Machine Learning

Process Improvement: Leveraging Machine Learning To Lower the Risk of Lending in Emerging Markets

A vast majority of the populations in the emerging markets of Southeast Asia, Latin America, and India are at the cusp of financial inclusion, thanks to the growing availability and adoption of digital lending services. The fintech-as-a-service market is “predicted to grow to around USD 949BN by 2028”1 due to the popularity of the alternative payment solutions, like Buy Now Pay Later, in these markets.

With the increased acceptability of digital lending in segments that had never been a part of the financial mainstream, organizations must enhance risk decisioning while ensuring fast turnaround on credit applications. Maintaining a high rate of credit approvals and managing risk while lending to people with little credit information is a challenge that more and more financial services providers are looking to solve by leveraging machine learning and artificial intelligence. 

Fintech companies are automating these processes by enriching their machine learning techniques with alternative data and scores that improve predictive risk modeling. Here are three ways machine learning can improve your acquisition and lending processes.

 

1. Enable Faster Credit Decisioning

In the digital lending space, where some firms are now approving credit within seconds, quick turnaround on credit applications is a must for any organization wanting to remain competitive. The standard customer due diligence (CDD) function at these institutions, a process to highlight credit risk by evaluating various data points and fraud signals, has been completely disrupted with the use of automation and machine learning.

 

2. Lower Your Credit Risk

Fintech companies use predictive models to develop detailed consumer profiles to prevent fraud and flag default risks. The models use machine learning to harness massive amounts of structured or unstructured data to extract immediate insights. With unified data points from watchlists, fraud screenings, email/phone/address validations, and more, companies can instantly confirm the identity and understand the behavior of their prospective customers.

 

3. Improve Cross-Sell and Up-Sell

With the attributes used to create detailed risk profiles and mitigate potential fraud, companies have the opportunity to expand the profiles of their high-value customers by enriching their machine learning models with predictive features to help them better understand behaviors, demographics and households beyond the data they capture internally.

Marketers and data analysts within these organizations can now use these profiles to develop personalized retention and cross-sell strategies to nurture these relationships while building lookalike models to apply the data characteristics of the most valuable borrowers to capture new customers

 

High Quality Data Empowers Machine Learning Algorithms

Developing a complete customer risk profile requires aggregated, privacy compliant, clean data from multiple sources, especially in markets that do not have traditional credit or payments data readily available. Data partners must ensure that the data provided has been obtained lawfully and in compliance with local regulations where data was sourced. 

 

Connect with our data experts to learn more about Mobilewalla’s feature-rich data enrichment offerings or download our BNPL sample data to see how Mobilewalla helps data and marketing professionals build more precise AI and ML models for fintech-as-a-service organizations.

1Zion Market Research, Global Fintech-as-a-Service Platform Market To Generate A Revenue of USD 949 Billion by 2028. (2022, August). https://www.zionmarketresearch.com/news/global-fintech-as-a-service-platform-market.

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