The age of AI has arrived – just in time for organizations to embrace and move forward with better credit risk decision making.

(The author, Mr. Varun Bhalla, Managing Director, Provenir India and the opinions expressed in this article are his own)

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