3 ways machine learning can improve the lending process

A large majority of the population in the emerging markets of Southeast Asia, Latin America and India are on the cusp of financial inclusion, thanks to the increasing availability and use of digital lending services.

The fintech-as-a-service market is forecast to grow to around $949 billion by 2028 due to the popularity of the Buy Now Pay Later alternative payment solution in these markets.

With increased acceptance of digital lending in segments that have never been part of the financial mainstream, organizations need to improve risk decisions while ensuring faster processing of credit applications.

Maintaining a high level of credit approvals and managing risk while lending to people with poor credit information is a challenge that more and more financial institutions are looking to solve by leveraging machine learning and artificial intelligence.

Fintech companies are automating these processes by enriching their machine learning techniques with 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 decision

In the digital lending space, where some firms now approve credit within minutes, fast processing of credit applications is a must for any organization that wants to remain competitive.

The standard Customer Due Diligence (CDD) function at these institutions, a process of highlighting credit risk by evaluating various data points and fraud signals, has been completely disrupted with the use of automation and machine learning.

2. Lower the 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 leverage vast amounts of structured or unstructured data to extract immediate insights. With unified data points from watchlists, fraud screenings, email/phone/address validation and more, businesses can instantly verify the identity and understand the behavior of potential customers.

3. Improve cross-selling and up-selling

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

Marketers and data analysts in these organizations can now use these profiles to develop personalized retention and cross-selling strategies to nurture these relationships, while building models to use the data characteristics of the most valuable buyers to capture new customers.

High-quality data feeds machine learning algorithms

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

Mobilewalla recently launched its industry-first solution, LendBetter, to help financial institutions reduce lending risk in emerging credit markets. Connect with Mobilewalla data experts to learn more about their feature-rich data enrichment offering or download their BNPL sample data to see how Mobilewalla helps data and marketing experts build more accurate AI and ML models for fintech-as-a-service – organizations.

Featured image credit: Freepik