Financial service providers have been using data for key processes such as onboarding new customers and determining customer’s credit worthiness via credit scoring and risk profiles. Traditional data that are typically used in these processes, such as credit reports and application data, may be absent especially for the underbanked or unbanked population. In the absence of these data points, what are some of the alternative data solutions that could be available? Plus, can companies build more powerful models by combining more data sources?

The context for the discussion around data solutions is set around today’s customer landscape in Southeast Asia – one of the world’s largest region with significant untapped unbanked/underbanked population.

About 30% of adults in Southeast Asia are currently serviced by traditional financial services. On the flip side, about 70% or 400 milion adults are currently underbanked/unbanked, with the majority represented by Vietnam, Indonesia and the Philippines. Alternative data can help in both cases. How so?

Data solutions help businesses serve both the unbanked and banked

Financial inclusion will help more of the 70% unbanked or underbanked access the financial system and open up more commercial opportunities, which is what some of the payment, BNPL companies, and superapps are trying to leverage. But financial institutions can’t make decisions when it comes to these potential customers because they don’t have enough traditional data; information such as credit bureau data details could be lacking. That’s when non-traditional data sources or alternative data sources can be useful. Information such as telco- and social media related data can help organisations better gauge the creditworthiness and risk profiles of these potential customers.

Businesses trying to target the 30% banked population can find themselves within a saturated market. To differentiate yourself by hyper-segmenting the market and with more targeted offering using non-traditional data, you can understand this audience in ways you traditionally wouldn’t be able to.

Data solutions can help businesses serve the banked and unbanked

So whether you’re looking to target the unbanked or better cater to the banked population, data solutions particularly non-traditional or alternative data solutions can be useful. A good example is pre-application; most traditional data is only available to you after the point of consent as that’s typically when a customer submits an application.

Even before that, you’d already need to explore marketing efforts to target specific customer segments for specific products, you are now able to look at other data sources or other data points to gather greater insights. For example, data can be acquired based on geo-location, language, browser cookies, and market segmentation, with support from other known data sets. Other background checks, such as telco- and social media-related data can also give a better idea of who your customer may be, and how best to cater to his/her needs.

More data points can be generated via customer behaviours, and how they interact with various e-commerce, banking, and finance apps. These data points will help identify up-selling or cross-selling opportunities, as well as inform customer retention strategies, such as providing more regular communication or fee waivers.

Getting started on data solutions

Organisations are increasingly realising the value of alternative data, especially in situations where there is no access or limited access to traditional data. But that needn’t be the only approach.

While initially, what led us to alternative data sources is the lack of traditional data, for example, the lack of credit bureau information on the unbanked and underbanked, or what the financial industry sometimes call thin-file customers, perhaps a more holistic approach should be adopted. Because it’s not about choosing traditional or alternative. Rather, it’s crucial to look at the business objectives – are you looking to unlock a new customer segment? Are you expanding customer value over the credit life cycle? At ADVANCE.AI, our approach is to consider data relevant for the intended purposes.

Many times, it’s useful for organisations to consider data sets that are already available, such as looking into your databases for application data, credit data and so forth. When considering data sources, it’s also essential to examine:

Availability of data
Completeness of data
Reliability
Legitimacy
Relevance and interpretation

Only then can you make better decisions, because whether it’s extending microloans, increasing loan limits or even business strategies around expansion to new markets and so on, always make sure it’s the right data for the right purpose.

Curious about the ADVANCE.AI Academy webinar: Introduction to data solutions for financial service providers? Watch it on-demand now.

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