In today’s rapidly transforming financial landscape, traditional credit scoring models are being challenged by the complexities of informal economies, diverse consumer behaviors, and evolving digital ecosystems. Artificial intelligence (AI) has emerged as a revolutionary tool, offering innovative solutions to bridge the financial inclusion gap worldwide. By leveraging alternative datasets and advanced technologies like Machine Learning (ML), Natural Language Processing (NLP), and Predictive Analytics, AI is reshaping the way financial institutions assess creditworthiness, unlocking opportunities for millions.
Beyond traditional credit scoring
Globally, traditional systems such as FICO have long been the standard for assessing creditworthiness. However, these models often rely heavily on static data points like credit card transactions and bank account histories, which exclude vast segments of unbanked or underbanked populations. In emerging markets, gig workers, rural populations, and small businesses operating in cash-based economies are often left without access to formal credit.
Why traditional models fall short?
- Data gaps: Over 1.4 billion adults globally lack access to formal financial services (World Bank, 2023).
- Exclusionary practices: Inconsistent income patterns and the absence of traditional credit histories often disqualify gig workers and small business owners.
- Static assessments: Limited flexibility to incorporate dynamic or region-specific variables.
While global digital ecosystems are rapidly advancing, traditional scoring models remain reliant on static data sources. In contrast, alternative data points such as mobile phone usage, utility payments, and e-commerce activity are:
- More accessible in SEA due to widespread mobile penetration (90% mobile penetration in Indonesia, for example).
- Strong indicators of creditworthiness for those excluded from traditional banking.
Traditional systems like FICO lack the capability to ingest and process such diverse data types at scale.
AI’s advantages in credit scoring
The integration of AI in credit scoring is critical for a regions, where informal economies dominate, and access to credit is limited. With alternative data, AI bridges the gap between financial institutions and underserved populations, fostering financial inclusion and driving regional economic growth. AI technologies, tailored to local challenges, are proving instrumental in reshaping the global credit landscape.
- Inclusion: Incorporates alternative data like mobile usage, utility payments, and e-commerce activity to assess creditworthiness.
- Precision: Minimizes biases and enhances accuracy in predicting financial behaviors.
- Real-Time adaptability: Allows financial institutions to adjust to economic changes and consumer habits dynamically.
Global innovations in AI-powered credit scoring
Key players in the digital economy have entered the DFS (Digital Financial Services) sector by launching digital banks. They are embracing generative AI to power virtual assistants and perform credit scoring for underserved customers, among other uses.
From micro finance in Indonesia to gig economy loans in the Vietnam, AI is proving instrumental in reshaping credit scoring systems. Here’s how:
1. Bank BRI, Indonesia
By using AI to analyze agricultural data, Bank BRI provided micro loans to millions of rural borrowers who lacked formal credit histories. This approach reduced loan processing times via mobile app from 2 weeks to less than 2 days. It also enhanced fraud detection, reducing the rate of fraud by 40% to record low levels, versus other banks.
2. Grab Financial, Southeast Asia
With a variety of loan products tailor-made for gig-workers, Grab loan disbursals increased 57% year-on-year to USD 1.5 billion in 2023.
Using platform data such as transaction histories and customer ratings, Grab Financial offered personalized loans to gig workers, enabling 33% of its driver-partners to access credit.
3. UnionBank, Philippines
UnionBank’s fintech arm, UBX, partnered with Singapore-based Bixie Pte. Ltd. to create an open finance platform targeting women. This partnership aims to disburse aid funds directly to Filipino women beneficiaries through a “last mile” disbursement network.
UnionBank’s gender-sensitive AI models empowered women entrepreneurs by analyzing behavioral data and business performance metrics, addressing systemic barriers in traditional credit systems.
4. Kredivo, Indonesia
Kredivo integrated AI into e-commerce platforms, enabling real-time credit assessments based on purchasing behavior and mobile activity. This approach allowed over 2 million users to access credit safely and effectively.
By leveraging e-commerce behavior, payment histories, and mobile activity, Kredivo successfully bridged the gap between digital consumers and formal credit systems, enabling millions of underserved users to access credit safely and responsibly.
5. MYBank, China
MYBank’s AI-driven “310 model” revolutionized SME lending by approving loans in seconds and disbursing funds within three minutes. By incorporating supply chain data, MyBank served over 53 million SMEs with a default rate of just 1%.
By the end of 2023, MyBank had served over 53 million SMEs cumulatively, demonstrating the scale of its impact on financial inclusion. Despite serving high-risk segments, MyBank maintains a default rate of just 1%, showcasing the effectiveness of its AI-driven credit assessment.
6. Timo Bank, Vietnam
Timo used social media and telecom data to score freelancers and gig workers, providing flexible loan terms tailored to their irregular income streams.
Our ultimate mission is to enable financial inclusion for everyone – not just for those who are ‘unhappily banked’ or those who are currently ‘unbanked’, but inclusion across our entire society. Utilizing Mambu’s cloud banking platform allows us to build a bank that is truly accessible to everybody. We want to become a financial partner to our customers and provide services that enhance our customers’ lives, and with Mambu we’ve found a technology partner that allows us to do that. ~Henry Nguyen, CEO, Timo.
5 lessons for the global market from SEA
AI-driven credit scoring offers critical lessons for financial institutions worldwide:
- Embrace alternative data: Mobile phone usage, social media activity, and e-commerce behavior are valuable indicators of creditworthiness.
- Collaborate for scale: Partnerships with telecom, fintech, and e-commerce platforms enable reliable, scalable data collection.
- Prioritize financial inclusion: Tailored AI models can address the unique needs of unbanked and underserved populations, fostering global economic growth.
- Adopt mobile-first strategies: With smartphones as a primary tool for financial access, mobile-first approaches ensure broader outreach.
- Leverage predictive analytics: Real-time insights into borrower behavior allow for dynamic decision-making and risk mitigation.
A look ahead: The future of credit scoring
The global credit landscape is evolving, and AI is leading the charge. Emerging technologies like generative AI, blockchain, and predictive modeling will further enhance credit scoring systems. By enabling real-time decisions, integrating with decentralized finance (DeFi) platforms, and fostering collaborations across industries, AI is poised to democratize credit access on an unprecedented scale.
As financial institutions continue to innovate, the focus must remain on inclusivity and sustainability. With AI, the dream of universal financial access is not just achievable—it’s imminent.