Leaning into Innovation to Bring Newfound Speed and Accuracy to Credit Risk Modelling


By Bharath Vellore, General Manager Provenir APAC

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 100 industry decision makers across APAC. The results were more than a little unsettling – only 16 percent of fintech and financial services organisations believe their credit risk models are accurate at least 76 percent of the time.

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, organisations 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. The Singapore government recognises the importance of AI and has invested SGD180 million into the programme for the finance industry. In collaboration with the Monetary Authority of Singapore (MAS) and the National AI Office (NAIO) at the Smart Nation and Digital Government Office (SNDGO), the programme is to implement AI into the financial sector for the benefit of improved customer service and risk management.

Our survey underscored the growing appetite for AI predictive analytics and machine learning, data integration, and the use of alternative data as the means to improve credit risk decisioning. Real-time credit risk decisioning was the respondents’ No. 1 planned investment area in 2022, as organisations 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 (91 percent), automating decisions across the credit lifecycle (75 percent), improving cost savings and operational efficiency (68 percent) and more competitive pricing (60 percent) .

However, many companies struggle with mounting the resources needed to support their AI initiatives; it can take a long time to develop and implement AI, and it can be prohibitively expensive, with only 7 percent of financial services organisations beginning to see a return on investment from AI initiatives within 120 days. PWC’s Uncovering the Ground Truth: AI in Indian Financial Services reports that lack of integration is posing as a challenge for AI to be fully adopted. Financial institutions in India are still reliant on legacy systems and because of the increase in data and variety, AI applications cannot be suited to make the most out of it.

Sixty-five percent of decision makers in our survey indicated they recognise the importance of alternative data in credit risk analysis for improved fraud detection. Additionally, 46 percent recognise 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 organisations the opportunity to support those consumers’ financial journeys. In India, the growth of unbanked and underbanked customers has increased significantly. Fintech company’s like ANVI have built a digital platform utilising artificial intelligence to support customers in having financial accessibility. Financial services organisations 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 and traditional 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, as well as embracing alternative data, organisations 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.

As organisations come to terms with stark inequalities over credit risks, AI and machine learning offers the power to resolve these challenges and provide seamless experiences for internal and external stakeholders. The era of AI is here – just in time for organisations to come to terms with and move forward with better credit risk decisioning.