4 Ways Alternative Data is Improving Fintech Companies in APAC


Various categories of fintech companies – Buy Now, Pay Later (BNPL), digital lending, payments and collections – are increasingly relying on predictive models built using artificial intelligence and machine learning to support critical business functions such as risk decision making.

According to a report by Grand View Research, Inc., the global AI in Fintech market size is expected to reach US$41.16 billion by 2030, growing at a compound annual growth rate (CAGR) of 19 .7% in Asia-Pacific alone from 2022 to 2030.

The success of AI in fintech, or any business for that matter, depends on an organization’s ability to make accurate predictions based on data.

While internal data (first-party data) should be considered in AI models, this data often fails to capture critical predictive features, resulting in these models underperforming. In these situations, the alternative enrichment of data and functionality can be a powerful advantage.

Enriching first-party data with highly predictive capabilities adds the breadth, depth, and scale needed to increase the accuracy of machine learning models.

Here’s an overview of four data enrichment strategies for certain use cases and processes that fintechs can leverage to grow their business and manage risk.

1. Improve Know Your Customer (KYC) verification processes

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Generally, all fintech companies can benefit from an AI-powered KYC implementation with enough data and a highly predictive model.

Fintech companies can consider enriching their internal data with large-scale, high-quality alternative data to compare with customer inputs, such as address, to help verify customer identity.

This machine-generated information can be more accurate than manual information and serve as a layer of protection against human error and can also speed up customer onboarding.

Accurate, near real-time verification can help improve the overall user experience, which in turn increases customer conversion rates.

2. Improve risk modeling to improve credit availability

Many fintech companies offer consumer credit through virtual credit cards or e-wallets and often with a pay-after system.

The last five years have seen the rapid emergence of these companies, mostly in emerging markets such as Southeast Asia and Latin America, where the availability of credit is limited among the general population.

Since the majority of applicants don’t have traditional credit scores, this new generation of credit grantors must use different methods to assess risk and make quick go-or-go decisions.

In response to this, these companies are building their own risk assessment models that replace traditional risk scoring using alternative data, often from third-party data providers. This method produces models that act as substitutes for traditional risk markers.

By leveraging the power of AI and alternative consumer data, risk can be assessed with a level of accuracy comparable to that of traditional credit bureaus.

3. Understand high-value customers to reach similar prospects

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First-party data is generally limited to consumer interactions with the company collecting it.

Alternative data can be particularly valuable when used to deepen a fintech’s understanding of its best customers. This allows businesses to focus on serving the audiences that generate the greatest value.

It also allows them to identify similar audiences of prospects who share the same characteristics.

For example, fintech companies that provide some kind of credit can use predictive modeling to build portraits of their most important customers and then score consumers based on their fit with those attributes.

To achieve this, they combine their internal data with third-party predictive features such as life stages, interests and travel intentions.

This model can be used to reach new audiences with the highest likelihood of turning into high-value customers.

4. Feed Affinity Models With Unique Behavioral Information

Affinity modeling is similar to risk modeling described above. But whereas risk modeling determines the likelihood of undesirable outcomes such as credit defaults, affinity modeling predicts the likelihood of desired outcomes such as offer acceptance.

Specifically, affinity analysis helps fintech companies determine which customers are most likely to purchase other products and services based on their purchase history, demographics, or individual behavior.

This information enables more effective cross-selling, up-selling, loyalty programs and personalized experiences, guiding customers to new products and service upgrades.

These affinity models, like the credit risk models described above, are built by applying machine learning to consumer data.

Sometimes it is possible to build these models using first-party data containing details such as purchase history and financial behavior data, but such data is becoming more common among financial services.

To build affinity models with increased reach and accuracy, fintechs can combine their data with unique behavioral information such as app usage and interests outside of their environment to understand which customers have the propensity to buy new offers, as well as recommending the next best solution. product that matches their preferences.

The Business Case for Data and AI in Fintech

If you don’t adopt a plan soon to leverage alternative data and AI in your fintech business, you’ll likely be left behind.

The IBM Global AI Adoption Index 2022 indicates that 35% of companies today said they are using AI in their business, and an additional 42% said they are exploring AI.

In a Tribe Fintech Five by Five report, 70% of fintechs are already using AI with wider adoption expected by 2025. 90% use APIs and 38% of respondents believe the greatest future application of AI will be the predictions of consumer behavior.

Regardless of the product or service being offered, modern consumers expect intelligent, personalized experiences that come with access to data, predictive modeling, AI, and marketing automation.

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