To level up decisioning, organisations need more data, automation, sophisticated processes, forward-looking predictions and greater speed-to-decisioning. And to this end, they need artificial intelligence (AI), machine learning, and alternative data.
The Singapore government recognises the importance of AI and has invested $180 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.
The good news is that there is a 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. Our study found that real-time credit risk decisioning is the 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, automating decisions across the credit lifecycle, improving cost savings and operational efficiency and more competitive pricing.
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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% 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 one of the challenges of AI adoption. Financial institutions in the country 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.
Decision-makers are recognising the importance of alternative data in credit risk analysis for improved fraud detection. They also see its importance in supporting financial inclusion.
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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. 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 offer 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.
Bharath Vellore is the general manager for Provenir APAC
