Various 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 decisioning.
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, hinges on an organisation’s ability to make accurate predictions based on data.
While internal data (first-party data) needs to 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 adds 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.
1. Improving Know Your Customer (KYC) Verification Processes
Generally, all fintech companies can benefit from AI-driven KYC implementation with enough data and a highly predictive model.
Fintech companies can look at enriching their internal data with large scale, high quality alternative data to compare with customer inputs, such as address, to help verify customer 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 overall user experience which in turn boosts customer conversion rates.
2. Enhancing Risk Modeling to Improve Credit Availability
Many fintech firms provide consumer credit via virtual credit cards or e-wallets and oftentimes, with a pay later scheme.
The last five years have seen rapid emergence of these companies, with the majority in emerging markets such as Southeast Asia and Latin America, where there is limited availability of credit among the broader population.
Since the majority of applicants lack traditional credit scores, this new breed of credit provider must use different methods to assess risk and make quick accept or decline decisions.
In response to this, these companies are building their own risk assessment models that supplant traditional risk scoring using alternative data, often sourced from third party data providers. This method produces models that act as proxies of traditional risk markers.
By leveraging the power of AI and alternative consumer data, it’s possible to assess risk with a level of precision comparable to traditional credit bureaus.
3. Understanding High-Value Customers to Reach Similar Prospects
First-party data is usually limited to consumers’ interactions with the business 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 drive the greatest value.
It also empowers them to identify lookalike audiences of prospects that share the same characteristics.
For example, fintech firms that provide some kind of credit may employ predictive modeling to build portraits of their highest-value customers and then score consumers based on their fit against these attributes.
To achieve this, they combine their internal data with third-party predictive features like life stages, interests and travel intent.
This model can be used to reach new audiences with the greatest likelihood of turning into high-value customers.
4. Powering Affinity Models with Unique Behavioral Insights
Affinity modeling is similar to the risk modeling described above. But while risk modeling determines the likelihood of unwanted 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 buy into other products and services based on their buying history, demographics or individual behavior.
This information enables more effective cross-selling, upselling, loyalty programmes and personalised experiences, leading customers to new products and service upgrades.
These affinity models, like the credit risk models described above, are constructed by applying machine learning on consumer data.
Sometimes it’s possible to create these models using first-party data containing details like historical purchases and financial behavior data, however 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 of their environment to understand which customers have the propensity to purchase new offerings, as well as recommend the next-best product that matches their preferences.
The 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’ll likely be left behind.
IBM Global AI Adoption Index 2022 says 35% of companies today have reported using AI in their business, and an additional 42% reported they are exploring AI.
In a Tribe report Fintech Five by Five, 70% of fintechs already use AI with wider adoption expected by 2025. 90% of them use APIs and 38% of respondents think the biggest future application of AI will be predictions of consumer behavior.
Regardless of the product or service being offered, modern consumers are coming to expect the smart, personalised experiences that come along with access to data, predictive modeling, AI and marketing automation.
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