Different categories of fintech firms – Buy Now, Pay Later (BNPL), digital lending, payments and collections – are increasingly using predictive models built using artificial intelligence and machine learning to support core business functions such as decision making on risks.
According to A the report According to Grand View Research, Inc., the global fintech artificial intelligence market is expected to reach US$41.16 billion by 2030, with Asia Pacific alone growing at a compound annual growth rate of growth rate (CAGR) by 19.7%.
The success of AI in financial technology, 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 considered in AI models, this data often does not capture important predictive features, causing these models to fail. In these situations, alternative data and feature enrichment can create a strong advantage.
Enriching raw data with highly predictive features adds the necessary breadth, depth, and scale needed to improve the accuracy of machine learning models.
Here’s an overview of four data enrichment strategies for specific use cases and processes that fintech companies can use to grow their business and manage risk.
1. Improving Know Your Customer (KYC) verification processes.
Generally, all fintech companies can benefit from implementing AI-powered KYC with sufficient data and a highly predictive model.
Fintech companies can 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.
This machine-generated information can be more accurate than manual and serves as a layer of protection against human error, and can speed up customer onboarding.
Accurate near-real-time validation can help improve the overall user experience, which in turn increases customer conversion rates.
2. Improving risk modeling to increase credit availability
Many fintech companies provide consumer loans through virtual credit cards or e-wallets and often on a pay-later basis.
The past five years have seen a rapid emergence of these companies, most of which are located in emerging markets such as Southeast Asia and Latin America, where there is limited access to credit among the general population.
Because most applicants lack traditional credit scores, this new breed of loan providers must use different methods to assess risk and make or reject decisions quickly.
In response, these companies are creating their own risk assessment models that replace traditional risk assessment using alternative data, often obtained from third-party data providers. This method creates models that act as proxies for traditional risk markers.
Using the power of artificial intelligence and alternative consumer data, risk can be assessed with a level of accuracy comparable to traditional credit bureaus.
3. Understanding valuable customers to reach similar prospects
First-party data is usually limited to a consumer’s interaction with the company that collects it.
Alternative data can be particularly valuable when used to deepen a fintech company’s understanding of its best customers. This allows companies to focus on serving the audience that brings the most value.
It also gives them the ability to identify a similar audience of potential customers who share the same characteristics.
For example, fintech companies that provide some kind of credit can use predictive modeling to create portraits of their most valuable 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 most likely to convert into high-value customers.
4. Providing proximity models with unique behavioral information
Proximity modeling is similar to the risk modeling described above. But while risk modeling determines the likelihood of undesirable outcomes, such as loan defaults, proximity modeling predicts the likelihood of desirable outcomes, such as acceptance of an offer.
In particular, affinity analysis helps fintech companies determine which customers are most likely to buy other products and services based on their purchase history, demographics or individual behavior.
This information drives more effective cross-selling, up-selling, loyalty programs and personalized experiences by guiding customers to new products and service upgrades.
These proximity models, like the credit risk models described above, are built by applying machine learning to consumer data.
It is sometimes possible to build such models using proprietary data containing details such as purchase history and financial behavior data, but this data is becoming increasingly common among financial services.
To build membership models with greater reach and accuracy, fintech companies can combine their data with unique behavioral data, such as app usage and interests outside of their environment, to understand which customers are likely to purchase new offers and recommend the next best product , which matches their preferences.
The Business Case for Data and Artificial Intelligence in FinTech
If you don’t adopt a plan soon to use alternative data and artificial intelligence in your fintech company, you are likely to be left behind.
IBM 2022 Global Artificial Intelligence Application Index says that 35% of companies today reported using artificial intelligence in their business, and another 42% reported studying artificial intelligence.
In the tribe the report Fintech five by five, 70% of fintechs are already using artificial intelligence with wider adoption expected by 2025. 90% of them use APIs, and 38% of respondents believe that the biggest use of artificial intelligence in the future will be predicting consumer behavior.
Regardless of the product or service offered, today’s consumers expect a smart, personalized experience that comes with access to data, predictive modeling, artificial intelligence and marketing automation.