The Art of Alternative Credit Scoring

The Art of Alternative Credit Scoring

New-age fintech start-ups are using AI and machine learning on swathes of alternate data in a market where adequate transaction data or banking records aren’t available

Embracing the rise of alternative credit scoring models has become an integral aspect of finance for lenders – especially in sections of the market considered too impenetrable or difficult to underwrite. Major developments in artificial intelligence and machine learning models, especially innovations in using data outside the list of mainstream lending practices, is what has made this possible.

Getting around the thin file

Entrepreneur magazine writes: “Some of the visionary players in the market segments where inadequate data is a major impediment to underwriting and hence lending, are making great use of alternative credit scoring models using AI/ML on non-conventional data to profile and evaluate customers. These models often combine elements of different computer vision algorithms (for image segmentation, object detection), geospatial analysis, and NLP methods for information extraction from textual data.”

This approach has turned out to be especially relevant for those in the ‘new-to-credit’ segment. For early movers in the sector targeting the lower section of MSME sectors (where mainstream underwriting credit history data is rather weak), AI/ML-driven alternative credit scoring models have become ‘increasingly integral’ to the lending processes, and a ‘key differentiator’ of the future.

Usually, credit lending institutions follow conventional methods of credit scoring, i.e. formal banking records, accounting records, sufficient credit history (credit bureau data), tax return filing information for several years. Fintech-based start-ups tackling the market facing a dearth of the aforementioned data set up alternative credit-scoring models through models focusing on geolocation-based data, demographic factors, economic and risk indicators, satellite image data as well as other location-based sectoral economic data to feed into models powered by AI/ML.

Modern alternative AI/ML-based credit scoring models also make use of permitted mobile data (informal accounting data from mobile apps, transactional SMS data, etc) using a regular expression based on Natural Language Processing (NLP) methods followed by machine learning modeling. Entrepreneur opines: “one important aspect of alternative credit scoring approach is that this approach makes use of the alternative data, along with any limited banking data available or even any little credit history (‘thin file’) that may be available in some scenarios.”

AI/ML crucial for multiple data types

It isn’t just the use of a wide variety of non-conventional data, there are also several data types that are used in alternative credit scoring. This includes texts, images as well as swathes of numeric data. This makes computing and data extraction techniques using AI/ML techniques absolutely crucial to ingest and use this alternative data, such as image recognition or SMS scraping etc, which would not have been used in most traditional methods.

“Carefully developed and rigorously tested ML models using such comprehensive data from multiple sources, are capable of highly accurate credit risk prediction. This enables fintech firms to address the critical data gap by substituting conventional credit scoring with AI/ML-based credit scoring models using alternative data.”

Alternative credit scoring allows for the expanding of the scope of lending to include a major portion of underserved segments, thereby enhancing revenue with appropriate risk management and pricing for lenders and catering to cases of financial inclusion.

AI/ML-based solutions enabling credit risk modeling are going to be crucial in bringing almost completely digitized lending products to newer segments. Early movers and early adopters will gain a significant advantage in the space, owing to their “significantly evolved AI/ML practices and rich, organized internal alternative data they accumulated along with a deeper understanding of the markets.”

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