4 ways alternative data is improving Fintech companies in APAC

by James · July 27, 2022

Different categories of fintech firms – Buy Now, Pay Later (BNPL), digital lending, payments and collections – are increasingly leveraging predictive models built using artificial intelligence and machine learning to support core business functions such as risk decisions.

According to a report by Grand View Research, Inc., the global artificial intelligence in fintech market is expected to reach $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) must be factored into AI models, this data often fails to capture critical predictive features, causing these models to underperform. In these situations, alternative data and feature enrichment can establish a powerful advantage.

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

Here’s a look at four data enrichment strategies for certain use cases and processes that fintech companies can leverage to grow their business and manage risk.

Table of Contents

1. Improve Know Your Customer (KYC) verification processes.

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

Fintech companies can look to enrich their internal data with large-scale, high-quality alternative data to compare with customer input, such as address, to verify the customer’s identity.

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

The accurate and 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 firms provide consumer credit via virtual credit cards or e-wallets and often with a pay later scheme.

Over the past five years, these companies have grown rapidly, with the majority in emerging markets such as Southeast Asia and Latin America, where there is limited access to credit among the wider population.

Since the majority of applicants lack traditional credit scores, this new type of credit providers must use different methods to assess risk and make quick decisions to accept or decline.

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

By harnessing the power of AI and alternative consumer data, it is possible to assess risk with a level of precision comparable to traditional credit bureaus.

3. Understand high value customers to reach similar prospects

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First-party data is usually limited to consumers’ interactions with the business that collects it.

Alternative data can be particularly valuable when used to deepen a fintech’s understanding of its best customers. This allows companies to focus on serving the audience that creates the most value.

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

For example, fintech firms that provide some form of credit can use predictive modeling to create portraits of their most valuable customers and then score consumers based on their fit against those characteristics.

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

This model can be used to reach new audiences most likely to become high value customers.

4. Strengthen affinity models with unique behavioral insights

Affinity modeling is similar to the risk modeling described above. However, while risk modeling determines the probability of undesirable outcomes such as credit default, affinity modeling predicts the probability of desired outcomes such as offer acceptance.

Specifically, affinity analysis helps fintech companies determine which customers are most likely to buy into 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, driving customers to new products and service upgrades.

These affinity models, like the credit risk models described above, are constructed using machine learning on consumer data.

It is sometimes possible to create these models using first-party data that contains details such as historical purchase and financial behavior data, but this data is increasingly common among financial services.

To construct affinity models with greater reach and accuracy, fintech firms can combine their data with unique behavioral insights such as app usage and interests outside the environment to understand which customers are inclined to purchase new offers, as well as recommend the next best product that matches their preferences.

Business Case for data and AI in Fintech

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

The IBM Global AI Adoption Index 2022 states that 35% of companies today reported using AI in their business, and another 42% reported exploring AI.

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

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