With all the disruption stemming from COVID-19 over the past two years, how sound are credit risk models? According to our recent survey, only 16% of fintech and financial services organisations believe their credit risk models are accurate at least 76% of the time.

This state of great uncertainty in credit risk modelling exposes the shortcomings of legacy approaches for credit risk decisioning that leverage limited data, workflow and automation – often in separate systems.

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.

See also: <a class='gtm_see_also_1_inread' style='text-decoration: underline;' href='/digitaledge/digital-economy/shopback-leverages-forter-grow-securely?utm_source=website&utm_medium=article&utm_campaign=ONPAGESEO'>ShopBack leverages Forter to grow securely</a>

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.

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.

See also: <a class='gtm_see_also_2_inread' style='text-decoration: underline;' href='/digitaledge/digital-economy/endowus-relies-twilio-scale-without-compromising-customer-experience?utm_source=website&utm_medium=article&utm_campaign=ONPAGESEO'>Endowus relies on Twilio to scale without compromising on customer experience</a>

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

Leaning into innovation to bring newfound speed and accuracy to credit risk modelling 

DBS suspends coverage of Bukit Sembawang Estates; keeps 'buy'

CGS-CIMB downgrades GKE with lower target price of 10 cents

Investors should focus on equities in the current environment with more defensive bias: Julius Baer

Leaning into innovation to bring newfound speed and accuracy to credit risk modelling

Related Stories

Highlights

DBS starts Wing Tai at 'buy' with TP of $2.05

Related Stories

Nansen turns to Google Cloud to empower Web3 investors with real-time market intelligence

Combatting illegal shark and ray trade in Singapore with AI

How gamification with AI and Big Data is helping make education accessible for all