Human behaviour intelligence driving next-gen credit decisions

Traditionally, lenders evaluate a borrower’s credit history based on credit utilization, the performance of prior loans, and the forms of credit they use. However, this process of assessing creditworthiness often does not work in favor of the unbanked population (India has the second-largest in the world, as per the RBI) that has a short or non-existent credit history. Also, we belong to a world hit by the pandemic where everyone suffered. So, what was in the past is hardly a reflection of what the future beholds. What truly matters is the behavior at present. This is where human behavior intelligence solutions are creating opportunities for financial institutions to improve credit decisions that underpin their lending processes.

The current state of credit decisions

Lack of data has always been an obstacle for underwriting processes by banks and financial institutions. This also refrains them from achieving financial inclusion. On one hand, new-to-credit customers and MSMEs are finding it difficult to get loans from banks due to a lack of financial and operating data for underwriting and risk management. On the other hand, banks also find the conventional approach inefficient and costly for processing small-ticket loans. 

The conventional approach to credit scoring often uses data derived from financial statements and credit bureaus to predict possible loan defaults. As businesses reinvent their operating models in the post-pandemic era, the traditional approach is not feasible for assessing new-to-credit customers or MSMEs that lack financial data to support a prediction.

This intensifies the need to make credit decisions faster with more reliable and deeper insights. 

The building blocks of next-gen credit decisioning

Behavioral intelligence solutions that harp on data, analytics, and collaborative business models between lending institutions and fintech firms help address the above challenges. These solutions extract patterns from surrogate data to understand customer behavior. They assess the creditworthiness of an individual based on app usage, geolocation, utility payments, small loans, travel history, online shopping, government transactions, property records, social media usage, and education or employment history, among others. 

Why human behavior intelligence?

Human behavior intelligence not only lowers the barriers to accessing loans but also creates an opportunity for banks to automate their credit underwriting processes. It allows lenders to access alternate sources of data for credit scoring and tap into opportunities across the lending pyramid. Additionally, human behavior intelligence allows financial institutions to tackle some of the key challenges posed by traditional credit scoring processes including:

–  Accelerated loan processing: As Artificial Intelligence (AI) and Machine Learning (ML) process and structure raw data into meaningful insights within minutes, financial institutions can perform credit scoring in a much shorter time. Simultaneously, they can also accelerate any re-assessment, if required. For example, corporate filing with the Ministry of Corporate Affairs (MCA) and Registrar of Companies (ROC) may unveil the structural data but this information is not digitized in a way that is readable by a machine. However, AI can help pull out this information which is highly unlikely otherwise.

–  Credibility: Human behavior analysis makes for a more reliable source of credit assessment as compared to financial data analysis and has been observed to reduce lending risk. For instance, a high CIBIL score may not be reflective of a borrower’s defaulting bill payment habits. Analysis of bill payment behaviors adds to the credibility factor.

–  Fraud detection: Alternative data is available through digital automation and ML can help to detect abnormal patterns of business operations with which lenders can detect fraud and implement risk mitigation measures. For example, a firm that has constant disputes with the law (based on the number of cases against it) can be held as a riskier proposal as compared to a company with relatively fewer feuds.

–  Continuous monitoring: With human behavior intelligence, the lender can monitor the borrower’s actual business situation, getting a more complete and comprehensive view of a consumer’s creditworthiness. For example, business health can be judged by observing its filing of returns with tax authorities and labour bodies. It must be noted that any delay in TDS filing indicates liquidity issues.

To make credit decisions, lenders must look past the credit history of customers. In this post-pandemic world, businesses deserve to be understood holistically. Thankfully, lenders already have access to important data as well as external data that augments the process. This is backed by evolving technology that further accelerates this process. It is time for lenders to bid adieu to the traditional methods and adopt innovation and change their approach to lending.



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